amrlotfy77 / 300Days__MachineLearningDeepLearning

I am sharing my Journey of 300DaysOfData in Machine Learning and Deep Learning.

Journey of 300DaysOfData in Machine Learning and Deep Learning

Books and Resources
1. Machine Learning From Scratch
2. A Comprehensive Guide to Machine Learning
3. Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
4. Speech and Language Processing
5. Machine Learning Crash Course
6. Deep Learning with PyTorch: Part I
7. Dive into Deep Learning

Research Papers
1. Practical Recommendations for Gradient based Training of Deep Architectures

Projects and Notebooks
1. California Housing Prices

Day1 of 300DaysOfData!

• Gradient Descent and Cross Validation: Gradient Descent is an iterative approach to approximating the Parameters that minimize a Differentiable Loss Function. Cross Validation is a resampling procedure used to evaluate Machine Learning Models on a limited Data sample which has a parameter that splits the data into number of groups. On my Journey of Machine Learning and Deep Learning, Today I have read in brief about the fundamental Topics such as Calculus, Matrices, Matrix Calculus, Random Variables, Density Functions, Distributions, Independence, Maximum Likelihood Estimation and Conditional Probability. I have also read and Implemented about Gradient Descent and Cross Validation. I am starting this Journey from Scratch and I am following the Book:Machine Learning From Scratch. I have presented the Implementation of Gradient Descent and Cross Validation here in the Snapshots. I hope you will also spend some time reading the Topics from the Book mentioned above. I am excited about the days to come!!
• Book:
  • Machine Learning From Scratch

Day2 of 300DaysOfData!

• Ordinary Linear Regression: Linear Regression is a linear approach to modelling the relationships between a scalar response or dependent variable and one or more explanatory variables or independent variables. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Ordinary Linear Regression, Parameter Estimation, Minimizing Loss and Maximizing Likelihood along with the Construction and Implementation of the LR from the Book Machine Learning From Scratch. I have also started reading the Book A Comprehensive Guide to Machine Learning which focuses on Mathematics and Theory behind the Topics. I have read about Regression, Ordinary Least Squares, Vector Calculus, Orthogonal Projection, Ridge Regression, Feature Engineering, Fitting Ellipses, Polynomial Features, Hyperparameters and Validation, Errors and Cross Validation from this book. I have presented the Implementation of Linear Regression along with Visualizations using Python here in the Snapshots. I hope you will also spend some time reading the Topics and Books mentioned above. Excited about the days ahead!!
• Books:
  • Machine Learning From Scratch
  • A Comprehensive Guide to Machine Learning

Day3 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Regularized Regression such as Ridge Regression and Lasso Regression, Bayesian Regression, GLMs, Poisson Regression along with Construction and Implementation of the same from the Book Machine Learning From Scratch. I have also read the Book A Comprehensive Guide to Machine Learning which focuses on Mathematics and Theory behind the Topics. I have read about Maximum Likelihood Estimation or MLE and Maximum a Posteriori or MAE for Regression, Probabilistic Model, Bias Variance Tradeoff, Metrics, Bias Variance Decomposition, Alternative Decomposition, Multivariate Gaussians, Estimating Gaussians from Data, Weighted Least Squares, Ridge Regression, and Generalized Least Squares from this Book. I have presented the Implementation of Ridge Regression, Lasso Regression along with Cross Validation, Bayesian Regression and Poisson Regression using Python here in the Snapshot. I hope you will also spend some time reading the Topics and Books mentioned above. Excited about the days ahead!!
• Books:
  • Machine Learning From Scratch
  • A Comprehensive Guide to Machine Learning

Day4 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Discriminative Classifiers such as Binary and Multiclass Logistic Regression, The Perceptron Algorithm, Parameter Estimation, Fishers Linear Discriminant and Fisher Criterion along with Construction and Implementation of the same from the Book Machine Learning From Scratch. I have also read the Book A Comprehensive Guide to Machine Learning which focuses on Mathematics and Theory behind the Topics. I have read about Kernels and Ridge Regression, Linear Algebra Derivation, Computational Analysis, Sparse Least Squares, Orthogonal Matching Pursuit, Total Least Squares, Low rank Formulation, Dimensionality Reduction, Principal Component Analysis, Projection, Changing Coordinates, Minimizing Reconstruction Errors and Probabilistic PCA from this Book. I have presented the Implementation of Binary and Multiclass Logistic Regression, The Perceptron Algorithm and Fishers Linear Discriminant using Python here in the Snapshot. I hope you will also spend some time reading the Topics and Books mentioned above. Excited about the days ahead!!
• Books:
  • Machine Learning From Scratch
  • A Comprehensive Guide to Machine Learning

Day5 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Generative Classifiers such as Linear Discriminative Analysis or LDA, Quadratic Discriminative Analysis or QDA, Naive Bayes, Parameter Estimation and Data Likelihood along with Construction and Implementation of the same from the Book Machine Learning From Scratch. I have also read the Book A Comprehensive Guide to Machine Learning which focuses on Mathematics and Theory behind the Topics. I have read about Generative and Discriminative Classification, Bayes Decision Rule, Least Squares Support Vector Machines, Feature Extension, Neural Network Extension, Binary and Multiclass Logistic Regression, Loss Function, Training, Multiclass Extension, Gaussian Discriminant Analysis, QDA and LDA Classification and Support Vector Machines from this Book. I have presented the Implementation of LDA, QDA and Naive Bayes along with Visualizations using Python here in the Snapshot. I hope you will also spend some time reading the Topics and Books mentioned above. Excited about the days ahead!!
• Books:
  • Machine Learning From Scratch
  • A Comprehensive Guide to Machine Learning

Day6 of 300DaysOfData!

• Decision Trees: A Decision Tree is an interpretable machine learning for Regression and Classification. It is a flow chart like structure in which each internal node represents a Test on an attribute and each branch represents the outcome of the Test. On my Journey of Machine Learning and Deep Learning, Today I have read about Decision Trees such as Regression Trees and Classification Trees, Building Trees, Making Splits and Predictions, Hyperparameters, Pruning and Regularization along with Construction and Implementation of the same from the Book Machine Learning From Scratch. I have also read the Book A Comprehensive Guide to Machine Learning which focuses on Mathematics and Theory behind the Topics. I have read about Decision Tree Learning, Entropy and Information, Gini Impurity, Stopping Criteria, Random Forests, Boosting and AdaBoost, Gradient Boosting and KMeans Clustering from this Book. I have presented the Implementation of Regression Trees and Classification Trees using Python here in the Snapshot. I hope you will also spend some time reading the Topics and Books mentioned above. Excited about the days ahead!!
• Books:
  • Machine Learning From Scratch
  • A Comprehensive Guide to Machine Learning

Day7 of 300DaysOfData!

• Tree Ensemble Methods: Ensemble Methods combine the outputs of multiple simple Models which is often called Learners in order to create the fine Model with low variance. Due to their high variance, a decision trees often fail to reach a level of precision comparable to other predictive algorithms and Ensemble Methods minimize the variance. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Tree Ensemble Methods such as Bagging for Decision Trees, Bootstrapping, Random Forests and Procedure, Boosting, AdaBoost for Binary Classification, Weighted Classification Trees, The Discrete AdaBoost Algorithm and AdaBoost for Regression along with Construction and Implementation of the same from the Book Machine Learning From Scratch. I have presented the Implementation of Bagging, Random Forests and AdaBoost along with different base estimators using Python here in the Snapshot. I hope you will also spend some time reading the Topics and Book mentioned above. Excited about the days ahead !!
• Books:
  • Machine Learning From Scratch

Day8 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Neural Networks from the Book Machine Learning From Scratch. I have read about Model Structure, Communication between Layers, Activation Functions such as ReLU, Sigmoid, The Linear Activation Function, Optimization, Back Propagation, Calculating Gradients, Chain Rule and Observations, Loss Functions along with Construction using The Loop Approach and The Matrix Approach and Implementation of the same from this Book. I have also read the Book A Comprehensive Guide to Machine Learning which focuses on Mathematics and Theory behind the Topics. I have read about Convolutional Neural Networks and Layers, Pooling Layers, Back Propagation for CNN, ResNet and Visual Understanding of CNNs from this Book. Besides, I have seen a couple of videos of Neural Networks and Deep Learning. I have presented the simple Implementation of Neural Networks with The Functional API and The Sequential API using TensorFlow here in the Snapshot. I hope you will also spend some time reading the Topics and Books mentioned above. Excited about the days ahead !!
• Books:
  • Machine Learning From Scratch
  • A Comprehensive Guide to Machine Learning

Day9 of 300DaysOfData!

• Reinforcement Learning: In Reinforcement Learning, The Learning system called an agent in a particular context can observe the environment, select and perform actions and get rewards in return or penalties in the form of negative rewards. It must learn by itself what is the best policy to get the most reward over time. On my Journey of Machine Learning and Deep Learning, Today I have started reading and Implementing from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have read briefly about The Machine Learning Landscape viz. Types of Machine Learning Systems such as Supervised and Unsupervised Learning, Semisupervised Learning, Reinforcement Learning, Batch Learning and Online Learning, Instance Based Learning and Model Based Learning from this Book. I have presented the simple Implementation of Linear Regression and KNearest Neighbors along with a simple plot using Python here in the Snapshot. I hope you will also spend some time reading the Topics and Book mentioned above. Excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day10 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have read about the Main Challenges of Machine Learning such as Insufficient Quantity of Training Data, Non representative Training Data, Poor Quality Data, Irrelevant Features, Overfitting and Underfitting the Training Data and Testing and Validating, Hyperparameter Tuning and Model Selection and Data Mismatch from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have started working on California Housing Prices Dataset which is included in this Book. I will build a Model of Housing Prices in California in this Project. I have presented the simple Implementation of Data Processing and few techniques of EDA using Python here in the Snapshot. I have also presented the Implementation of Sweetviz Library for Analysis here. I really appreciate Chanin Nantasenamat for sharing about this Library in one of his videos. I hope you will also spend some time reading the Topics and Book mentioned above. Excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
• Chanin Nantasenamat Video on Sweetviz
• California Housing Prices

Day11 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have learned and Implemented about Creating categories from attributes, Stratified Sampling, Visualizing Data to gain insights, Scatter Plots, Correlations, Scatter Matrix and Attribute Combinations from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have continued working with California Housing Prices Dataset which is included in this Book. This Dataset was based on Data from the 1990 California Census. I will build a Model of Housing Prices in California in this Project. I am still working on the same. I have presented the Implementation of Stratified Sampling, Correlations using Scatter Matrix and Attribute combinations using Python here in the Snapshots. I have also presented the Snapshots of Correlations using Scatter plots here. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead !!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
• California Housing Prices

Day12 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have learned and Implemented about Preparing the Data for Machine Learning Algorithms, Data Cleaning, Simple Imputer, Ordinal Encoder, OneHot Encoder, Feature Scaling, Transformation Pipeline, Standard Scaler, Column Transformer, Linear Regression, Decision Tree Regressor and Cross Validation from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have continued working with California Housing Prices Dataset which is included in this Book. This Dataset was based on Data from the 1990 California Census. I will build a Model of Housing Prices in California in this Project. The Notebook contains almost every Topics mentioned above. I have presented the Implementation of Data Preparation, Handling missing values, OneHot Encoder, Column Transformer, Linear Regression, Decision Tree Regressor along with Cross Validation using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead !!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
• California Housing Prices

Day13 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have learned and Implemented about Random Forest Regressor, Ensemble Learning, Tuning the Model, Grid Search, Randomized Search, Analyzing the Best Models and Errors, Model Evaluation, Cross Validation and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have completed working with California Housing Prices Dataset which is included in this Book. This Dataset was based on Data from the 1990 California Census. I have built a Model using Random Forest Regressor of California Housing Prices Dataset to predict the price of the Houses in California. I have presented the Implementation of Random Forest Regressor and Tuning the Model with Grid Search and Randomized Search along with Cross Validation using Python here in the Snapshot. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
• California Housing Prices

Day14 of 300DaysOfData!

• Confusion Matrix: Confusion Matrix is a better way to evaluate the performance of a Classifier. The general idea of Confusion Matrix is to count the number of times instances of Class A are classified as Class B. This approach requires to have a set of predictions so that they can be compared to the actual targets. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Classification, Training a Binary Classifier using Stochastic Gradient Descent, Measuring Accuracy using Cross Validation, Implementation of CV, Confusion Matrix, Precision and Recall and their Curves and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of SGD Classifier in MNIST Dataset along with Precision and Recall using Python here in the Snapshots. I have also presented the curves of Precision and Recall here. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. I am excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day15 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about The ROC Curve, Random Forest Classifier, SGD Classifier, Multi Class Classification, One vs One and One vs All Strategies, Cross Validation, Error Analysis using Confusion Matrix, Multi Class Classification, KNeighbors Classifier, Multi Output Classification, Noises, Precision and Recall Tradeoff and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have completed the Topic Classification from this Book. I have presented the Implementation of The ROC Curve, Random Forest Classifier in Multi Class Classification, The One vs One Strategy, Standard Scaler, Error Analysis, Multi Label Classification and Multi Output Classification using Scikit Learn here in the Snapshots. I hope you will also work on the same. I hope you will also spend some time reading the Topics and Book mentioned above. I am excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day16 of 300DaysOfData!

• Ridge Regression: Ridge Regression is a regularized Linear Regression viz. a regularization term is added to the cost function which forces the learning algorithm to not only fit the Data but also keep the model weights as small as possible. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Training the Models, Linear Regression, The Normal Equations and Computational Complexity, Cost Function and Gradient Descent such as Batch Gradient Descent, Convergence Rate, Stochastic Gradient Descent, Mini batch Gradient Descent, Polynomial Regression and Poly Features, Learning Curves, Bias and Variance Tradeoff, Regularized Linear Models such as Ridge Regression and few more related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Polynomial Regression, Learning Curves and Ridge Regression along with Visualization using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day17 of 300DaysOfData!

• Elastic Net: Elastic Net is a middle grouped between Ridge Regression and Lasso Regression. The regularization term r is a simple mix of both Ridge and Lasso's regularization terms. When r equals 0, it is equivalent to Ridge and when r equals 1, it is equivalent to Lasso Regression. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Lasso Regression, Elastic Net, Early Stopping, SGD Regressor, Logistic Regression, Estimating Probabilities, Training and Cost Function, Sigmoid Function, Decision Boundaries, Softmax Regression or Multinomial Logistic Regression, Cross Entropy and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have just started reading the Topic Support Vector Machines. I have presented the simple Implementation of Lasso Regression, Elastic Net, Early Stopping, Logistic Regression and Softmax Regression using Scikit Learn here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day18 of 300DaysOfData!

• Support Vector Machines: A Support Vector Machines or SVM is a very powerful and versatile Machine Learning model which is capable of performing Linear and Nonlinear Classification, Regression and even outlier detection. SVMs are particularly well suited for classification of complex but medium sized datasets. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Support Vector Machines, Linear SVM Classification, Soft Margin Classification, Nonlinear SVM Classification, Polynomial Regression, Polynomial Kernel, Adding Similarity Features, Gaussian RBF Kernel, Computational Complexity, SVM Regression which is Linear as well Nonlinear and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Nonlinear SVM Classification using SVC and Linear SVC along with Visualization using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day19 of 300DaysOfData!

• Voting Classifiers: Voting Classifiers are the classifiers which aggregates the predictions of different Classifiers and predicts the class that gets the most votes. The majority vote classifier is called a Hard Voting Classifier. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Ensemble Learning and Random Forests, Voting Classifiers such as Hard Voting and Soft Voting Classifiers and few more topics related to the same. Actually, I have also started working on a Research Project with an amazing Team. I have presented the Implementation of Hard Voting and Soft Voting Classifiers using Scikit Learn here in the Snapshots. I hope you will spend some time working on the same and reading the Topics mentioned above. Excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day20 of 300DaysOfData!

• The CART Training Algorithm: The Algorithm which represents Scikit Learn's Implementation of the Classification and Regression Tree or CART Training algorithm to train Decision Trees also called Growing Trees. It's working principle is splitting the Training set into two subsets using a feature and a threshold. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Decision Functions and Predictions, Decision Trees, Decision Tree Classifier, Making Predictions, Gini Impurity, White Box Models and Black Box Models, Estimating Class Probabilities, The CART Training Algorithm, Computational Complexities, Entropy, Regularization Hyperparameters, Decision Tree Regressor, Cost Function and Instability from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the simple Implementation of Decision Tree Classifier and Decision Tree Regressor along with Visualization of the same using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day21 of 300DaysOfData!

• Bagging and Pasting: It refers to the approach which uses the same Training Algorithm for every predictor but to train them on different random subsets of the Training set. When sampling is performed with replacement, it is called Bagging and when sampling is performed without replacement, it is called Pasting. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Ensemble Learning and Random Forests, Voting Classifiers, Bagging and Pasting in Scikit Learn, Out of Bag Evaluation, Random Patches and Random Subspaces, Random Forests, Extremely Randomized Trees Ensemble, Feature Importance, Boosting, AdaBoost, Gradient Boosting and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Bagging Ensembles, Decision Trees, Random Forest Classifier, Feature Importance, AdaBoost Classifier and Gradient Boosting using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day22 of 300DaysOfData!

• Manifold Learning: Manifold Learning refers to the Dimensionality Reduction Algorithms that work by modeling the manifold on which the training instances lie which relies on manifold hypothesis which holds that most real world high dimensional datasets to a much lower dimensional manifold. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Gradient Boosting, Early Stopping, Stochastic Gradient Boosting, Extreme Gradient Boosting or XGBoost, Stacking and Blending, Dimensionality Reduction, Curse of Dimensionality, Approaches for Dimensionality Reduction, Projection and Manifold Learning and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Gradient Boosting with Early Stopping along with Visualization using Scikit Learn here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day23 of 300DaysOfData!

• Incremental PCA: Incremental PCA or IPCA Algorithms are the algorithms in which we can split the Training set into mini batches and feed an IPCA Algorithm one mini batch at a time. It is useful for large Training sets and also to apply PCA online. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Principal Component Analysis or PCA, Preserving the Variance, Principal Components, Projecting Down the Dimensions, Explained Variance Ratio, Choosing the Right Number of Dimensions, PCA for Compression and Decompression, Reconstruction Error, Randomized PCA, SVD, Incremental PCA and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of PCA, Randomized PCA and Incremental PCA along with Visualizations using Scikit Learn here in the Snapshots. I hope you will spend some time working on the same. I hope you will also spend some time reading the Topics and Book mentioned above. Excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day24 of 300DaysOfData!

• Clustering: Clustering Algorithms are the algorithms whose goal is to group similar instances together into Clusters. It is a great tool for Data Analysis, Customer Segmentation, Recommender Systems, Search Engines, Image Segmentation, Dimensionality Reduction and many more. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Kernel Principal Component Analysis, Selecting a Kernel and Tuning Hyperparameters, Pipeline and Grid Search, Locally Linear Embedding, Dimensionality Reduction Techniques such as Multi Dimensional Scaling, Isomap and Linear Discriminant Analysis, Unsupervised Learning such as Clustering and KMeans Clustering Algorithm and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Kernel PCA and Grid Search CV, and KMeans Clustering Algorithm along with a Visualization using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day25 of 300DaysOfData!

• Image Segmentation: Image Segmentation is the task of partitioning an Image into multiple segments. In Semantic Segmentation, all the pixels that are part of the same object type get assigned to the same segment. In Instance Segmentation, all pixels that are part of the individual object are assigned to the same segment. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about KMeans Algorithms, Centroid Initialization, Accelerated KMeans and Mini Batch KMeans, Finding the Optimal Numbers of Clusters, Elbow rule and Silhouette Coefficient score, Limitations of KMeans, Using Clustering for Image Segmentation and Preprocessing such as Dimensionality Reduction and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Clustering Algorithms for Image Segmentation and Preprocessing along with Visualizations using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day26 of 300DaysOfData!

• Gaussian Mixtures Model: A Gaussian Mixture Model or GMM is a probabilistic Model that assumes that the instances were generated from the mixture of several Gaussian distributions whose parameters are unknown. All the instances generated from a single Gaussian Distributions form a cluster that typically looks like an Ellipsoid. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about using Clustering Algorithms for Semi Supervised Learning, Active Learning and Uncertainty Sampling, DBSCAN, Agglomerative Clustering, Birch Algorithms, Mean Shift and Affinity Propagation Algorithms, Spectral Clustering, Gaussian Mixtures Model, Expectation Maximization Algorithm and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Clustering Algorithms for Semi supervised Learning and DBSCAN along with Visualizations using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day27 of 300DaysOfData!

• Anomaly Detection: Anomaly Detection also called Outlier Detection is the task of detecting instances that deviate strongly from the norm. These instances are called anomalies or outliers while the normal instances are called inliers. It is useful in Fraud Detection and more. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Gaussian Mixture Models, Anomaly Detection using Gaussian Mixtures, Novelty Detection, Selecting the Number of Clusters, Bayesian Information Criterion, Akaike Information Criterion, Likelihood Function, Bayesian Gaussian Mixture Models, Fast MCD, Isolation Forest, Local Outlier Factor, One Class SVM and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have just started Neural Networks and Deep Learning from this Book. I have presented the Implementation of Gaussian Mixture Model along with Visualizations using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day28 of 300DaysOfData!

• Rectified Linear Unit Function or ReLU : It is a continuous but not differentiable at 0 where the slope changes abruptly and makes the Gradient Descent bounce around. It works very well and has the advantage of fast to compute. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Introduction to Artificial Neural Networks with Keras, Biological Neurons, Logical Computations with Neurons, The Perceptron, Hebbian Learning, Multi Layer Perceptron and Backpropagation, Gradient Descent, Hyperbolic Tangent Function and Rectified Linear Unit Function, Regression MLPs, Classification MLPs, Softmax Activation and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Building an Image Classifier using the Sequential API along with Visualization using Keras here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. I am excited about the days ahead !!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day29 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Creating the Model using Sequential API, Compiling the Model, Loss Function and Activation Function, Training and Evaluating the Model, Learning Curves, Using the Model to make Predictions, Building the Regression MLP using the Sequential API, Building Complex Models using the Functional API, Deep Neural Networks and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Building Regression MLP using Sequential API and Functional API here in the Snapshots. I hope you will gain some insights and you will spend some time working on the same. I hope you will also spend some time reading and Implementing the Topics from the Book mentioned above. I am excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day30 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Building the Complex Models using Functional API, Deep Neural Network Architecture, ReLU Activation Function, Handling Multiple Inputs in the Model, Mean Squared Error Loss Function and Stochastic Gradient Descent Optimizer, Handling Multiple Outputs or Auxiliary Output for Regularization and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Handling Multiple Inputs using Keras Functional API along with the Implementation of Handling Multiple Outputs or Auxiliary Output for Regularization using the same here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. I am excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day31 of 300DaysOfData!

• Callbacks and Early Stopping: Early Stopping is a method that allows you to specify an arbitrarily large number of Training epochs and stopping once the Model stops improving on the validation dataset. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Building Dynamic Models using the Sub Classing API, Sequential API and Functional API, Saving and Restoring the Model, Using Callbacks, Model Checkpoints, Early Stopping, Weights and Biases and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Building Dynamic Models using the Sub Classing API along with the Implementation of Using Callbacks and Early Stopping here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead!!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day32 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Visualization using TensorBoard, Learning Curves, Fine Tuning Neural Network Hyperparameters, Randomized Search CV, Regressor, Libraries to optimize Hyperparameters such as Hyperopt, Talos and few more, Number of Hidden Layers, Number of Neurons per Hidden Layer, Learning Rate, Batch size and Other Hyperparameters and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have also spend some time reading the Paper which is named as Practical Recommendations for Gradient based Training of Deep Architectures. Here, I have read about Deep Learning and Greedy Layer Wise Pretraining, Online Learning and Optimization of Generalization Error and few more related to the same. I have presented the Implementation of Tuning Hyperparameters, Keras Regressors and Randomized Search CV here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
• Paper:
  • Practical Recommendations for Gradient based Training of Deep Architectures

Day33 of 300DaysOfData!

• Vanishing Gradient: During Backpropagation and calculating Gradients, it often gets smaller and smaller as the Algorithms progresses down to the lower layers which prevents the Training to converge to the good solution. This leads to Vanishing Gradient Problem. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Training Deep Neural Networks, Vanishing and Exploding Gradient Problems, Glorot and He Initialization, Non Saturating Activation Functions, Batch Normalization and its Implementation, Logistic and Sigmoid Activation Function, SELU Activation Function, ReLU Activation Function and Variants, Leaky ReLU and Parametric Leaky ReLU and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Leaky ReLU and Batch Normalization here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day34 of 300DaysOfData!

• Gradient Clipping: Gradient Clipping is the Technique to lessen the exploding Gradients problem which simply clip the Gradients during backpropagation so that they never exceed some threshold and it is mostly used in Recurrent Neural Networks. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Gradient Clipping, Batch Normalization, Reusing Pretrained Layers, Deep Neural Networks and Transfer Learning, Unsupervised Pretraining, Restricted Boltzmann Machines, Pretraining on an Auxiliary Task, Self Supervised Learning, Faster Optimizers, Gradient Descent Optimizer, Momentum Optimization, Nesterov Accelerated Gradient and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the simple Implementation of Transfer Learning using Keras and Sequential API here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day35 of 300DaysOfData!

• Adam Optimization: Adam which stands for Adaptive Moment Estimation combines the ideas of Momentum Optimization and RMSProp where Momentum Optimization keeps track of an exponentially decaying average of past gradients and RMSProp keeps track of an exponentially decaying average of past squared gradients. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about AdaGrad Algorithm, Gradient Descent, RMSProp Algorithm, Adaptive Moment Estimation or Adam Optimization, Adamax, Nadam Optimization, Training Sparse Models, Dual Averaging, Learning Rate Scheduling, Power Scheduling, Exponential Scheduling, Piecewise Constant Scheduling, Performance Scheduling and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Exponential Scheduling and Piecewise Constant Scheduling here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day36 of 300DaysOfData!

• Deep Neural Networks: The best Deep Neural Networks configurations which will work fine in most cases without requiring much Hyperparameter Tuning is here: Kernel Initializer as LeCun Initialization, Activation Function as SELU, Normalization as None, Regularization as Early Stopping, Optimizer as Nadam, Learning Rate Schedule as Performance Scheduling. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Avoiding Overfitting Through Regularization, L1 and L2 Regularization, Dropout Regularization, Self Normalization, Batch Normalization, Monte Carlo Dropout, Max Norm Regularization, Activation Functions like SELU and Leaky ReLU, Nadam Optimization and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of L2 Regularization and Dropout Regularization using Keras here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day37 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Custom Models and Training with TensorFlow, High Level Deep Learning APIs, IO and Preprocessing, Lower Level Deep Learning APIs, Deployment and Optimization, TensorFlow Architecture, Tensors and Operations, Keras Low Level API, Tensors and Numpy, Sparse Tensors, Arrays, String Tensors, Custom Loss Functions, Saving and Loading the Models containing Custom Components and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have also started reading a Book Speech and Language Processing. Here, I have read about Regular Expressions, Text Normalization, Tokenization, Lemmatization, Stemming, Sentence Segmentation, Edit Distance and few more Topics related to the same. I have presented the simple Implementation of Custom Loss Function here in the Snapshot. I hope you will also spend some time reading the Topics from the Books mentioned above and below. Excited about the days ahead !!
• Books:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
  • Speech and Language Processing

Day38 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Custom Activation Functions, Initializers, Regularizers and Constraints, Custom Metrics, MAE and MSE, Streaming Metric, Custom Layers, Custom Models, Losses and Metrics based on Models Internals and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have also started reading a Book Speech and Language Processing. Here, I have read about Regular Expressions, Basic Regular Expression Patterns, Disjunction, Range, Kleene Star, Wildcard Expression, Grouping and Precedence, Operator Hierarchy, Greedy and Non Greedy matching, Sequence and Anchors, Counters and few more Topics related to the same. I have presented the Implementation of Custom Activation Functions, Initializers, Regularizers, Constraints and Custom Metrics here in the Snapshots. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Books:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow
  • Speech and Language Processing

Day39 of 300DaysOfData!

• Prefetching and Data API: Prefetching is the loading of the resource before it is required to decrease the time waiting for that resource. In other words, while the Training Algorithm is working on one batch the dataset will already be working in parallel on getting the next batch ready which will improve the performance dramatically. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Loading and Preprocessing Data using TensorFlow, The Data API, Chaining Transformations, Shuffling the Dataset, Gradient Descent, Interleaving Lines From Multiple Files, Parallelism, Preprocessing the Dataset, Decoding, Prefetching, Multithreading and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the simple Implementation of Data API using TensorFlow here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Books:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day40 of 300DaysOfData!

• Embedding and Representation Learning: An Embedding is a trainable dense vector that represents a category. The better the representation of the categories, the easier it will be for the Neural Network to make accurate predictions, so Embeddings must make the useful representations of the categories. This is called Representation Learning. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about The Features API, Column Transformer, Numerical and Categorical Features, Crossed Categorical Features, Encoding Categorical Features using One Hot Vectors and Embeddings, Representation Learning, Word Embeddings, Using Feature Columns for Parsing, Using Feature Columns in the Models and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the simple Implementation of The Features API in Numerical and Categorical Columns along with Parsing here in the Snapshots. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Books:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day41 of 300DaysOfData!

• Convolutional Layer:The most important building block of CNN is the Convolutional Layer. Neurons in the first Convolutional Layer are not connected to every single pixel in the Input Image but only to pixels in their respective fields. Similarly, each Neurons in second CL is connected only to neurons located within a small rectangle in the first layer. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Deep Computer Vision using Convolutional Neural Networks, The Architecture of the Visual Cortex, Convolutional Layer, Zero Padding, Filters, Stacking Multiple Feature Maps, Padding, Memory Requirements, Pooling Layer, Invariance, Convolutional Neural Network Architectures and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the simple Implementation of Convolutional Neural Network Architecture here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Books:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day42 of 300DaysOfData!

• ResNet Model: Residual Network or ResNet won the ILSVRC 2015 Challenge, developed by Kaiming He, using an extremely deep CNN composed of 152 Layers. This Network uses the Skip connections which is also called Shortcut connections: The signal feeding into a layer is also added to the output of a layer located a bit higher up the stack. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about LeNet-5 Architecture, AlexNet CNN Architecture, Data Augmentation, Local Response Normalization, GoogLeNet Architecture, Inception Module, VGGNet, Residual Network or ResNet, Residual Learning, Xception or Extreme Inception, Squeeze and Excitation Network or SENet and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of ResNet 34 CNN using Keras here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Books:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day43 of 300DaysOfData!

• Xception Model: Xception which stands for Extreme Inception is a variant of GoogLeNet Architecture which was proposed in 2016 by François Chollet. It merges the ideas of GoogLeNet and ResNet Architecture but it replaces the Inception modules with a special type of layer called a Depthwise Separable Convolution. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Using Pretrained Models from Keras, GoogLeNet and Residual Network or ResNet, ImageNet, Pretrained Models for Transfer Learning, Xception Model, Convolutional Neural Network, Batching, Prefetching, Global Average Pooling and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have presented the Implementation of Pretrained Models such as ResNet and Xception for Transfer Learning here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Books:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day44 of 300DaysOfData!

• Semantic Segmentation: In Semantic Segmentation, each pixel is classified according to the class of the object it belongs to but the different objects of the same class are not distinguished. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Classification and Localization, Crowdsourcing in Computer Vision, Intersection Over Union metric, Object Detection, Fully Convolutional Networks or FCNs, VALID Padding, You Only Look Once or YOLO Architecture, Mean Average Precision or MAP, Convolutional Neural Networks, Semantic Segmentation and few more Topics related to the same from the Book Hands On Machine Learning with Scikit Learn, Keras and TensorFlow. I have just completed learning from this Book. I have presented the Implementation of Classification and Localization along with the Visualization here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Books:
  • Hands On Machine Learning with Scikit Learn, Keras and TensorFlow

Day45 of 300DaysOfData!

• Empirical Risk Minimization: Training a Model means learning good values for all the weights and the biases from Labeled examples. In Supervised Learning, a Machine Learning Algorithm builds a Model by examining many examples and attempting to find a Model that minimizes loss which is called Empirical Risk Minimization. On my Journey of Machine Learning and Deep Learning, Today I have started learning from the Machine Learning Crash Course of Google. Here, I have learned about Machine Learning Philosophy, Fundamentals of Machine Learning and Uses, Labels and Features, Labeled and Unlabeled Example, Models and Inference, Regression and Classification, Linear Regression, Weights and Bias, Training and Loss, Empirical Risk Minimization, Mean Squared Error or MSE, Reducing Loss, Gradient Descent and few more Topics related to the same. I have presented the simple Implementation of Basic Recurrent Neural Network here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Course mentioned above and below. Excited about the days ahead !!
• Course:
  • Machine Learning Crash Course

Day46 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have learned from Machine Learning Crash Course of Google. Here, I have learned and Implemented about Learning Rate or Step size, Hyperparameters in Machine Learning Algorithms, Regression, Gradient Descent, Optimizing Learning Rate, Stochastic Gradient Descent or SGD, Batch and Batch Size, Minibatch Stochastic Gradient Descent, Convergence, Hierarchy of TensorFlow Toolkits and few more Topics related to the same. I have also spend some time in reading the Book Speech and Language Processing. Here, I have read about Regular Expressions and Patterns, Precision and Recall, Kleene Star, Aliases for Common Characters, RE Operators for Counting and few more Topics related to the same. I have presented the simple Implementation of Recurrent Neural Network and Deep RNN using Keras here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Course and Book mentioned above and below. Excited about the days ahead !!
• Course:
  • Machine Learning Crash Course
• Book:
  • Speech and Language Processing

Day47 of 300DaysOfData!

• Feature Vector and Feature Engineering: Feature Engineering means transforming Raw Data into Feature Vector, which is the set of Floating values comprising the examples of the Dataset. On my Journey of Machine Learning and Deep Learning, Today I have learned from Machine Learning Crash Course of Google. Here, I have learned and Implemented about Generalization of Model, Overfitting, Gradient Descent and Loss, Statistical and Computational Learning Theories, Stationarity of Data, Splitting of Data and Validation Set, Representation and Feature Engineering, Feature Vector, Categorical Features and Vocabulary, One Hot Encoding and Sparse Representation, Qualities of Good Features and few more Topics related to the same. I have presented the simple Implementation of RNN along with GRU here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Course and Book mentioned above and below. Excited about the days ahead !!
• Course:
  • Machine Learning Crash Course

Day48 of 300DaysOfData!

• Scaling Features: Scaling means converting floating point Feature Values from their Natural range into Standard range such as 0 to 1. If the Feature set contains multiple Features, then Feature Scaling helps Gradient Descent to converge more quickly. On my Journey of Machine Learning and Deep Learning, Today I have learned from Machine Learning Crash Course of Google. Here, I have learned and Implemented about Scaling Feature Values, Handling Extreme Outliers, Binning, Scrubbing the Data, Standard Deviation, Feature Cross and Synthetic Feature, Encoding Nonlinearity, Stochastic Gradient Descent, Cross Product, Crossing One Hot Vectors, Regularization For Simplicity, Generalization Curve, L2 Regularization, Early Stopping, Lambda and Learning Rate and few more Topics related to the same. I have presented the simple Implementation of Linear Regression Model using Sequential API here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Course mentioned above and below. Excited about the days ahead !!
• Course:
  • Machine Learning Crash Course

Day49 of 300DaysOfData!

• Prediction Bias: Prediction Bias is a quantity that measures how far apart is the average of predictions from the average of labels in Dataset. Prediction Bias is completely a different quantity than Bias. On my Journey of Machine Learning and Deep Learning, Today I have learned from Machine Learning Crash Course of Google. Here, I have learned and Implemented about Logistic Regression and Calculating Probability, Sigmoid Function, Binary Classification, Log Loss and Regularization, Early Stopping, L1 and L2 Regularization, Classification and Thresholding, Confusion Matrix, Class Imbalance and Accuracy, Precision and Recall, ROC Curve, Area Under Curve or AUC, Prediction Bias, Calibration Layer, Bucketing, Sparsity, Feature Cross and One Hot Encoding and few more Topics related to the same. I have presented the simple Implementation of Normalization and Binary Classification using Keras here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Course mentioned above and below. Excited about the days ahead !!
• Course:
  • Machine Learning Crash Course

Day50 of 300DaysOfData!

• Categorical Data and Sparse Tensors: Categorical Data refers to Input Features that represent one or more Discrete items from a finite set of choices. Sparse Tensors are the tensors with very few non zero elements. On my Journey of Machine Learning and Deep Learning, Today I have learned from Machine Learning Crash Course of Google. Here, I have learned and Implemented about Neural Networks, Hidden Layers and Activation Functions, Nonlinear Classification and Feature Crosses, Sigmoid Function, Rectified Linear Unit or ReLU, Backpropagation, Vanishing and Exploding Gradients, Dropout Regularization, Multi Class Neural Networks, Softmax, Logistic Regression, Embeddings, Collaborative Filtering, Sparse Features, Principal Component Analysis, Word2Vec and few more Topics related to the same. I have presented the simple Implementation of Deep Neural Networks in Multi Class Classification here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Course mentioned above and below. Excited about the days ahead !!
• Course:
  • Machine Learning Crash Course

Day51 of 300DaysOfData!

• Deep Learning: Deep Learning is the general class of Algorithms which falls under Artificial Intelligence and deals with training Mathematical entities named Deep Neural Networks by presenting the instructive examples. It uses large amounts of Data to approximate Complex Functions. On my Journey of Machine Learning and Deep Learning, Today I have started reading and Implementing from the Book Deep Learning with PyTorch. Here, I have learned about Core PyTorch, Deep Learning Introduction and Revolution, Tensors and Arrays, Deep Learning Competitive Landscape, Utility Libraries, Pretrained Neural Network that recognizes the subject of an Image, ImageNet, Image Recognition, AlexNet and ResNet, Torch Vision Module and few more Topics related to the same from here. I have presented the Implementation of Obtaining Pretrained Neural Networks for Image Recognition using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day52 of 300DaysOfData!

• The GAN Game: GAN stands for Generative Adversarial Network where Generative means something being created, Adversarial means the two Neural Networks are competing to out smart the other and well Network means Neural Networks. A Cycle GAN can turn Images of one Domain into Images of another Domain without the need for us to explicitly provide matching pairs in the Training set. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Pretrained Models, Generative Adversarial Network or GAN, ResNet Generator and Discriminator Models, Cycle GAN Architecture, Torch Vision Module, Deep Fakes, A Neural Network that turns Horses into Zebras and few more Topics related to the same from here. I have presented the Implementation of Cycle GAN that turns Horses into Zebras using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day53 of 300DaysOfData!

• Tensors and Multi Dimensional Arrays: Tensors are the Fundamental Data Structure in PyTorch. A Tensor is an array that is a Data Structure which stores a collection of numbers that are accessible individually using a index and that can be indexed with multiple indices. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about A Pretrained Neural Network that describes the scenes, NeuralTalk2 Model, Recurrent Neural Network, Torch Hub, Fundamental Building Block: Tensors, The world as Floating Point Numbers, Multidimensional Arrays and Tensors, Lists and Indexing Tensors, Named Tensors, Einsum, Broadcasting and few more Topics related to the same from here. I have presented the simple Implementation of Indexing Tensors and Named Tensors using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day54 of 300DaysOfData!

• Tensors and Multi Dimensional Arrays: Tensors are the Fundamental Data Structure in PyTorch. A Tensor is an array that is a Data Structure which stores a collection of numbers that are accessible individually using a index and that can be indexed with multiple indices. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Named Tensors, Changing the names of Named Tensors, Broadcasting Tensors, Unnamed Dimensions, Tensor Element Types, Specifying the Numeric Data Type, The Tensor API, Creation Operations, Indexing, Random Sampling, Serialization, Parallelism, Tensors Storage, Referencing Storage, Indexing into Storage and few more Topics related to the same from here. I have presented the simple Implementation of Named Tensors, Tensor Datatype Attributes and Tensor API using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day55 of 300DaysOfData!

• Encoding Color Channels: The most common way to encode Colors into numbers is RGB where a color is defined by three numbers representing the Intensity of Red, Green and Blue. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Tensors Metadata such as Size, Offset and Stride, Transposing Tensors without Copying, Transposing in Higher Dimensions, Contiguous Tensors, Managing Tensors Device Attribute such as moving to GPU and CPU, Numpy Interoperability, Generalized Tensors, Serializing Tensors, Data Representation using Tensors, Working with Images, Adding Color Channels, Changing the Layout and few more Topics related to the same from here. I have presented the Implementation of Working with Images such as Changing the Layout and Permute method along with Contiguous Tensors using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day56 of 300DaysOfData!

• Continuous, Ordinal and Categorical Values: Continuous Values are the values which can be counted and measured along with units. Ordinal Values are the continuous values with no fixed relationships between values. Categorical Values are the enumerations of possibilities. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Normalizing the Image Data, Working with 3D Images or Volumetric Image Data, Representing the Tabular Data, Loading the Data Tensors using Numpy, Continuous Values, Ordinal Values, Categorical Values, Ratio Scale and Interval Scale, Nominal Scale, One Hot Encoding and Embeddings, Singleton Dimensions and few more Topics related to the same from here. I have presented the Implementation of Normalizing the Image Data, Volumetric Data, Tabular Data and One Hot Encoding using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day57 of 300DaysOfData!

• Continuous, Ordinal and Categorical Values: Continuous Values are the values which can be counted and measured along with units. Ordinal Values are the continuous values with no fixed relationships between values. Categorical Values are the enumerations of possibilities. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Continuous and Categorical Data, PyTorch Tensor API, Finding Thresholds in Tabular Data, Advanced Indexing, Working with Time Series Data, Adding Time Dimension in Data, Shaping the Data by Time Period, Tensors and Arrays and few more Topics related to the same from here. I have presented the Implementation of Working with Categorical Data, Time Series Data and Finding Thresholds using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day58 of 300DaysOfData!

• Encoding and ASCII: Every written characters is represented by a code which refers to a sequence of bits of appropriate length so that each character can be uniquely identified and it is called Encoding. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Working with Time Series Data, Ordinal Variables, One Hot Encoding and Concatenation, Unsqueeze and Singleton Dimension, Mean, Standard Deviation and Rescaling Variables, Text Representation, Natural Language Processing and Recurrent Neural Networks, Converting the Text into Numbers, Project Gutenberg Corpus, One Hot Encoding of Characters, Encoding and ASCII, Embeddings and Processing the Text and few more Topics related to the same from here. I have presented the Implementation of Time Series Data and Text Representation using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day59 of 300DaysOfData!

• Loss Function: Loss Function is a function that computes a single numerical value that the learning process will attempt to minimize. The calculation of loss typically involves taking the difference between the desired outputs for some training samples. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about One Hot Encoding and Vectors, Data Representation using Tensors, Text Embeddings, Natural Language Processing, The Mechanics of Learning, Johannes Kepler's Lesson in Modeling, Eccentricity, Parameter Estimation, Weight, Bias and Gradients, Simple Linear Model, Loss Function or Cost Function, Mean Square Loss, Broadcasting and few more Topics related to the same from here. I have presented the simple Implementation of Representing Text, Mechanics of Learning and Simple Linear Model using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day60 of 300DaysOfData!

• Gradient Descent: Gradient Descent is the first order iterative Optimization Algorithm for finding a local minimum of a Differentiable Function. Simply, Gradient is the derivates of the Function with respect to each Parameter. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Cost Function or Loss Function, Optimizing Parameters using Gradient Descent, Decreasing Loss Function, Parameter Estimation, Mechanics of Learning, Scaling Factor and Learning Rate, Evaluations of Model, Computing the Derivative of Loss Function and Linear Function, Defining Gradient Function, Partial Derivative and Iterating the Model, The Training Loop and few more Topics related to the same from here. I have presented the Implementation of Loss Function, Computing Derivatives, Gradient Function and Training Loop here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day61 of 300DaysOfData!

• Hyperparameter Tuning: Hyperparameter Tuning refers to the Training of Model's parameters and hyperparameters control how the Training goes. Hyperparameters are generally set manually. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Gradient Descent, Optimizing the Training Loop, Overtraining, Convergence and Divergence, Learning Rate, Hyperparameter Tuning, Normalizing the Inputs, Visualization or Plotting the Data, Argument Unpacking, PyTorch's Autograd and Backpropagation, Chain Rule, Linear Model and few more Topics related to the same from here. I have presented the simple Implementation of Training Loop and Gradient Descent along with Visualization using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day62 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Gradient Descent, PyTorch's Autograd and Backpropagation, Chain Rule and Tensors, Grad Attribute and Parameters, Simple Linear Function and Simple Loss Function, Accumulating Grad Functions, Zeroing the Gradients, Autograd Enabled Training Loop, Optimizers and Vanilla Gradient Descent and Optim Submodule of Torch and few more Topics related to the same from here. I have presented the simple Implementation of Linear Model and Loss Function, Autograd Enabled Training Loop using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day63 of 300DaysOfData!

• Stochastic Gradient Descent: Stochastic Gradient Descent or SGD comes from the fact that the Gradient is typically obtained by averaging over a random subset of all Input samples. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Optimizers, Vanilla Gradient Descent Optimization, Stochastic Gradient Descent, Momentum Argument, Minibatch, Learning Rate and Params, Optim Module, Neural Network Models, Adam Optimizers, Backpropagation, Optimizing Weights, Training, Validation and Overfitting, Evaluating the Training Loss, Generalizing to the Validation Set, Overfitting and Penalization Terms and few more Topics related to the same from here. I have presented the Implementation of SGD and Adam Optimizer along with the Training Loop here in the Snapshots. It is the continuation of the previous Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day64 of 300DaysOfData!

• Activation Functions: Activation Functions are Nonlinear which allows the overall network to approximate more complex functions. They are differentiable so that Gradients can be computed through them. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I am learning to use a Neural Network to fit the Data, Artificial Neurons, The Learning Process and Loss Function, Non Linear Activation Functions, Weights and Biases, Composing a Multilayer Network, Understanding the Error Function, Capping and Compressing the Output Range, Tanh and ReLU Activations, Choosing the Activation Functions, The PyTorch NN Module and few more Topics related to the same from here. I have presented the simple Implementation of Linear Model and Training Loop using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day65 of 300DaysOfData!

• Activation Functions: Activation Functions are Nonlinear which allows the overall network to approximate more complex functions. They are differentiable so that Gradients can be computed through them. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about The PyTorch NN Module, Simple Linear Model, Batching Input Data, Optimizing Batches, Mean Square Error Loss Function, Training Loop, Neural Networks, Sequential Model, Tanh Activation Function, Inspecting Parameters, Weights and Biases, OrderedDict Module, Comparing to the Linear Model, Overfitting and few more Topics related to the same form here. I have presented the simple Implementation of Sequential Model and OrderedDict Submodule using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day66 of 300DaysOfData!

• Computer Vision: Computer Vision is an Interdisciplinary scientific field that deals with how computers can gain high level understanding from digital images or videos. It seeks to understand and automate tasks that the human visual system can do. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have started the new Topic Learning From Images. I have learned about Simple Image Recognition, CIFAR10 which is a Dataset of Tiny Images, Torch Vision Module, The Dataset Class, Iterable Dataset, Python Imaging Library or PIL Package, Dataset Transforms, Arrays and Tensors, Permute Function and few more Topics related to the same. I have presented the simple Implementation of Torch Vision Module along with CIFAR10 Dataset using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day67 of 300DaysOfData!

• Computer Vision: Computer Vision is an Interdisciplinary scientific field that deals with how computers can gain high level understanding from digital images or videos. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Permutation Function, Normalizing the Data, Stacking, Mean and Standard Deviation, Torch Vision Module and Submodules, CIFAR10 Dataset, PIL Package, Image Recognition, Building the Dataset, Building a fully connected Neural Networks Model, Sequential Model, Simple Linear Model, Classification and Regression Problems, One Hot Encoding and Softmax and few more Topics related to the same from here. I have presented the Implementation of Normalizing the Data, Building the Dataset and Neural Network Model using Torch Vision Modules here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day68 of 300DaysOfData!

• Softmax Function: Softmax Function is a type of function that takes a vector of values and produces another vector of the same dimension where the values satisfy the constraints presented as Probabilities. Softmax is a monotone function that the lower values in the input will correspond to lower values in the output. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Representing Output as Probabilities and Softmax Function, PyTorch's NN Module, Backpropagation, A Loss for Classification, MSE Loss, Negative Log Likelihood or NLL Loss, Log Softmax Function, Training the Classifier, Stochastic Gradient Descent, Hyperparameters, Minibatches and few more Topics related to the same from here. I have presented the Implementation of Softmax Function, Building Neural Network Model and Training Loop using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day69 of 300DaysOfData!

• Cross Entropy Loss: Cross Entropy Loss is a negative log likelihood of the predicted distribution under the target distribution as an outcome. The combination of Log Softmax Function and NLL Loss Function is equivalent to using Cross Entropy Loss. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Gradient Descent, Minibatches and Data Loader, Stochastic Gradient Descent, Neural Network Model, Log Softmax Function, NLL Loss Function, Cross Entropy Loss Function, Trainable Parameters, Weights an Biases, Translation Invariant, Data Augment, Torch Vision and NN Modules and few more Topics related to the same from here. I have presented the Implementation of Building Deep Neural Network, Training Loop and Model Evaluation using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day70 of 300DaysOfData!

• Translational Invariance: Translational Invariance makes the Convolutional Neural Network invariant to translation which means that if we translate the Inputs then the CNN will still be able to detect the class to which the Input belongs. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have started reading the Topic Using Convolutions to Generalize. I have learned about Convolutional Neural Network, Translation Invariant, Weights and Biases, Discrete Cross Correlations, Locality or Local Operations on Neighborhood Data, Model Parameters, Multi Channel Image, Padding the Boundary, Kernel Size, Detecting Features with Convolutions and few more Topics related to the same. I have presented the simple Implementation of CNN and Building the Data using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day71 of 300DaysOfData!

• Down Sampling: Down Sampling is the scaling of an Image by half which is equivalent of taking four neighboring pixels as input and producing one pixel as Output. Down Sampling principle can be implemented in different ways. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Kernel Size, Padding the Image, Edge Detection Kernel, Locality and Translation Invariant, Learning Rate and Weight Update, Max Pooling Layer and Down Sampling, Stride, Convolutional Neural Networks, Receptive Field, Tanh Activation Function, Simple Linear Model, Sequential Model, Parameters of the Model and few more Topics related to the same from here. I have presented the Implementation of Convolutional Neural Network, Plotting the Image and Inspecting the Parameters of the Model using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day72 of 300DaysOfData!

• Down Sampling: Down Sampling is the scaling of an Image by half which is equivalent of taking four neighboring pixels as input and producing one pixel as Output. Down Sampling principle can be implemented in different ways. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Sub Classing the NN Module, The Sequential or The Modular API, Forward Function, Linear Model, Max Pooling Layer, Padding the Data, Convolutional Neural Network Architecture, ResNet, Kernel Size and Attributes, Tanh Activation Function, Model Parameters, The Functional API, Stateless Modules and few more Topics related to the same from here. I have presented the Implementation of Sub Classing the NN Module using The Sequential API and The Functional API using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day73 of 300DaysOfData!

• Down Sampling: Down Sampling is the scaling of an Image by half which is equivalent of taking four neighboring pixels as input and producing one pixel as Output. Down Sampling principle can be implemented in different ways. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about The Torch NN Module, The Functional API, Convolutional Neural Network and The Training, The Data Loader Module, Forward and Backward Pass of the Network, Stochastic Gradient Descent Optimizer, Zeroing the Gradients, Cross Entropy Loss Function, Model Evaluation and Gradient Descent and few more Topics related to the same from here. I have presented the Implementation of Training Loop and Model Evaluation using PyTorch here in the Snapshot. Actually, It is the continuation of yesterday's Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day74 of 300DaysOfData!

• Down Sampling: Down Sampling is the scaling of an Image by half which is equivalent of taking four neighboring pixels as input and producing one pixel as Output. Down Sampling principle can be implemented in different ways such as Max Pooling. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Saving and Loading the Model, Weights and Parameters of the Model, Training the Model on GPU, The Torch NN Module and Sub Modules, Map Location Keyword, Designing Model, Long Short Term Memory or LSTM, Adding Memory Capacity or Width to the Network, Feed Forward Network, Overfitting and few more Topics related to the same from here. I have presented the Implementation of Adding Memory Capacity or Width to the Network using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day75 of 300DaysOfData!

• L2 Regularization: L2 Regularization is the sum of the squares of all the weights in the Model whereas L1 Regularization is the sum of the absolute values of all the weights in the Model. L2 Regularization is also referred to as Weight Decay. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Convolutional Neural Network, L2 Regularization and L1 Regularization, Optimization and Generalization, Weight Decay, The PyTorch NN Module and Sub Modules, Stochastic Gradient Descent Optimizer, Overfitting and Dropout, Deep Neural Networks, Randomization and few more Topics related to the same from here. I have presented the Implementation of L2 Regularization and Dropout Layer using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day76 of 300DaysOfData!

• L2 Regularization: L2 Regularization is the sum of the squares of all the weights in the Model whereas L1 Regularization is the sum of the absolute values of all the weights in the Model. L2 Regularization is also referred to as Weight Decay. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Dropout Module, Batch Normalization and Non Linear Activation Functions, Regularization and Principled Augmentation, Convolutional Neural Networks, Minibatch and Standard Deviation, Deep Neural Networks and Depth Module, Skip Connections Mechanism, ReLU Activation Function, Implementation of Functional API and few more Topics related to the same from here. I have presented the Implementation of Batch Normalization and Deep Neural Networks and Depth Module using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day77 of 300DaysOfData!

• Identity Mapping: When the output of the first activations is used as the input of the last in addition to the standard feed forward path then it is called the Identity Mapping. Identity Mapping alleviate the issues of vanishing gradients. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Convolutional Neural Networks, Skip Connections, ResNet Architecture, Simple Linear Layer, Max Pooling Layer, Identity Mapping, Highway Networks, UNet Model, Dense Networks and Very Deep Neural Networks, Sequential and Functional API, Forward and Backpropagation, Torch Vision Module and Sub Modules, Batch Normalization Layer, Custom Initializations and few more Topics related to the same from here. I have presented the Implementation of ResNet Architecture and Very Deep Neural Networks using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day78 of 300DaysOfData!

• Voxel: A Voxel is the 3D equivalent to the familiar 2D pixel. It encloses a volume of space rather than an area. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about CT Scan Dataset, Voxel, Segmentation, Grouping and Classification, Nodules, 3D Convolutions, Neural Networks, Downloading the LUNA Dataset, Data Loading, Parsing the Data, Training and Validation Set and few more Topics related to the same from here. I have started working with LUNA Dataset which stands for Lung Nodule Analysis 2016. The LUNA Grand Challenge is the combination of an open dataset with high quality labels of patient CT scans: many with lung nodules and a public ranking of classifiers against the data. I have presented the Implementation of Preparing the Data using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day79 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Data Loading and Parsing the Data, CT Scan Dataset, Data Pipeline and few more Topics related to the same from here. Besides, I have also learned about Auto Encoders, Recurrent Neural Networks and Long Short Term Memory or LSTM, Data Processing, One Hot Encoding, Random Splitting of Training and Validation Dataset and few more. I have continued working with LUNA Dataset which stands for Lung Nodule Analysis 2016. The LUNA Grand Challenge is the combination of an open dataset with high quality labels of patient CT scans: many with lung nodules and a public ranking of classifiers against the data. I have presented the simple Implementation of Data Preparation using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day80 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about Loading the Individual CT Scans Dataset, 3D Nodules Density Data, SimpleITK Library, Hounsfield Units, Voxels, Batch Normalization, Loading a Nodule using the Patient Coordinate System, Converting between Millimeters and Voxel Addresses, Array Coordinates, Matrix Multiplication and few more Topics related to the same from here. Besides I have also learned about Auto Encoders using LSTM, Stateful Decoder Model and Data Visualization. I have continued working with LUNA Dataset which stands for Lung Nodule Analysis 2016. I have presented the Implementation of Conversion between Patient Coordinates and Arrays Coordinates on CT Scans Dataset using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day81 of 300DaysOfData!

• Voxel and Nodules: A Voxel is the 3D equivalent to the familiar 2D pixel. It encloses a volume of space rather than an area. A mass of tissue made of proliferating cell in the lung is called a Tumor. A small Tumor just a few millimeters wide is called a Nodules. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Deep Learning with PyTorch. Here, I have learned about PyTorch Dataset Instance Implementation, LUNA Dataset Class, Cross Entropy Loss, Positive and Negative Nodules, Arrays and Tensors, Caching Candidate Arrays, Training and Validation Datasets, Data Visualization and few more Topics related to the same from here. Besides I have also learned about about Normalization of Data, Variance Threshold, RDKIT Library and few more Topics related to the same. I have presented the Implementation of Preparing the LUNA Dataset using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Deep Learning with PyTorch

Day82 of 300DaysOfData!

• Tagging Algorithms: The problem of learning to predict classes that are not mutually exclusive is called Multilabel Classification. Auto Tagging Problems are best described as Multilabel Classification Problems. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about A Motivating Example on Machine Learning, Learning Algorithms, Training Process, Data, Features, Models, Objective Functions, Optimization Algorithms, Supervised Learning, Regression, Binary, Multiclass and Hierarchical Classification, Cross Entropy and Mean Squared Error Loss Functions, Gradient Descent, Tagging Algorithms and few more Topics related to the same from here. I have presented the Implementation of Preparing the Data, Normalization, Removing Low Variance Features and Data Loaders using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day83 of 300DaysOfData!

• Reinforcement Learning: Reinforcement Learning gives a very general statement of problem in which an agent interacts with the environment over a series of time steps and receives some observation and must choose action. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Search Algorithms, Recommender Systems, Sequence Learning, Tagging and Parsing, Machine Translation, Unsupervised Learning, Interacting with an Environment and Reinforcement Learning, Data Manipulation, Mathematical Operations, Broadcasting Mechanisms, Indexing and Slicing, Saving Memory in Tensors, Conversion to Other Datatypes and few more Topics related to the same from here. I have presented the Implementation of Mathematical Operations, Tensors Concatenation, Broadcasting Mechanisms and Datatypes Conversion using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day84 of 300DaysOfData!

• Tensors: Tensors refer to algebraic objects describing the n dimensional arrays with an arbitrary number of axes. Vectors are first order Tensors and Matrices are second order Tensors. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Data Processing, Reading the Dataset, Handling the Missing Data, Categorical Data, Conversion to the Tensor Format, Linear Algebra such as Scalars, Vectors, Length, Dimensionality and Shape, Matrices, Symmetric Matrix, Tensors, Basic Properties of Tensor Arithmetic, Reduction, Non Reduction Sum, Dot Products, Matrix Vector Products and few more Topics related to the same from here. I have presented the Implementation of Data Processing, Handling the Missing Data, Scalars, Vectors, Matrices and Dot Products using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day85 of 300DaysOfData!

• Method of Exhaustion: The ancient process of finding the area of curved shapes such as circle by inscribing the polygons in such shapes which better approximate the circle is called the Method of Exhaustion. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Matrix Multiplication, L1 and L2 Normalization, Frobenius Normalization, Calculus, Method of Exhaustion, Derivatives and Differentiation, Partial Derivatives, Gradient Descents, Chain Rule, Automatic Differentiation, Backward for Non Scalar Variables, Detaching Computation, Backpropagation, Computing the Gradient with Control Flow and few more Topics related to the same from here. I have presented the Implementation of Matrix Multiplication, L1, L2 and Frobenius Normalization, Derivatives and Differentiation, Automatic Differentiation and Computing the Gradient using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day86 of 300DaysOfData!

• Method of Exhaustion: The ancient process of finding the area of curved shapes such as circle by inscribing the polygons in such shapes which better approximate the circle is called the Method of Exhaustion. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Probabilities, Basic Probability Theory, Sampling, Multinomial Distribution, Axioms of Probability Theory, Random Variables, Dealing with Multiple Random Variables, Joint Probability, Conditional Probability, Bayes Theorem, Marginalization, Independence and Dependence, Expectation and Variance, Finding Classes and Functions in a Module and few more Topics related to the same from here. I have presented the Implementation of Multinomial Distribution, Visualization of Probabilities, Derivatives and Differentiation using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day87 of 300DaysOfData!

• Hyperparameters: The parameters that are tunable but not updated in the training loop are called Hyperparameters. Hyperparameters Tuning is the process by which hyperparameters are chosen and typically requires adjusting based on the results of the Training Loop. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Linear Regression, Basic Elements of Linear Regression, Linear Model and Transformation, Loss Function, Analytic Solution, Minibatch Stochastic Gradient Descent, Making Predictions with the Learned Model, Vectorization of Speed, The Normal Distribution and Squared Loss, Linear Regression to Deep Neural Networks, Biological Interpretation, Hyperparameters Tuning and few more Topics related to the same from here. I have presented the Implementation of Vectorization of Speed and Normal Distributions using Python here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day88 of 300DaysOfData!

• Hyperparameters: The parameters that are tunable but not updated in the training loop are called Hyperparameters. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Linear Regression Implementation From Scratch, Data Pipeline, Deep Learning Frameworks, Generating the Artificial Dataset, Scatter Plot and Correlation, Reading the Dataset, Minibatches, Features and Labels, Parallel Computing, Initializing the Model Parameters, Minibatch Stochastic Gradient Descent, Defining the Simple Linear Regression Model, Broadcasting Mechanism, Vectors and Scalars and few more Topics related to the same from here. I have presented the Implementation of Generating the Synthetic Dataset, Generating the Scatter Plot, Reading the Dataset, Initializing the Model Parameters and Defining the Linear Regression Model using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day89 of 300DaysOfData!

• Linear Regression: Linear Regression is a linear approach to modelling the relationship between a scalar response and one or more explanatory variables also known as dependent variables and independent variables. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Linear Regression, Defining the Loss Function, Defining the Optimization Algorithm, Minibatch Stochastic Gradient Descent, Training the Model, Tensors and Differentiation, Concise Implementation of Linear Regression, Generating the Synthetic Dataset, Model Evaluation and few more Topics related to the same from here. I have presented the Implementation of Defining the Loss Function, Minibatch Stochastic Gradient Descent, Training and Evaluating the Model, Concise Implementation of Linear Regression and Reading the Dataset using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day90 of 300DaysOfData!

• Linear Regression: Linear Regression is a linear approach to modelling the relationship between a scalar response and one or more explanatory variables also known as dependent variables and independent variables. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Softmax Regression, Classification Problem, Network Architecture, Parameterization Cost of Fully Connected Layers, Softmax Operation, Vectorization for Minibatches, Loss Function, Log Likelihood, Softmax and Derivatives, Cross Entropy Loss, Information Theory Basics, Entropy and Surprisal, Model Prediction and Evaluation, The Image Classification Dataset and few more Topics related to the same from here. I have presented the Implementation of Image Classification Dataset, Visualization, Softmax Regression and Operation along with Model Parameters using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day91 of 300DaysOfData!

• Activation Functions: Activation Functions decide whether a neuron should be activated or not by calculating the weighted sum and further adding bias with it. They are differentiable operators. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Cross Entropy Loss Function, Classification Accuracy and Training, Softmax Regression, Model Parameters, Optimization Algorithms, Multi Layer Perceptrons, Hidden Layers, Linear Models Problems, From Linear to Nonlinear Models, Universal Approximators, Activation Functions like RELU Function, Sigmoid Function, Tanh Function, Derivatives and Gradients and few more Topics related to the same from here. I have presented the Implementation of Softmax Regression Model, Classification Accuracy, RELU Function, Sigmoid Function, Tanh Function along with Visualizations using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day92 of 300DaysOfData!

• Activation Functions: Activation Functions decide whether a neuron should be activated or not by calculating the weighted sum and further adding bias with it. They are differentiable operators. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Implementation of Multi Layer Perceptrons, Initializing Model Parameters, RELU Activation Functions, Cross Entropy Loss Function, Training the Model, Fully Connected Layers, Simple Linear Layer, Softmax Regression and Function, Stochastic Gradient Descent, Sequential API, High Level APIs, Learning Rate, Weights and Biases, Tensors, Hyperparameters and few more Topics related to the same from here. I have presented the Implementation of Multi Layer Perceptrons, RELU Activation Function, Training the Model and Model Evaluations using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day93 of 300DaysOfData!

• Multi Layer Perceptrons: The simplest deep neural networks are called Multi Layer Perceptrons. They consist of multiple layers of Neurons. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Model Selection, Underfitting, Overfitting, Training Error and Generalization Error, Statistical Learning Theory, Model Complexity, Early Stopping, Training, Testing and Validation Dataset, K-Fold Cross Validation, Dataset Size, Polynomial Regression, Generating the Dataset, Training and Testing the Model, Third Order Polynomial Function Fitting, Linear Function Fitting, High Order Polynomial Function Fitting, Weight Decay, Normalization and few more Topics related to the same from here. I have presented the Implementation of Generating the Dataset, Defining the Training Function and Polynomial Function Fitting using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day94 of 300DaysOfData!

• Multi Layer Perceptrons: The simplest deep neural networks are called Multi Layer Perceptrons. They consist of multiple layers of neurons each fully connected to those in layers below from which they receive input and above which in turn influence. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about High Dimensional Linear Regression, Model Parameters, Defining L2 Normalization Penalty, Defining the Training Loop, Regularization and Weight Decay, Dropout and Overfitting, Bias and Variance Tradeoff, Gaussian Distributions, Stochastic Gradient Descent, Training Error and Test Error and few more Topics related to the same from here. I have presented the Implementation of High Dimensional Linear Regression, Model Parameters, L2 Normalization Penalty, Regularization and Weight Decay using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day95 of 300DaysOfData!

• Dropout and Co-adaption: Dropout is the process of injecting noise while computing each internal layer during forward propagation. Co-adaption is the condition in neural network which is characterized by a state in which each layer relies on the specific pattern of the activations in the previous layer. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Dropout, Overfitting, Generalization Error, Bias and Variance Tradeoff, Robustness through Perturbations, L2 Regularization and Weight Decay, Co-adaption, Dropout Probability, Dropout Layer, Fashion MNIST Dataset, Activation Functions, Stochastic Gradient Descent, The Sequential and Functional API and few more Topics related to the same from here. I have presented the Implementation of Dropout Layer, Training and Testing the Model using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day96 of 300DaysOfData!

• Dropout and Co-adaption: Dropout is the process of injecting noise while computing each internal layer during forward propagation. Co-adaption is the condition in neural network which is characterized by a state in which each layer relies on the specific pattern of the activations in the previous layer. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Forward Propagation, Backward Propagation and Computational Graphs, Numerical Stability, Vanishing and Exploding Gradients, Breaking the Symmetry, Parameter Initialization, Environment and Distribution Shift, Covariate Shift, Label Shift, Concept Shift, Non stationary Distributions, Empirical Risk and True Risk, Batch Learning, Online Learning, Reinforcement Learning and few more Topics related to the same from here. I have presented the Implementation of Data Preprocessing and Data Preparation using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning
• Predicting Housing Prices

Day97 of 300DaysOfData!

• On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Training and Building Deep Networks, Downloading and Caching Datasets, Data Preprocessing, Regression Problems, Accessing and Reading the Dataset, Numerical and Discrete Categorical Features, Optimization and Variance, Arrays and Tensors, Simple Linear Model, The Sequential API, Root Mean Squared Error, Adam Optimizer, Hyperparameter Tuning, K-Fold Cross Validation, Training and Validation Error, Model Selection, Overfitting and Regularization and few more Topics related to the same from here. I have presented the Implementation of Simple Linear Model, Root Mean Squared Error, Training Function and K-Fold Cross Validation using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning
• Predicting Housing Prices

Day98 of 300DaysOfData!

• Constant Parameters: Constant Parameters are the terms that are neither the result of the previous layers nor updatable parameters in the Neural Networks. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about K-Fold Cross Validation, Training and Predictions, Hyperparameters Optimization, Deep Learning Computation, Layers and Blocks, Softmax Regression, Multi Layer Perceptrons, ResNet Architecture, Forward and Backward Propagation Function, RELU Activation Function, The Sequential Block Implementation, MLP Implementation, Constant Parameters and few more Topics related to the same from here. I have presented the Implementation of MLP, The Sequential API Class and Forward Propagation Function using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning
• Predicting Housing Prices

Day99 of 300DaysOfData!

• Constant Parameters: Constant Parameters are the terms that are neither the result of the previous layers nor updatable parameters in the Neural Networks. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Parameter Management, Parameter Access, Targeted Parameters, Collecting Parameters from Nested Block, Parameter Initialization, Custom Initialization, Tied Parameters, Deferred Initialization, Multi Layer Perceptrons, Input Dimensions, Defining Custom Layers, Layers without Parameters, Forward Propagation Function, Constant Parameters, Xavier Initializer, Weight and Bias and few more Topics related to the same from here. I have presented the Implementation of Parameter Access, Parameter Initialization, Tied Parameters and Layers without Parameters using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day100 of 300DaysOfData!

• Invariance and Locality Principle: Translation Invariance principle states that out network should respond similarly to the same patch regardless of where it appears in the image. Locality Principle states that the network should focus on local regions without regard to the contents of the image in distant regions. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Fully Connected Layers to Convolutions, Translation Invariance, Locality Principle, Constraining the MLP, Convolutional Neural Networks, Cross Correlation, Images and Channels, File IO, Loading and Saving Tensors, Loading and Saving Model Parameters, Custom Layers, Layers with Parameters and few more Topics related to the same from here. I have presented the Implementation of Layers with Parameters, Loading and Saving the Tensors and Model Parameters using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day101 of 300DaysOfData!

• Invariance and Locality Principle: Translation Invariance principle states that out network should respond similarly to the same patch regardless of where it appears in the image. Locality Principle states that the network should focus on local regions without regard to the contents of the image in distant regions. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Convolutional Neural Networks, Convolutions for Images, The Cross Correlation Operation, Convolutional Layers, Constructor and Forward Propagation Function, Weight and Bias, Object Edge Detection in Images, Learning a Kernel, Back Propagation, Feature Map and Receptive Field, Kernel Parameters and few more Topics related to the same from here. I have presented the Implementation of Cross Correlation Operation, Convolutional Layers and Learning a Kernel using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day102 of 300DaysOfData!

• Maximum Pooling: Pooling Operators consist of a fixed shape window that is slid over all the regions in the input according to its stride computing a single output for each location which is either maximum or the average value of the elements in the pooling window. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Padding and Stride, Strided Convolutions, Cross Correlations, Multiple Input and Multiple Output Channels, Convolutional Layer, Maximum Pooling Layer and Average Pooling Layer, Pooling Window and Operators, Convolutional Neural Networks, LeNet Architecture, Supervised Learning, Convolutional Encoder, Sigmoid Activation Function and few more Topics related to the same from here. I have presented the Implementation of CNN, Implementation of Padding, Stride and Pooling Layers, Multiple Channels using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day103 of 300DaysOfData!

• VGG Networks: VGG Networks construct a network using reusable convolutional blocks. VGG Models are defined by the number of convolutional layers and output channels in each block. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Convolutional Neural Networks, Supervised Learning, Deep CNN and AlexNet, Support Vector Machine and Features, Learning Representations, Data and Hardware Accelerator Problems, Architectures of LeNet and AlexNet, Activation Functions such as ReLU, Networks using CNN Blocks, VGG Neural Networks Architecture, Padding and Pooling, Convolutional Layers, Dropout, Dense and Linear Layers and few more Topics related to the same from here. I have presented the Implementation of AlexNet Architecture and VGG Networks Architecture along with CNN Blocks using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day104 of 300DaysOfData!

• VGG Networks: VGG Networks construct a network using reusable convolutional blocks. VGG Models are defined by the number of convolutional layers and output channels in each block. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Network In Network or NIN Architecture, NIN Blocks and Model, Convolutional Layer, RELU Activation Function, The Sequential and Functional API, Global Average Pooling Layer, Networks with Parallel Concatenations or GoogLeNet, Inception Blocks, GoogLeNet Model and Architecture, Maximum Pooling Layer, Training the Model and few more Topics related to the same from here. I have presented the Implementation of NIN Block and Model, Inception Block and GoogLeNet Model using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning

Day105 of 300DaysOfData!

• Batch Normalization: Batch Normalization continuously adjusts the intermediate output of the neural network by utilizing the mean and standard deviation of the minibatch so that the values of the intermediate output are more stable. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book Dive into Deep Learning. Here, I have learned about Batch Normalization, Training Deep Neural Networks, Scale Parameter and Shift Parameter, Batch Normalization Layers, Fully Connected Layers, Convolutional Layers, Batch Normalization during Prediction, Tensors, Mean and Variance, Applying BN in LeNet, Concise Implementation of BN using high level API, Internal Covariate Shift, Dropout Layer, Residual Networks or ResNet, Function Classes, Residual Blocks and few more Topics related to the same from here. I have presented the Implementation of Batch Normalization Architecture using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!
• Book:
  • Dive into Deep Learning