- The Development Of Neural Networks
- Receptive Field in CNN
- Standard Gaussian Distribution - Modelling Nature
- Convolution - Math Driving The Computer Vision
- Half Order Derivatives
- Fourier Transforms For Image Processing
- Singular Value Decomposition — Diagnolization of Square Matrix
- Can you find Inverse of Rectangular Matrix? YES, Go through this
- Intuitively Understanding Convolutions for Deep Learning
- Image Segmentation - Basics From TensorFlow
- UNet — Line by Line Explanation
- U-Net: Convolutional Networks for Biomedical Image Segmentation
- ANT-UNet: Accurate and Noise-Tolerant Segmentation for Pathology Image Processing
- Deep CNN for Removal of Salt and Aepper Noise
- A noise robust convolutional neural network for image classification
- Xception: Deep Learning with Depthwise Separable Convolutions
- Advanced Guide to Inception v3
- Inception V3 - Keras Blog
- Deep Residual Learning for Image Recognition
Resources — Popular Modern & Traditional Machine Learning Algorithms — Theory — Math — Implementation
- Machine Learning Cheatsheet — be used to with ML terms
- Deep Learning Book
- Basic Image Processing — learn basics of image processing for image-preprocessing.
- Xgboost with Different Categorical Encoding Methods
- Linear Regression | Lasso Regression | Ridge Regession — details of regression concepts with thoery and code.
- Magic Behind, Gaussian Naive Bias Classification Algorithm
- The Theory and Code Behind K-Nearest Neighbors
- Learn About Decision Trees — Working and Methods in Layman's Term With Code
- Get Used With Logistic Regression — With Code and Math Running Behind This Algorithm
- Various Kinds of Distances in Data Mining and Machine Learning
- Bayes' Theorem
- Chapter I Vectors
- Chapter II Linear combinations, span, and basis vectors
- Chapter III Linear transformations and matrices
- Chapter IV Matrix multiplication as composition
- Chapter V Three-dimensional linear transformations
- Chapter VI The determinant
- Chapter VII Inverse matrices, column space and null space
- Chapter VIII Nonsquare matrices as transformations between dimensions
- Chapter IX Dot products and duality
- Chapter X Cross products
- Chapter XI Cross products in the light of linear transformations
- Chapter XII Cramer's rule, explained geometrically
- Chapter XIII Change of basis
- Chapter XIV Eigenvectors and eigenvalues
- Chapter XV A quick trick for computing eigenvalues
- Chapter XVI Abstract vector spaces
- Radon Transformation
- Fourier Transform
- Hankel Transformation
- Cross Correlation - Generalized Projection of Function Into Reference Vector
- Autocorrelation
- Convolution
- Correlation
- Laplace Transformation
- Kullback–Leibler Divergence
- Creating Neural Network From Scratch — Step By Step With Pythonic Code
- Learn About Bayesian Deep Learning
- Learn Neural Networks and Deep Learning From Scratch — Theory
- Learn BERT — Bidirectional Encoder Representations from Transformers — state-of-art NLP model
- Generative Pre-trained Transformer 3 (GPT-3) — revolutionary NLP model — 515 times more powerful than BERT
- XGBoost Tutorials — Docs from the creater themselves
- ML Ops: Machine Learning as an Engineering Discipline
- Rules of Machine Learning : Best Practices for ML Engineering
- Regular Expression — Official Python Regex Module
- Learn Regex
- Regex Made Easy With Real Python
- Regular Expressions Demystified
- Dive Into Python
- Learn About Python's Pathlib — No Really, Python's Pathlib is Great
- Python 101
- Object Oriented With Python — Wholesome Blog For Learning OOP with Python 3
- Code Refactoring for Software Engineering
- Guide to Python Design Patterns
- Popular Python Design Patterns - Explicitely Python
- Learn Python By Doing Python
- Writing Pythonic Code — Transforming from messy code to beautiful pythonic code
- Write More Pythonic Code
- PEP 8 -- Style Guide for Python Code
- The Hitchhiker’s Guide to Python!
- Article: Securely storing configuration credentials in a Jupyter Notebook
- Article: Automatically Reload Modules with %autoreload
- Calmcode: ipywidgets
- Documentation: Jupyter Lab
- Pluralsight: Getting Started with Jupyter Notebook and Python
- Youtube: William Horton - A Brief History of Jupyter Notebooks
- Youtube: I Like Notebooks
- Youtube: I don't like notebooks.- Joel Grus (Allen Institute for Artificial Intelligence)
- Youtube: Ryan Herr - After model.fit, before you deploy| JupyterCon 2020
- Youtube: nbdev live coding with Hamel Husain
- Youtube: How to Use JupyterLab
- Article: Stacking made easy with Sklearn
- Article: Curve Fitting With Python
- Article: A Guide to Calibration Plots in Python
- Calmcode: human-learn
- Datacamp: Supervised Learning with scikit-learn
- Datacamp: Machine Learning with Tree-Based Models in Python
- Datacamp: Introduction to Linear Modeling in Python
- Datacamp: Linear Classifiers in Python
- Datacamp: Generalized Linear Models in Python
- Notebook: scikit-learn tips
- Pluralsight: Building Machine Learning Models in Python with scikit-learn
- Video: human learn
- Youtube: dabl: Automatic Machine Learning with a Human in the Loop
00:25:43
- Youtube: Multilabel and Multioutput Classification -Machine Learning with TensorFlow & scikit-learn on Python
- Youtube: DABL: Automatic machine learning with a human in the loop- AI Latim American SumMIT Day 1
- Coursera: Introduction to Tensorflow
- Coursera: Convolutional Neural Networks in TensorFlow
- Deeplizard: Keras - Python Deep Learning Neural Network API
- Book: Deep Learning with Python (Page: 276)
- Datacamp: Deep Learning in Python
- Datacamp: Convolutional Neural Networks for Image Processing
- Datacamp: Introduction to TensorFlow in Python
- Datacamp: Introduction to Deep Learning with Keras
- Datacamp: Advanced Deep Learning with Keras
- Google: Machine Learning Crash Course
- Pluralsight: Deep Learning with Keras
- Udacity: Intro to TensorFlow for Deep Learning
- Version Control Via Git
- A Sucessful Git Branching Model
- Git & Github Crash Course
- Everything About Git & Gitbash
- Lecture 1 | Introduction to Convolutional Neural Networks
- Lecture 2 | Image Classification
- Lecture 3 | Loss Functions and Optimizations
- Lecture 4 | Introduction to Neural Networks
- Lecture 5 | Convolutional Neural Networks
- Lecture 6 | Training Neural Networks I
- Lecture 7 | Training Neural Networks II
- Lecture 8 | Deep Learning Software
- Lecture 9 | CNN Architectures
- Lecture 10 | Recurrent Neural Networks
- Lecture 11 | Detection and Segmentation
- Lecture 12 | Visualizing and Understanding
- Lecture 13 | Generative Models
- Lecture 14 | Deep Reinforcement Learning
- Lecture 15 | Efficient Methods and Hardware for Deep Learning
- Lecture 16 | Adversarial Examples and Adversarial Training
Master The Computer Vision — List of blogs and tutorials for diving deep into world of intelligent vision
- Linear Algebra
- Singular Value Decomposition
- Basic Pattern Recognition
- Reduce The Dimesnion — PCA
- Guide To Kalman Filtering
- Fourtier Transforms
- Linear Discriminant Analysis
- Probability, Bayes rule, Maximum Likelihood, MAP
- Mixtures and Expectation-Maximization Algorithm
- Introductory level Statistical Learning
- Hidden Markov Models
- Support Vector Machines
- Genetic Algorithms
- Bayesian Networks
- StatQuest: Machine Learning
- StatQuest: Fitting a line to data, aka least squares, aka linear regression.
0:09:21
- StatQuest: Linear Models Pt.1 - Linear Regression
0:27:26
- StatQuest: StatQuest: Linear Models Pt.2 - t-tests and ANOVA
0:11:37
- StatQuest: Odds and Log(Odds), Clearly Explained!!!
0:11:30
- StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!!
0:16:20
- StatQuest: Logistic Regression
0:08:47
- Logistic Regression Details Pt1: Coefficients
0:19:02
- Logistic Regression Details Pt 2: Maximum Likelihood
0:10:23
- Logistic Regression Details Pt 3: R-squared and p-value
0:15:25
- Saturated Models and Deviance
0:18:39
- Deviance Residuals
0:06:18
- Regularization Part 1: Ridge (L2) Regression
0:20:26
- Regularization Part 2: Lasso (L1) Regression
0:08:19
- Ridge vs Lasso Regression, Visualized!!!
0:09:05
- Regularization Part 3: Elastic Net Regression
0:05:19
- StatQuest: Principal Component Analysis (PCA), Step-by-Step
0:21:57
- StatQuest: PCA main ideas in only 5 minutes!!!
0:06:04
- StatQuest: PCA - Practical Tips
0:08:19
- StatQuest: PCA in Python
0:11:37
- StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
0:15:12
- StatQuest: MDS and PCoA
0:08:18
- StatQuest: t-SNE, Clearly Explained
0:11:47
- StatQuest: Hierarchical Clustering
0:11:19
- StatQuest: K-means clustering
0:08:57
- StatQuest: K-nearest neighbors, Clearly Explained
0:05:30
- Naive Bayes, Clearly Explained!!!
0:15:12
- Gaussian Naive Bayes, Clearly Explained!!!
0:09:41
- StatQuest: Decision Trees
0:17:22
- StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data
0:05:16
- Regression Trees, Clearly Explained!!!
0:22:33
- How to Prune Regression Trees, Clearly Explained!!!
0:16:15
- StatQuest: Random Forests Part 1 - Building, Using and Evaluating
0:09:54
- StatQuest: Random Forests Part 2: Missing data and clustering
0:11:53
- The Chain Rule
0:18:23
- Gradient Descent, Step-by-Step
0:23:54
- Stochastic Gradient Descent, Clearly Explained!!!
0:10:53
- AdaBoost, Clearly Explained
0:20:54
- Gradient Boost Part 1: Regression Main Ideas
0:15:52
- Gradient Boost Part 2: Regression Details
0:26:45
- Gradient Boost Part 3: Classification
0:17:02
- Gradient Boost Part 4: Classification Details
0:36:59
- Support Vector Machines, Clearly Explained!!!
0:20:32
- Support Vector Machines Part 2: The Polynomial Kernel
0:07:15
- Support Vector Machines Part 3: The Radial (RBF) Kernel
0:15:52
- XGBoost Part 1: Regression
0:25:46
- XGBoost Part 2: Classification
0:25:17
- XGBoost Part 3: Mathematical Details
0:27:24
- XGBoost Part 4: Crazy Cool Optimizations
0:24:27
- StatQuest: Fiitting a curve to data, aka lowess, aka loess
0:10:10
- Statistics Fundamentals: Population Parameters
0:14:31
- Principal Component Analysis (PCA) clearly explained (2015)
0:20:16
- Decision Trees in Python from Start to Finish
1:06:23
- StatQuest: Fitting a line to data, aka least squares, aka linear regression.
-
Machine Learning Engineering for Production (MLOps) Specialization — COURSERA SPECIALIZATION
-
MIT: 18.06 Linear Algebra (Professor Strang)
- 1. The Geometry of Linear Equations
0:39:49
- 2. Elimination with Matrices.
0:47:41
- 3. Multiplication and Inverse Matrices
0:46:48
- 4. Factorization into A = LU
0:48:05
- 5. Transposes, Permutations, Spaces R^n
0:47:41
- 6. Column Space and Nullspace
0:46:01
- 9. Independence, Basis, and Dimension
0:50:14
- 10. The Four Fundamental Subspaces
0:49:20
- 11. Matrix Spaces; Rank 1; Small World Graphs
0:45:55
- 14. Orthogonal Vectors and Subspaces
0:49:47
- 15. Projections onto Subspaces
0:48:51
- 16. Projection Matrices and Least Squares
0:48:05
- 17. Orthogonal Matrices and Gram-Schmidt
0:49:09
- 21. Eigenvalues and Eigenvectors
0:51:22
- 22. Diagonalization and Powers of A
0:51:50
- 24. Markov Matrices; Fourier Series
0:51:11
- 25. Symmetric Matrices and Positive Definiteness
0:43:52
- 27. Positive Definite Matrices and Minima
0:50:40
- 29. Singular Value Decomposition
0:40:28
- 30. Linear Transformations and Their Matrices
0:49:27
- 31. Change of Basis; Image Compression
0:50:13
- 33. Left and Right Inverses; Pseudoinverse
0:41:52
- 1. The Geometry of Linear Equations
-
CNN For Visual Recognition — cs231n
- Lecture 1 | Introduction to Convolutional Neural Networks
- Lecture 2 | Image Classification
- lecture 3 | Loss Function and Optimization
- lecture 4 | Introduction to Neural Networks
- Lecture 5 | Convulutional Neural Network
- Lecture 6 | Training Neural Network I
- Lecture 7 | Training Neural Network II
- Lecture 8 | Deep learning Software
- Lecture 9 | CNN Architectures
- Lecture 10 | Recurrent Neural Networks
- Lecture 11 | Detection and Segmentation
- Lecture 12 | Visualizing and Understanding
- Lecture 13 | Generative Models
- Lecture 14 | Deep Reinforcement Learning
- Lecture 15 | Efficient Methods and Hardware for Deep Learning
- Lecture 16 | Adversarial Examples and Adversarial Training
-
Learn eXtreme Gradient Boosting - State-of-art ML Algorithm for Kaggle Contest till date.
- The Twelve Factors
- Book "Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations", 2018 by Nicole Forsgren et.al
- Book "The DevOps Handbook" by Gene Kim, et al. 2016
- State of DevOps 2019
- Clean Code concepts adapted for machine learning and data science.
- School of SRE
- Machine Learning Operations: You Design It, You Train It, You Run It!
- MLOps SIG Specification
- ML in Production
- Awesome production machine learning: State of MLOps Tools and Frameworks
- Udemy “Deployment of ML Models”
- Full Stack Deep Learning
- Engineering best practices for Machine Learning
- 🚀 Putting ML in Production
- Stanford MLSys Seminar Series
- IBM ML Operationalization Starter Kit
- Productize ML. A self-study guide for Developers and Product Managers building Machine Learning products.
- MLOps (Machine Learning Operations) Fundamentals on GCP
- ML full Stack preparation
- Machine Learing Engineering in Production | DeepLearning AI
- AI Infrastructure for Everyone: DeterminedAI
- Deploying R Models with MLflow and Docker
- What Does it Mean to Deploy a Machine Learning Model?
- Software Interfaces for Machine Learning Deployment
- Batch Inference for Machine Learning Deployment
- AWS Cost Optimization for ML Infrastructure - EC2 spend
- CI/CD for Machine Learning & AI
- Itaú Unibanco: How we built a CI/CD Pipeline for machine learning with online training in Kubeflow
- 101 For Serving ML Models
- Deploying Machine Learning models to production — Inference service architecture patterns
- Serverless ML: Deploying Lightweight Models at Scale
- ML Model Rollout To Production. Part 1 | Part 2
- Deploying Python ML Models with Flask, Docker and Kubernetes
- Deploying Python ML Models with Bodywork
- Building dashboards for operational visibility (AWS)
- Monitoring Machine Learning Models in Production
- Effective testing for machine learning systems
- Unit Testing Data: What is it and how do you do it?
- How to Test Machine Learning Code and Systems (Accompanying code)
- Wu, T., Dong, Y., Dong, Z., Singa, A., Chen, X. and Zhang, Y., 2020. Testing Artificial Intelligence System Towards Safety and Robustness: State of the Art. IAENG International Journal of Computer Science, 47(3).
- Multi-Armed Bandits and the Stitch Fix Experimentation Platform
- A/B Testing Machine Learning Models
- Data validation for machine learning. Polyzotis, N., Zinkevich, M., Roy, S., Breck, E. and Whang, S., 2019. Proceedings of Machine Learning and Systems
- Testing machine learning based systems: a systematic mapping
- Explainable Monitoring: Stop flying blind and monitor your AI
- WhyLogs: Embrace Data Logging Across Your ML Systems
- Evidently AI. Insights on doing machine learning in production. (Vendor blog.)
- The definitive guide to comprehensively monitoring your AI
- Introduction to Unit Testing for Machine Learning
- Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance
- Test-Driven Development in MLOps Part 1
- MLOps Infrastructure Stack Canvas
- Rise of the Canonical Stack in Machine Learning. How a Dominant New Software Stack Will Unlock the Next Generation of Cutting Edge AI Apps
- AI Infrastructure Alliance. Building the canonical stack for AI/ML
- Linux Foundation AI Foundation
- ML Infrastructure Tools for Production | Part 1 — Production ML — The Final Stage of the Model Workflow | Part 2 — Model Deployment and Serving
- The MLOps Stack Template (by valohai)
- CS 10 - The Beauty and Joy of Computing - Spring 2015 - Dan Garcia - UC Berkeley InfoCoBuild
- 6.0001 - Introduction to Computer Science and Programming in Python - MIT OCW
- 6.001 - Structure and Interpretation of Computer Programs, MIT
- CS 50 - Introduction to Computer Science, Harvard University (cs50.tv)
- CS 61A - Structure and Interpretation of Computer Programs [Python], UC Berkeley
- CPSC 110 - Systematic Program Design [Racket], University of British Columbia
- CS50's Understanding Technology
- CSE 142 Computer Programming I (Java Programming), Spring 2016 - University of Washington
- CS 1301 Intro to computing - Gatech
- CS 106A - Programming Methodology, Stanford University (Lecture Videos)
- CS 106B - Programming Abstractions, Stanford University (Lecture Videos)
- CS 106X - Programming Abstractions in C++ (Lecture Videos)
- CS 107 - Programming Paradigms, Stanford University
- CmSc 150 - Introduction to Programming with Arcade Games, Simpson College
- LINFO 1104 - Paradigms of computer programming, Peter Van Roy, Université catholique de Louvain, Belgium - EdX
- FP 101x - Introduction to Functional Programming, TU Delft
- Introduction to Problem Solving and Programming - IIT Kanpur
- Introduction to programming in C - IIT Kanpur
- Programming in C++ - IIT Kharagpur
- Python Boot Camp Fall 2016 - Berkeley Institute for Data Science (BIDS)
- CS 101 - Introduction to Computer Science - Udacity
- 6.00SC - Introduction to Computer Science and Programming (Spring 2011) - MIT OCW
- 6.00 - Introduction to Computer Science and Programming (Fall 2008) - MIT OCW
- 6.01SC - Introduction to Electrical Engineering and Computer Science I - MIT OCW
- Modern C++ Course (2018) - Bonn University
- Modern C++ (Lecture & Tutorials, 2020, Vizzo & Stachniss) - University of Bonn
- Object Oriented Design
- Object-oriented Program Design and Software Engineering - Aduni
- OOSE - Object-Oriented Software Engineering, Dr. Tim Lethbridge
- Object Oriented Systems Analysis and Design (Systems Analysis and Design in a Changing World)
- CS 251 - Intermediate Software Design (C++ version) - Vanderbilt University
- OOSE - Software Dev Using UML and Java
- Object-Oriented Analysis and Design - IIT Kharagpur
- CS3 - Design in Computing - Richard Buckland UNSW
- Informatics 1 - Object-Oriented Programming 2014/15- University of Edinburgh
- Software Engineering with Objects and Components 2015/16- University of Edinburgh
- Software Engineering
- Computer Science 169- Software Engineering - Spring 2015 - UCBerkeley
- CS 5150 - Software Engineering, Fall 2014 - Cornell University
- Introduction to Service Design and Engineering - University of Trento, Italy
- CS 164 Software Engineering - Harvard
- System Analysis and Design - IISC Bangalore
- Software Engineering - IIT Bombay
- Dependable Systems (SS 2014)- HPI University of Potsdam
- Software Testing - IIT Kharagpur
- Informatics 2C - Software Engineering 2014/15- University of Edinburgh
- Software Architecture
- Efficient Estimation of Word Representations in Vector Space — Word2Vec
- eXtreme Gradient Boosting — A Scalable Tree Boosting System
- Paper: A Neural Probabilistic Language Model
- Paper: Efficient Estimation of Word Representations in Vector Space
- Paper: Sequence to Sequence Learning with Neural Networks
- Paper: Neural Machine Translation by Jointly Learning to Align and Translate
- Paper: Attention Is All You Need
- Paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- Paper: XLNet: Generalized Autoregressive Pretraining for Language Understanding
- Paper: RoBERTa: A Robustly Optimized BERT Pretraining Approach
- Paper: GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
- Paper: Amazon.com Recommendations Item-to-Item Collaborative Filtering
- Paper: Collaborative Filtering for Implicit Feedback Datasets
- Paper: BPR: Bayesian Personalized Ranking from Implicit Feedback
- Paper: Factorization Machines
- Paper: Wide & Deep Learning for Recommender Systems
- Paper: Multiword Expressions: A Pain in the Neck for NLP
- Paper: PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Paper: ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS
- Paper: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
- Paper: A Simple Framework for Contrastive Learning of Visual Representations
- Paper: Self-Supervised Learning of Pretext-Invariant Representations
- Paper: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
- Paper: Self-Labelling via Simultalaneous Clustering and Representation Learning
- Paper: A survey on Semi-, Self- and Unsupervised Techniques in Image Classification
- Paper: Train Once, Test Anywhere: Zero-Shot Learning for Text Classification
- Paper: Zero-shot Text Classification With Generative Language Models
- Paper: How to Fine-Tune BERT for Text Classification?
- Paper: Universal Sentence Encoder
- Paper: Enriching Word Vectors with Subword Information
- Paper: Beyond Accuracy: Behavioral Testing of NLP models with CheckList
- Paper: Temporal Ensembling for Semi-Supervised Learning
- Paper: Boosting Self-Supervised Learning via Knowledge Transfer
- Paper: Follow-up Question Generation
- Paper: The Hardware Lottery
- Paper: Question Generation via Overgenerating Transformations and Ranking
- Paper: Good Question! Statistical Ranking for Question Generation
- Paper: Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX)
- Paper: Neural Text Generation: A Practical Guide
- Paper: Pest Management In Cotton Farms: An AI-System Case Study from the Global South
- Paper: BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search
- Paper: On the surprising similarities between supervised and self-supervised models
- Paper: All-but-the-Top: Simple and Effective Postprocessing for Word Representations
- Paper: Simple and Effective Dimensionality Reduction for Word Embeddings
- Paper: AutoCompete: A Framework for Machine Learning Competitions
- Paper: Cost-effective Deployment of BERT Models in Serverless Environment
- Paper: Evaluating Large Language Models Trained on Code
- Paper: What Does BERT Learn about the Structure of Language?
- Paper: What do RNN Language Models Learn about Filler–Gap Dependencies?
- Paper: Is this a wampimuk? Cross-modal mapping between distributional semantics and the visual world
- Paper: MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
- Paper: Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs
- Paper: Show and Tell: A Neural Image Caption Generator
- Paper: The Curious Case of Neural Text Degeneration
- Paper: Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
- Paper : Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery
- Fine Tuning Unet For Ultrasound Image Segmentation
Credit: Keep Learning
- A Blog From a Human-engineer-being http://www.erogol.com/ (RSS)
- Aakash Japi http://aakashjapi.com/ (RSS)
- Abhinav Sagar https://medium.com/@abhinav.sagar (RSS)
- Adit Deshpande https://adeshpande3.github.io/ (RSS)
- Advanced Analytics & R http://advanceddataanalytics.net/ (RSS)
- Adventures in Data Land http://blog.smola.org (RSS)
- Ahmed BESBES https://ahmedbesbes.com/ (RSS)
- Ahmed El Deeb https://medium.com/@D33B (RSS)
- Airbnb Data blog https://medium.com/airbnb-engineering/tagged/data-science (RSS)
- Alex Perrier http://alexisperrier.com/ (RSS)
- Algobeans | Data Analytics Tutorials & Experiments for the Layman https://algobeans.com (RSS)
- Amazon AWS AI Blog https://aws.amazon.com/blogs/ai/ (RSS)
- Amit Chaudhary https://amitness.com (RSS)
- Analytics Vidhya http://www.analyticsvidhya.com/blog/ (RSS)
- Analytics and Visualization in Big Data @ Sicara https://blog.sicara.com (RSS)
- Andreas Müller http://peekaboo-vision.blogspot.com/ (RSS)
- Andrej Karpathy blog http://karpathy.github.io/ (RSS)
- Andrey Vasnetsov https://comprehension.ml/ (RSS)
- Andrew Brooks http://brooksandrew.github.io/simpleblog/ (RSS)
- Andrey Kurenkov http://www.andreykurenkov.com/writing/ (RSS)
- Andrii Polukhin https://polukhin.tech/ (RSS)
- Anton Lebedevich's Blog http://mabrek.github.io/ (RSS)
- Arthur Juliani https://medium.com/@awjuliani (RSS)
- Audun M. Øygard http://www.auduno.com/ (RSS)
- Avi Singh https://avisingh599.github.io/ (RSS)
- Beautiful Data http://beautifuldata.net/ (RSS)
- Beckerfuffle http://mdbecker.github.io/ (RSS)
- Becoming A Data Scientist http://www.becomingadatascientist.com/ (RSS)
- Ben Bolte's Blog http://benjaminbolte.com/ml/ (RSS)
- Ben Frederickson http://www.benfrederickson.com/blog/ (RSS)
- Berkeley AI Research http://bair.berkeley.edu/blog/ (RSS)
- Big-Ish Data http://bigishdata.com/ (RSS)
- Blog on neural networks http://yerevann.github.io/ (RSS)
- Blogistic Regression https://wcbeard.github.io/blog/ (RSS)
- blogR | R tips and tricks from a scientist https://drsimonj.svbtle.com/ (RSS)
- Brain of mat kelcey http://matpalm.com/blog/ (RSS)
- Brilliantly wrong thoughts on science and programming https://arogozhnikov.github.io/ (RSS)
- Bugra Akyildiz http://bugra.github.io/ (RSS)
- Carl Shan http://carlshan.com/ (RSS)
- Casual Inference https://lmc2179.github.io/ (RSS)
- Chris Stucchio https://www.chrisstucchio.com/blog/index.html (RSS)
- Christophe Bourguignat https://medium.com/@chris_bour (RSS)
- Christopher Nguyen https://medium.com/@ctn (RSS)
- cnvrg.io blog https://blog.cnvrg.io/ (RSS)
- colah's blog http://colah.github.io/archive.html (RSS)
- Daniel Bourke https://www.mrdbourke.com (RSS)
- Daniel Forsyth http://www.danielforsyth.me/ (RSS)
- Daniel Homola https://danielhomola.com/ (RSS)
- Data Blogger https://www.data-blogger.com/ (RSS)
- Data Double Confirm https://projectosyo.wixsite.com/datadoubleconfirm (RSS)
- Data Miners Blog http://blog.data-miners.com/ (RSS)
- Data Mining Research http://www.dataminingblog.com/ (RSS)
- Data Mining: Text Mining, Visualization and Social Media http://datamining.typepad.com/data_mining/ (RSS)
- Data School http://www.dataschool.io/ (RSS)
- Data Science 101 http://101.datascience.community/ (RSS)
- Data Science @ Facebook https://research.fb.com/category/data-science/ (RSS)
- Data Science Dojo Blog https://datasciencedojo.com/blog/ (RSS)
- Data Science Insights http://www.datasciencebowl.com/data-science-insights/ (RSS)
- Data Science Tutorials https://codementor.io/data-science/tutorial (RSS)
- Data Science Vademecum http://datasciencevademecum.wordpress.com/ (RSS)
- Data Science Notebook http://uconn.science/ (RSS)
- Dataaspirant http://dataaspirant.com/ (RSS)
- Dataclysm https://theblog.okcupid.com/tagged/data (RSS)
- DataGenetics http://datagenetics.com/blog.html (RSS)
- Dataiku https://blog.dataiku.com/ (RSS)
- DataKind http://www.datakind.org/blog (RSS)
- Datanice https://datanice.wordpress.com/ (RSS)
- Dataquest Blog https://www.dataquest.io/blog/ (RSS)
- DataRobot http://www.datarobot.com/blog/ (RSS)
- Datascienceblog.net https://www.datascienceblog.net (RSS)
- Datascope http://datascopeanalytics.com/blog (RSS)
- DatasFrame http://tomaugspurger.github.io/ (RSS)
- David Mimno http://www.mimno.org/ (RSS)
- David Robinson http://varianceexplained.org/ (RSS)
- Dayne Batten http://daynebatten.com (RSS)
- Deep and Shallow https://deep-and-shallow.com (RSS)
- Deep Learning http://deeplearning.net/blog/ (RSS)
- Deepdish http://deepdish.io/ (RSS)
- Delip Rao http://deliprao.com/ (RSS)
- DENNY'S BLOG https://dennybritz.com/ (RSS)
- Dimensionless https://dimensionless.in/blog/ (RSS)
- Distill http://distill.pub/ (RSS)
- District Data Labs https://www.districtdatalabs.com/blog
- Diving into data https://blog.datadive.net/ (RSS)
- Domino Data Lab's blog http://blog.dominodatalab.com/ (RSS)
- Dr. Randal S. Olson http://www.randalolson.com/blog/ (RSS)
- Drew Conway https://medium.com/@drewconway (RSS)
- Dustin Tran http://dustintran.com/blog/ (RSS)
- Eder Santana https://edersantana.github.io/blog.html (RSS)
- Edwin Chen http://blog.echen.me (RSS)
- EFavDB http://efavdb.com/ (RSS)
- Eigenfoo https://eigenfoo.xyz/ (RSS)
- Ethan Rosenthalh https://www.ethanrosenthal.com/#blog (RSS)
- Emilio Ferrara, Ph.D. http://www.emilio.ferrara.name/ (RSS)
- Entrepreneurial Geekiness http://ianozsvald.com/ (RSS)
- Eric Jonas http://ericjonas.com/archives.html (RSS)
- Eric Siegel http://www.predictiveanalyticsworld.com/blog (RSS)
- Erik Bern http://erikbern.com (RSS)
- ERIN SHELLMAN http://www.erinshellman.com/ (RSS)
- Eugenio Culurciello http://culurciello.github.io/ (RSS)
- Fabian Pedregosa http://fa.bianp.net/ (RSS)
- Fast Forward Labs https://blog.fastforwardlabs.com/ (RSS)
- Florian Hartl http://florianhartl.com/ (RSS)
- FlowingData http://flowingdata.com/ (RSS)
- Full Stack ML http://fullstackml.com/ (RSS)
- GAB41 http://www.lab41.org/gab41/ (RSS)
- Garbled Notes http://www.chioka.in/ (RSS)
- Grate News Everyone http://gratenewseveryone.wordpress.com/ (RSS)
- Greg Reda http://www.gregreda.com/blog/ (RSS)
- i am trask http://iamtrask.github.io/ (RSS)
- I Quant NY http://iquantny.tumblr.com/ (RSS)
- inFERENCe http://www.inference.vc/ (RSS)
- Insight Data Science https://blog.insightdatascience.com/ (RSS)
- INSPIRATION INFORMATION http://myinspirationinformation.com/ (RSS)
- Ira Korshunova http://irakorshunova.github.io/ (RSS)
- I’m a bandit https://blogs.princeton.edu/imabandit/ (RSS)
- Java Machine Learning and DeepLearning http://ramok.tech/machine-learning/ (RSS)
- Jason Toy http://www.jtoy.net/ (RSS)
- jbencook https://jbencook.com/ (RSS)
- Jeremy D. Jackson, PhD http://www.jeremydjacksonphd.com/ (RSS)
- Jesse Steinweg-Woods https://jessesw.com/ (RSS)
- John Myles White http://www.johnmyleswhite.com/ (RSS)
- Jonas Degrave http://317070.github.io/ (RSS)
- Jovian https://blog.jovian.ai/ (RSS)
- Joy Of Data http://www.joyofdata.de/blog/ (RSS)
- Julia Evans http://jvns.ca/ (RSS)
- jWork.ORG. https://jwork.org/ (RSS)
- Kavita Ganesan's NLP and Text Mining Blog http://kavita-ganesan.com/ (RSS)
- KDnuggets http://www.kdnuggets.com/ (RSS)
- Keeping Up With The Latest Techniques http://colinpriest.com/ (RSS)
- Kenny Bastani http://www.kennybastani.com/ (RSS)
- Kevin Davenport https://kldavenport.com/ (RSS)
- kevin frans http://kvfrans.com/ (RSS)
- korbonits | Math ∩ Data http://korbonits.github.io/ (RSS)
- Large Scale Machine Learning http://bickson.blogspot.com/ (RSS)
- LATERAL BLOG https://blog.lateral.io/ (RSS)
- Lazy Programmer http://lazyprogrammer.me/ (RSS)
- Learn Analytics Here https://learnanalyticshere.wordpress.com/ (RSS)
- LearnDataSci http://www.learndatasci.com/ (RSS)
- Learning With Data https://learningwithdata.com/ (RSS)
- Life, Language, Learning http://daoudclarke.github.io/ (RSS)
- Locke Data https://itsalocke.com/blog/ (RSS)
- Loic Tetrel https://ltetrel.github.io/ (RSS)
- Louis Dorard http://www.louisdorard.com/blog/ (RSS)
- M.E.Driscoll http://medriscoll.com/ (RSS)
- Machine Learning (Theory) http://hunch.net/ (RSS)
- Machine Learning and Data Science http://alexhwoods.com/blog/ (RSS)
- Machine Learning https://charlesmartin14.wordpress.com/ (RSS)
- Machine Learning Mastery http://machinelearningmastery.com/blog/ (RSS)
- Machine Learning Blogs https://machinelearningblogs.com/ (RSS)
- Machine Learning, etc http://yaroslavvb.blogspot.com (RSS)
- Machine Learning, Maths and Physics https://mlopezm.wordpress.com/ (RSS)
- Machined Learnings http://www.machinedlearnings.com/ (RSS)
- MAPPING BABEL https://jack-clark.net/ (RSS)
- MAPR Blog https://mapr.com/blog/
- MAREK REI http://www.marekrei.com/blog/ (RSS)
- Mark White https://www.markhw.com/blog (RSS)
- MARGINALLY INTERESTING http://blog.mikiobraun.de/ (RSS)
- Math ∩ Programming http://jeremykun.com/ (RSS)
- Matthew Rocklin http://matthewrocklin.com/blog/ (RSS)
- Mic Farris http://www.micfarris.com/ (RSS)
- Mike Tyka http://mtyka.github.io/ (RSS)
- Mirror Image https://mirror2image.wordpress.com/ (RSS)
- Mitch Crowe http://www.mitchcrowe.com/ (RSS)
- MLWave http://mlwave.com/ (RSS)
- MLWhiz http://mlwhiz.com/ (RSS)
- Models are illuminating and wrong https://peadarcoyle.wordpress.com/ (RSS)
- Moody Rd http://blog.mrtz.org/ (RSS)
- Moonshots http://jxieeducation.com/ (RSS)
- Mourad Mourafiq http://mourafiq.com/ (RSS)
- Natural language processing blog http://nlpers.blogspot.fr/ (RSS)
- Neil Lawrence http://inverseprobability.com/blog.html (RSS)
- Neptune Blog: in-depth articles for machine learning practitioners https://neptune.ai/blog (RSS)
- Nikolai Janakiev https://janakiev.com/ (RSS)
- NLP and Deep Learning enthusiast http://camron.xyz/ (RSS)
- no free hunch http://blog.kaggle.com/ (RSS)
- Nuit Blanche http://nuit-blanche.blogspot.com/ (RSS)
- Number 2147483647 https://no2147483647.wordpress.com/ (RSS)
- On Machine Intelligence https://aimatters.wordpress.com/ (RSS)
- Opiate for the masses Data is our religion. http://opiateforthemass.es/ (RSS)
- p-value.info http://www.p-value.info/ (RSS)
- Pete Warden's blog http://petewarden.com/ (RSS)
- Peter Laurinec - Time series data mining in R https://petolau.github.io/ (RSS)
- Plotly Blog http://blog.plot.ly/ (RSS)
- Probably Overthinking It http://allendowney.blogspot.ca/ (RSS)
- Prooffreader.com http://www.prooffreader.com (RSS)
- ProoffreaderPlus http://prooffreaderplus.blogspot.ca/ (RSS)
- Publishable Stuff http://www.sumsar.net/ (RSS)
- PyImageSearch http://www.pyimagesearch.com/ (RSS)
- Pythonic Perambulations https://jakevdp.github.io/ (RSS)
- quintuitive http://quintuitive.com/ (RSS)
- R and Data Mining https://rdatamining.wordpress.com/ (RSS)
- R-bloggers http://www.r-bloggers.com/ (RSS)
- R2RT http://r2rt.com/ (RSS)
- Ramiro Gómez http://ramiro.org/notebooks/ (RSS)
- Randy Zwitch http://randyzwitch.com/ (RSS)
- RaRe Technologies http://rare-technologies.com/blog/ (RSS)
- Reinforcement Learning For Fun https://reinforcementlearning4.fun (RSS)
- Revolutions http://blog.revolutionanalytics.com/ (RSS)
- Rinu Boney http://rinuboney.github.io/ (RSS)
- RNDuja Blog http://rnduja.github.io/ (RSS)
- Robert Chang https://medium.com/@rchang (RSS)
- Rocket-Powered Data Science http://rocketdatascience.org (RSS)
- Sachin Joglekar's blog https://codesachin.wordpress.com/ (RSS)
- samim https://medium.com/@samim (RSS)
- Sebastian Raschka http://sebastianraschka.com/blog/index.html (RSS)
- Sebastian Ruder http://sebastianruder.com/ (RSS)
- Sebastian's slow blog http://www.nowozin.net/sebastian/blog/ (RSS)
- Self Learn Data Science https://selflearndatascience.com (RSS)
- Shakir's Machine Learning Blog http://blog.shakirm.com/ (RSS)
- Simply Statistics http://simplystatistics.org (RSS)
- Springboard Blog http://springboard.com/blog
- Startup.ML Blog http://startup.ml/blog (RSS)
- Stats and R https://www.statsandr.com/blog/ (RSS)
- Statistical Modeling, Causal Inference, and Social Science http://andrewgelman.com/ (RSS)
- Stigler Diet http://stiglerdiet.com/ (RSS)
- Stitch Fix Tech Blog http://multithreaded.stitchfix.com/blog/ (RSS)
- Stochastic R&D Notes http://arseny.info/ (RSS)
- Storytelling with Statistics on Quora http://datastories.quora.com/
- StreamHacker http://streamhacker.com/ (RSS)
- Subconscious Musings http://blogs.sas.com/content/subconsciousmusings/ (RSS)
- Swan Intelligence http://swanintelligence.com/ (RSS)
- TechnoCalifornia http://technocalifornia.blogspot.se/ (RSS)
- TEXT ANALYSIS BLOG | AYLIEN http://blog.aylien.com/ (RSS)
- The Angry Statistician http://angrystatistician.blogspot.com/ (RSS)
- The Clever Machine https://theclevermachine.wordpress.com/ (RSS)
- The Data Camp Blog https://www.datacamp.com/community/blog (RSS)
- The Data Incubator http://blog.thedataincubator.com/ (RSS)
- The Data Science Lab https://datasciencelab.wordpress.com/ (RSS)
- The Data Science Swiss Army Knife https://www.kamwithk.com/ (RSS)
- THE ETZ-FILES http://alexanderetz.com/ (RSS)
- The Science of Data http://www.martingoodson.com (RSS)
- The Shape of Data https://shapeofdata.wordpress.com (RSS)
- The unofficial Google data science Blog http://www.unofficialgoogledatascience.com/ (RSS)
- Tim Dettmers http://timdettmers.com/ (RSS)
- Tombone's Computer Vision Blog http://www.computervisionblog.com/ (RSS)
- Tommy Blanchard http://tommyblanchard.com/category/projects (RSS)
- Towards Data Science https://towardsdatascience.com/ (RSS)
- Trevor Stephens http://trevorstephens.com/ (RSS)
- Trey Causey http://treycausey.com/ (RSS)
- UW Data Science Blog http://datasciencedegree.wisconsin.edu/blog/ (RSS)
- Victor Zhou https://victorzhou.com (RSS)
- Wellecks http://wellecks.wordpress.com/ (RSS)
- Wes McKinney http://wesmckinney.com/archives.html (RSS)
- While My MCMC Gently Samples http://twiecki.github.io/ (RSS)
- WildML http://www.wildml.com/ (RSS)
- Will do stuff for stuff http://rinzewind.org/blog-en (RSS)
- Will wolf http://willwolf.io/ (RSS)
- WILL'S NOISE http://www.willmcginnis.com/ (RSS)
- William Lyon http://www.lyonwj.com/ (RSS)
- Win-Vector Blog http://www.win-vector.com/blog/ (RSS)
- Yanir Seroussi http://yanirseroussi.com/ (RSS)
- Zac Stewart http://zacstewart.com/ (RSS)
Credit: Data Science Blogs
You can import an opml file to your favorite RSS reader.
Also you can add a feed where the list is always up to date.
Your contributions are always welcome!
- R Cookbook
- R Blogdown
- ggplot2
- Headley Wickham
- Advance R
- R Package Documentation
- Parallel Processing in R
- Geo Computation with R
- Learn Python Org
- Python Graph Gallery
- Collection of Jupyter Notebooks
- Streamlit library for ML visuals
- Python Machine learning Notebooks
- Automate Stuff with Python
- Python from NSA
- Awesome Python
- Comprehensice python cheatsheet
- Real Python
- Function Decorators
- Data Science Central
- Towards Data Science
- Analytics Vidhya
- Data Science 101
- Data Science News
- Data Science Plus
- Listen Data
- Data Science Specialization Course Notes
- Various Data Science Tutorials
- Probabilistic Programming & Bayesian Methods for Hackers
- Unofficial Google Data Science Blog
- Data Science Cheat Sheet
- Flowing Data
- Seaborn pair plots
- D3 js examples
- D3 js examples newer version
- Data Visualization Society
- A Comprehensive guide to data exploration
- Dash
- Google AI Blog
- kdnuggets
- Kaggle
- Math Works
- In depth introduction to machine learning - Hastie & Tibshirani
- UC Business Analytics R programming guide
- Machine Learning from CMU
- ML Cheatsheet - Stanford CS229
- Learning from Data
- The Learning Machine
- Machine Learning Plus
- Machine Learning Resources from Sebastian Raschka
- Machine Learning Notebooks
- Machine Learning for beginners
- Curated Machine Learning Resources
- Machine Learning Toolbox
- Rules of Machine Learning: Best Practices for ML Engineering from Google
- Machine Learning Crash Course
- Machine Learning Interviews
- Applied ML - Curated list of papers, articles, and blogs on data science & machine learning in production
- Best of Machine Learning - Python
- Machine Learning Glossary
- Awesome Machine Learning
- Explanable AI
- Fairness and Machine Learning
- Google Reseatch 2021: Themes and beyond
- Machine Learning Complete - Notebooks & demos
- Awesome AI: A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers
- Seeing Theory
- Applied Modern Statistical Learning Techniques
- Probability Theory & Mathematical Statistics
- Probability Distributions Overview
- Applied Data Mining and Statistical Learning (PSU)
- Intro to Statistics - Distributions, Power, Sample size, Effective trial design and mixed effect models
- Statistics How To
- Probability Distributions in R
- Mathematical Challenges
- Statistics Basics & Inference
- Deep Learning Papers and read
- Convolutional Neural Network
- Convolutional Neural Network for Visual Recognition
- A simple introduction of ANN
- How backpropagation works
- UFLDL DeepLearning Tutorials
- Classification Results using Deep Learing
- VGGNet Architecture on Imagenet
- Deep Learning Book
- Andrej Karpathy
- Dive into Deep Learning
- Deep Learning Examples in PyTorch by Nvidia
- Deep Learning Examples in TensorFlow by Nvidia
- Curve Detectors
- Deep Learning Drizzle
- Full Stack Deep Learning - training machine learning models to deploying AI systems in the real world
- Practical Deep Learning by Fasi.ai
- Transformers from Scratch
- Forecasting Principles and Practice
- How To Identify Patterns in Time Series Data
- Applied Time Series Characteristics
- CausalImpact using Baysian structure time series
- Time Series Notes (Oregon State University)
- Extracting Seasonality and Trend from Data: Decomposition using R
- Text Processing - Steps, Tools & Examples
- Document Classification: 7 pragmatic approaches for small datasets
- Collection of Colab notebook based on deep learning & transformer models
- NLP on Spark
- NLP Index
- Regression (Glm)
- Forecasting using Time Series
- Types of Regressions
- Practice Algorithms
- Hidden Markov Models
- HMM Example: Dishonest Casino
- Hidden Markov Model Notes
- Kernals Trick(SVM)
- Boosting
- Chris Albon
- DS Lore
- Zack Stewart
- David Robinson
- Simply Statistics
- Citizen Statistics
- Civil Statistian
- R Studio Blog
- Data Science Plus
- R Weekly Org
- Andrew Gelman
- Edwin Chen's Blog
- R Statistcis co
- Datacamp Community News
- Data Science and Robots - Brandon Rohrer
- Lavanya.ai
- Data Flair
- Fast.ai Blog
- Domino Blog for Code, ML and Data Science
- Data36
- AI Show
- Distill.pub
- Jay Alammar - Blog on NLP and Deep Learning
- Open AI Blog
- Netflix Tech Blog for Data Science
- Google AI Blog
- AirBnb Engineering & Data Science
- Facebook Research
- The Yhat Blog
- Uber Engineering
- CS 229 ― Machine Learning
- Stat202 - Data Mining and analysis
- Columbia University Applied Machine Learning by Andreas Muller
- Fig Share
- Quandl
- Quora
- Public Data Sources
- US Gov
- Our World Data
- UCI Machine Learning Repository
- KDNuggets datasets
- Jerry Smith - Data Science Insights
- Data Quest
- Amazon Product Data
- Sentiment Analysis Datasets
- Machine Learning A-Z: Download Practice Datasets
- Microsoft Research Open Data
- Data Hub
- Collection of NLP datasets
- John Snow Labs NLP & Healthcare datasets
- Open Source Audio datasets
- Green Tea Press
- Machine learning and Data Science Books
- Time Series Analysis using R
- Free programming ebooks
- Machine Learning
- 65 Free machine learnign and data books
- Free Programming and ML pdf books
- Approaching any machine learning problem
- Machine Learning Cheat Sheet in R
- Which algorithn should one use?
- Papers with code
- Browse State of the art
- Data Science Projects
- Churn Prediction & Survival Analysis
- Stanford Machine Learning Projects
- Amazon Science Reasearch and blog
- Machine Learning Questions
- Graph database for beginners
- Top Github Repos
- Survival Regression with Sci-kit learn
- Evaluating Survival Regression
- Jupyter Notebook by Domain
- Jupyter Notebooks - DS,ML,TF,AWS,Python
- Data Science Interview Questions - Springboard
- Data Science Interviews by Category
- 120 Data Science Interview Questions
- Facebook Interview Prep
- Software/ML Engineer Interview Prep
- Tech Interview Handbook
- DS Interview Questions-Answers
- Interview Query
- Geeks for Geeks
- Program Creek
- Career Cup
- A Gentle Introduction to Algorithm Complexity Analysis
- Always be Coding
- Competitive Programming Tutorials
- Python for Algorithms & Data Structure - Interview
- Skilled.dev
- Big O Cheatsheet
- The Algorithms Repo
- Interview Cake (Glossary)
- Algorithm & Coding Interviews
- SDE Skills
- Tech Interview Handbook
- Git Explorer
- Interactive git tutorial for beginners
- How to Write a Git Commit Message
- Awesome Git
- Git Cheatsheet
- A Tour of 10 Useful Github Features
- Automate your data science project structure in three easy steps
- Building a compelling Data Science Portfolio with writing
- My favorite tools for managing, organizing, and reading research papers
- Don’t just take notes — turn them into articles and share them with others-An interview with Alexey Grigorev, author of the book- Machine Learning Bookcamp
- You do not become better by employing fancy techniques but by working on the fundamentals
- Publishing Is Powerful as It Serves as a Catalyst for Scope and Writing Decisions
- Increasing the amount and diversity of data using scikit-image in Python
- Creating custom image datasets for Deep Learning projects
- Vegetation Index calculation from Satellite Imagery
- Face Detection with Python using OpenCV
- Visualizing Decision Trees with Pybaobabdt
- Render Interactive plots with Matplotlib
- Increase the cuteness quotient of your charts
- Create GitHub’s style contributions plot for your Time Series data
- A better way to visualize Decision Trees with the dtreeviz library
- Get Interactive plots directly with pandas
- Cluster Analysis in Tableau
- Quadrant Analysis in Tableau
- Visualizing large datasets with H2O
- 10 Free tools to get started with Data Visualisation-Easily & Instantly
- 5 ‘More’ Open Source tools to get started with Data Visualisation, easily
- Advanced plots in Matplotlib - Part 1
- Advanced plots in Matplotlib — Part 2
- Recreating Gapminder in Tableau: A Humble tribute to Hans Rosling
- Overcoming ImageNet dataset biases with PASS
- What you see is what you’ll get: Twitter’s new strategy for displaying Images on the timeline
- My favorite tools for managing, organizing, and reading research papers
- H2O AI Hybrid Cloud: Democratizing AI for every person and every organization
- Automate your Model Documentation using H2O AutoDoc
- A Deep dive into H2O’s AutoML
- The curious case of Simpson’s Paradox
- Reducing memory usage in pandas with smaller datatypes
- 5 Real World datasets for honing your Exploratory Data Analysis skills
- Getting started with Time Series using Pandas
- Awesome JupyterLab Extensions
- Import HTML tables into Google Sheets effortlessly
- Getting Datasets for Data Analysis tasks - Useful sites for finding datasets
- Getting Datasets for Data Analysis tasks — Advanced Google Search
- 10 Simple hacks to speed up your Data Analysis in Python
- Explain Your Machine Learning Model Predictions with GPU-Accelerated SHAP
- Interpretable or Accurate? Why not both?
- Shapley summary plots: the latest addition to the H2O.ai’s Explainability arsenal
- Interpretable Machine Learning
- From the game of Go to Kaggle: The story of a Kaggle Grandmaster from Taiwan
- What does it take to win a Kaggle competition? Let’s hear it from the winner himself
- What it takes to become a World No 1 on Kaggle
- Meet the Data Scientist who just cannot stop winning on Kaggle
- The inspiring journey of the ‘Beluga’ of Kaggle World 🐋
- Learning from others is imperative to success on Kaggle says this Turkish GrandMaster
- Getting ‘More’ out of your Kaggle Notebooks
- How a passion for numbers turned this Mechanical Engineer into a Kaggle Grandmaster
- Geek Girls Rising: Myth or Reality
- Meet Yauhen: The first and the only Kaggle Grandmaster from Belarus
- The Data Scientist who rules the ‘Data Science for Good’ competitions on Kaggle
- From Academia to Kaggle: How a Physicist found love in Data Science
- A Data Scientist’s journey from Sudoku to Kaggle
- From clipboard to DataFrame with Pandas
- Get Interactive plots directly with Pandas
- There is more to ‘pandas.read_csv()’ than meets the eye
- A hands-on guide to ‘sorting’ dataframes in Pandas
- Reducing memory usage in pandas with smaller datatypes
- Loading large datasets in Pandas
- Extracting information from XML files into a Pandas dataframe
- PandasGUI: Analyzing Pandas dataframes with a Graphical User Interface
- Beware of the Dummy variable trap in pandas
- Pandas Plot: Deep Dive Into Plotting Directly with Pandas
- Five wonderful uses of ‘f- Strings’ in Python
- Use Colab more efficiently with these hacks
- Enabling notifications in your Jupyter notebooks for cell completion
- Using Python’s datatable library seamlessly on Kaggle
- Basics of BASH for Beginners
- Useful pip commands for Data Science
- Getting more value from the Pandas’ value_counts()
- Speed up your Data Analysis with Python’s Datatable package
- Useful String Methods in Python
- Elements of Functional Programming in Python
- An Overview of Python’s Datatable package
- Python’s Collections Module — High-performance container data types
- Reviewing the TensorFlow Decision Forests library
- Tensors are all you need
- Five Open-Source Machine learning libraries worth checking out
- Understanding Decision Trees
- Alternative Python libraries for Data Science
- Demystifying Neural Networks: A Mathematical Approach (Part 1)
- Demystifying Neural Networks: A Mathematical Approach (Part 2)
- Analysis of Emotion Data: A Dataset for Emotion Recognition Tasks
- Building a Simple Chatbot from Scratch in Python (using NLTK)
- Simplifying Sentiment Analysis using VADER in Python (on Social Media Text)
- Free hands-on tutorials to get started in Natural Language Processing