ASAD AYUB 's repositories
-Training-Deep-Neural-Networks-on-a-GPU-with-PyTorch-
This repository explores the MNIST Handwritten Digits and Fashion-MNIST datasets, covering tasks such as working with images in PyTorch, creating models, interpreting outputs, and later, implementing a deep neural network with GPU.
ASADAYUB1
Hi there, it's Asad đź‘‹ (Asad Ayub)
Classes-and-Object-Oriented-Programming-in-Python
This Repository contain Jupyter Files of Classes and Object Oriented Programming in Python, Sudoku Solver in Python and some Challenging Python Programming Exercises Questions
Decision-Tree-and-Random-Forests-with-Hyper-Parameter-Tuning
This repository contains Jupyter files that demonstrate the application of Decision Trees and Random Forests with Scikit Learn in Python. The examples cover two different datasets and include regularization and hyperparameter tuning.
Exploration-of-Datasets-for-ML
Here's in this repository, I have Explore the Walmart Weekly Sales Dataset and Used Cars Prices in the Canadian Market Dataset.
Exploring-CIFAR10-Images-with-Feed-Forward-Convolutional-Neural-Networks-and-ResNets
Explore CIFAR10 dataset through creating a Feed Forward Neural Network, training a CNN from scratch, and implementing advanced techniques like data normalization, augmentation, and ResNets in PyTorch to achieve over 90% accuracy.
Exploring-PyTorch-Fundamentals-Gradient-Descent-and-Intriguing-Tensor-Operations
Explore PyTorch basics, including tensors, gradients, and linear regression, demonstrating practical applications and tensor operations
Flower-image-classification-using-deep-convolutional-neural-network
In this project, I trained a deep learning model for flower recognition on a GPU. I employed a convolutional neural network with residual layers. The model achieved an accuracy of 'val_loss': 0.551, 'val_acc': 0.815, 'train_loss': 0.339.
GAN-and-Transfer-Learning-Models-in-PyTorch
In this repository, there are two Jupyter files. One of them is dedicated to Training Generative Adversarial Networks (GANs) in PyTorch, while the other focuses on Transfer Learning for Image Classification in PyTorch.
Gradient-Boosting-with-XGBoost-and-Unsupervised-Learning
Explored the Rossmann Store Sales dataset using a Gradient-Boosting model with XGBoost, and delved into Unsupervised Learning.
Linear-and-Logistic-Regression-with-Scikit-Learn-
This Repository contains Jupyter Files of Linear Regression and Logistic Regression for Classification with Scikit Learn in Python
Numpy_Pandas_Visualization_with_Matplotlib_Seaborn
Here in this repository there are three Jupyter files which contain codes for analyzing tabular data with Pandas, numerical computing with Numpy and Visualization with Matplotlib and Seaborn.
NYC-Taxi-Fare-Prediction-with-Ridge-Regression-Random-Forests-and-Gradient-Boosting
In this project, I've trained a machine learning model to predict taxi fares for rides in New York City. The model takes into account details such as pickup date and time, pickup and drop-off locations, and the number of passengers.
Programing-with-Python-
This Repository contains code files of Variables, Arithmetic, Conditional, Logical Operators, Data Type, Branching with if,elif and else, Iteration with while and foor loop, and reusable code with Functions in Python. Its also contain Some Practice Questions on all above mention topics in Python.
Project_2_EDA_on_customer-s_records_from_groceries_firm-s_database
In this Project, Exploratory data analysis is done on customer records from groceries firm's database. Here in this work, I use pandas, Numpy, Matplotlib and Seaborn Libraries.
Project_3_EDA_on_Stack_Overflow_Survey_Dataset
Here in this work EDA is perform on the Stack Overflow dataset which contains about 65,000 responses to 61 questions. Numpy, Pandas,Matplotlib and Seaborn libraries of python are used in this work.
Scraping-GitHub-s-Top-Repositories-by-Topics-Using-Python
This repository comprises Jupyter files that demonstrate the implementation of web scraping in Python using Beautiful Soup . It includes essential HTML files and the resulting CSV files, providing comprehensive resources for understanding and practicing web scraping techniques.
Sentimental_Analsysis
Here for this project a small data set is taken and Sentimental Analysis is done on the data. The Objective of the analysis is to first find the fraction of happy and sad tweets. After that on the basis of fractions calculate the overall sentimental score.
Web-Scraping-in-Python-Using-Beautiful-Soup-and-REST-API
This repository comprises Jupyter files that demonstrate the implementation of web scraping in Python using Beautiful Soup and REST API. It includes essential HTML files and the resulting CSV files.