MustafaMarwat

MUSED-1

A comprehensive sEMG dataset recorded at Mayo Hospital Lahore and National University of Sciences & Technology. It includes raw signals from healthy subjects and stroke patients performing six upper limb gestures, captured with Myo armband following rigorous ethical standards.

Amazon-Food-Review-Sentiment-Analysis-ML

This repository hosts Python code for a sentiment analysis of Amazon customer reviews. The project assesses the impact of various features on classifier performance and classifies reviews as positive or negative based on adjusted ratings. Explore and experiment with the classifiers to optimize results and use Matplotlib for visualization.

Certificates-in-AI-ML-DL-and-RL

Coursera & EDx Specialization in Machine Learning, Deep Learning & Reinforcement Learning

ConvNetBuilder

This notebook is an exciting journey into the world of Convolutional Neural Networks (CNNs). In this repository, you'll find comprehensive code for building CNNs from scratch. I have covered the entire process, starting with the fundamentals of convolution and pooling layers and moving all the way to implementing the forward and backward passes.

Deep-Q-Learning-LunarLander

Reinforcement Learning project dedicated to DeepLearning based RL algorithms and techniques for training rover in simulated environment. (LUNAR).

DeepNetBuilder

This repository hosts a comprehensive project for building deep neural networks from the scratch. Dive into the world of deep learning by exploring step-by-step implementations of neural networks, from basic two-layer models to advanced architectures, all coded from scratch

mustafamarwat

ResNets

This repository provides a clean and well-documented implementation of residual neural network. Explore the power of Residual Networks (ResNets) through this project. Implement and analyze the performance of ResNets on a specific task using a Jupyter Notebook.

Transfer-Learning

This project implements a deep learning-based image classifier to distinguish between alpaca and non-alpaca images. The classifier uses transfer learning with MobileNetV2, a pre-trained Convolutional Neural Network (CNN) trained on the ImageNet dataset. The goal is to create an efficient and accurate model for alpaca recognition.