Machine Learning Research Paper Implementations

Welcome to the Machine Learning Research Paper Implementations repository! This repository contains my implementations of various machine learning algorithms and models based on research papers. These implementations serve as a practical way to learn and understand the concepts and techniques proposed in the papers.

Feel free to explore the repository and use the code as a reference or starting point for your own projects. Each implementation is organized in its own folder, along with a detailed README that provides information about the paper, the model architecture, dataset used, and instructions on running the code.

Implemented Papers

Paper	Paper
Attention is all you need	Paper
Going Deeper with Convolutions (InceptionNet)	Paper
GradientBased Learning Applied to Document Recognition (LeNet)	Paper
Deep Residual Learning for Image Recognition (ResNet)	Paper
Image Style Transfer Using Convolutional Neural Networks	Paper
Very Deep Convolutional Networks for Large-Scale Image Recognition (VggNet)	Paper
Efficient Estimation of Word Representations in Vector Space (Word2Vec)	Paper
Playing Atari with Deep Reinforcement Learning (DQN)	Paper
MathPrompter: Mathematical Reasoning using Large Language Models	Paper
Improving Language Understanding by Generative Pre-Training (GPT2)	Paper
Fast Transformer Decoding: One Write-Head is All You Need (MQA)	Paper
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness	Paper
YOLOv3: An Incremental Improvement	Paper
U-Net: Convolutional Networks for Biomedical Image Segmentation	Paper