README.md

Machine Learning Tutorials for PyTorch

This is a series of machine learning tutorials I'm writing for implementing deep learning models on your own with the amazing PyTorch library. Basic knowledge of PyTorch and neural networks is assumed. These tutorials are composed of two sections:

Papers

This section is based on research papers. It is designed to enable coding while reading the paper, and executed it in a Colab for both coding and running small tasks. This write-up serves as a review, blending personal thoughts on the paper with key insights.

Category	Paper	Tutorial
SSL	Masked Autoencoders Are Scalable Vision Learners	Masked Autoencoder
VFM	CvT: Introducing Convolutions to Vision Transformers	Convolutional ViT
VFM	Swin Transformer: Hierarchical Vision Transformer using Shifted Windows	Swin Transformer
VFM	An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale	Vision Transformer
Machine Translation	Attention Is All You Need	Transformer
NLP	BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding	BERT

Others

This section entails various tasks implemented in Colab, serving as an introduction to fundamental machine learning concepts.

Category	Tutorial	KeyWord
Object Detection	Faster R-CNN(2)	Faster R-CNN
Object Detection	Faster R-CNN(1)	Faster R-CNN
NLP	Seq2Seq	Machine Translation
NLP	Simple Word Window Classification
Computer Vision	ResNet	Residual Neural Network
Computer Vision	AutoEncoder	AutoEncoder, Denoising AutoEncoder
ML	Recurrent Neural Networks	RNN, LSTM
ML	Simple CNN	Convolutional Neural Network
ML	Fully-Connected Neural Networks
Basic	NLP Basic Tutorial	Natural Language Processing
Basic	Losses	Multi-Class SVM, Softmax, Cross-Entropy
Basic	k-Nearest Neighbors	k-Nearest Neighbors, Cross-Validation
Basic	Pytorch Basic	PyTorch