Deep Tutorials for PyTorch

This is a series of in-depth tutorials I'm writing for implementing cool deep learning models on your own with the amazing PyTorch library.

Basic knowledge of PyTorch and neural networks is assumed.

27 Jan 2020: Working code for two new tutorials has been added — Super-Resolution and Machine Translation

In each tutorial, we will focus on a specific application or area of interest by implementing a model from a research paper.

Application	Paper	Tutorial	Status
Image Captioning	Show, Attend, and Tell	a PyTorch Tutorial to Image Captioning	Complete
Sequence Labeling	Empower Sequence Labeling with Task-Aware Neural Language Model	a PyTorch Tutorial to Sequence Labeling	Complete
Object Detection	SSD: Single Shot MultiBox Detector	a PyTorch Tutorial to Object Detection	Complete
Text Classification	Hierarchical Attention Networks for Document Classification	a PyTorch Tutorial to Text Classification	Code complete, tutorial in-progress
Super-Resolution	Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network	a PyTorch Tutorial to Super-Resolution	Code complete, tutorial in-progress
Machine Translation	Attention Is All You Need	a PyTorch Tutorial to Machine Translation	Code complete, tutorial in-progress
Text Recognition	An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition	a PyTorch Tutorial to Text Recognition	Planned
Text Summarization	Get To The Point: Summarization with Pointer-Generator Networks	a PyTorch Tutorial to Text Summarization	Planned
Semantic Segmentation	Pyramid Scene Parsing Network	a PyTorch Tutorial to Semantic Segmentation	Planned

Provide feedback