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.

If you're new to PyTorch, first read Deep Learning with PyTorch: A 60 Minute Blitz and Learning PyTorch with Examples.

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

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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