/Deep-Tutorials-for-PyTorch

In-depth tutorials for implementing deep learning models on your own with PyTorch.

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.

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