# Deep-Tutorials-for-PyTorch

**Repository Path**: miracle111/Deep-Tutorials-for-PyTorch

## Basic Information

- **Project Name**: Deep-Tutorials-for-PyTorch
- **Description**: In-depth tutorials for implementing deep learning models on your own with PyTorch.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2020-03-09
- **Last Updated**: 2021-11-03

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Deep Tutorials for [PyTorch](https://pytorch.org)

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](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html) and [Learning PyTorch with Examples](https://pytorch.org/tutorials/beginner/pytorch_with_examples.html).

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**27 Jan 2020**: Working code for two new tutorials has been added — [Super-Resolution](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Super-Resolution) and [Machine Translation](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-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_](https://arxiv.org/abs/1502.03044) | [a PyTorch Tutorial to Image Captioning](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning) | Complete
Sequence Labeling | [_Empower Sequence Labeling with Task-Aware Neural Language Model_](https://arxiv.org/abs/1709.04109) | [a PyTorch Tutorial to Sequence Labeling](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Sequence-Labeling) | Complete
Object Detection | [_SSD: Single Shot MultiBox Detector_](https://arxiv.org/abs/1512.02325) | [a PyTorch Tutorial to Object Detection](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection) | Complete
Text Classification | [_Hierarchical Attention Networks for Document Classification_](https://www.semanticscholar.org/paper/Hierarchical-Attention-Networks-for-Document-Yang-Yang/1967ad3ac8a598adc6929e9e6b9682734f789427) | [a PyTorch Tutorial to Text Classification](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Text-Classification) | Code complete, tutorial in-progress
Super-Resolution | [_Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network_](https://arxiv.org/abs/1609.04802) | [a PyTorch Tutorial to Super-Resolution](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Super-Resolution) | Code complete, tutorial in-progress
Machine Translation | [_Attention Is All You Need_](https://arxiv.org/abs/1706.03762) | [a PyTorch Tutorial to Machine Translation](https://github.com/sgrvinod/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_](https://arxiv.org/abs/1507.05717) | a PyTorch Tutorial to Text Recognition | Planned
Text Summarization | [_Get To The Point: Summarization with Pointer-Generator Networks_](https://arxiv.org/abs/1704.04368) | a PyTorch Tutorial to Text Summarization | Planned
Semantic Segmentation | [_Pyramid Scene Parsing Network_](https://arxiv.org/abs/1612.01105) | a PyTorch Tutorial to Semantic Segmentation | Planned
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