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

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martinleach/Deep-Tutorials-for-PyTorch

 
 

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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 readDeep Learning with PyTorch: A 60 Minute Blitz andLearning PyTorch with Examples.


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


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

ApplicationPaperTutorialStatus
Image CaptioningShow, Attend, and Tella PyTorch Tutorial to Image CaptioningComplete
Sequence LabelingEmpower Sequence Labeling with Task-Aware Neural Language Modela PyTorch Tutorial to Sequence LabelingComplete
Object DetectionSSD: Single Shot MultiBox Detectora PyTorch Tutorial to Object DetectionComplete
Text ClassificationHierarchical Attention Networks for Document Classificationa PyTorch Tutorial to Text ClassificationCode complete, tutorial in-progress
Super-ResolutionPhoto-Realistic Single Image Super-Resolution Using a Generative Adversarial Networka PyTorch Tutorial to Super-ResolutionCode complete, tutorial in-progress
Machine TranslationAttention Is All You Needa PyTorch Tutorial to Machine TranslationCode complete, tutorial in-progress
Text RecognitionAn End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognitiona PyTorch Tutorial to Text RecognitionPlanned
Text SummarizationGet To The Point: Summarization with Pointer-Generator Networksa PyTorch Tutorial to Text SummarizationPlanned
Semantic SegmentationPyramid Scene Parsing Networka PyTorch Tutorial to Semantic SegmentationPlanned

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