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This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch. Feel free to make a pull request to contribute to this list.

Table Of Contents

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<a name='Tutorials'></a>Tutorials

<a name='LargeLanguageModels'></a>Large Language Models (LLMs)

<a name='TabularData'></a>Tabular Data

<a name='Visualization'></a>Visualization

<a name='Explainability'></a>Explainability

<a name='ObjectDetection'></a>Object Detection

<a name='Long-TailedOut-of-DistributionRecognition'></a>Long-Tailed / Out-of-Distribution Recognition

<a name='ActivationFunctions'></a>Activation Functions

<a name='Energy-BasedLearning'></a>Energy-Based Learning

<a name='MissingData'></a>Missing Data

<a name='ArchitectureSearch'></a>Architecture Search

<a name='ContinualLearning'></a>Continual Learning

<a name='Optimization'></a>Optimization

<a name='Quantization'></a>Quantization

<a name='QuantumMachineLearning'></a>Quantum Machine Learning

<a name='NeuralNetworkCompression'></a>Neural Network Compression

<a name='FacialActionandPoseRecognition'></a>Facial, Action and Pose Recognition

<a name='Superresolution'></a>Super resolution

<a name='SynthetesizingViews'></a>Synthetesizing Views

<a name='Voice'></a>Voice

<a name='Medical'></a>Medical

<a name='DSegmentationClassificationandRegression'></a>3D Segmentation, Classification and Regression

<a name='VideoRecognition'></a>Video Recognition

<a name='RecurrentNeuralNetworksRNNs'></a>Recurrent Neural Networks (RNNs)

<a name='ConvolutionalNeuralNetworksCNNs'></a>Convolutional Neural Networks (CNNs)

<a name='Segmentation'></a>Segmentation

<a name='GeometricDeepLearning:GraphIrregularStructures'></a>Geometric Deep Learning: Graph & Irregular Structures

<a name='Sorting'></a>Sorting

<a name='OrdinaryDifferentialEquationsNetworks'></a>Ordinary Differential Equations Networks

<a name='Multi-taskLearning'></a>Multi-task Learning

<a name='GANsVAEsandAEs'></a>GANs, VAEs, and AEs

<a name='UnsupervisedLearning'></a>Unsupervised Learning

<a name='AdversarialAttacks'></a>Adversarial Attacks

<a name='StyleTransfer'></a>Style Transfer

<a name='ImageCaptioning'></a>Image Captioning

<a name='Transformers'></a>Transformers

<a name='SimilarityNetworksandFunctions'></a>Similarity Networks and Functions

<a name='Reasoning'></a>Reasoning

<a name='GeneralNLP'></a>General NLP

<a name='QuestionandAnswering'></a>Question and Answering

<a name='SpeechGenerationandRecognition'></a>Speech Generation and Recognition

<a name='DocumentandTextClassification'></a>Document and Text Classification

<a name='TextGeneration'></a>Text Generation

<a name='TexttoImage'></a>Text to Image

<a name='Translation'></a>Translation

<a name='SentimentAnalysis'></a>Sentiment Analysis

<a name='DeepReinforcementLearning'></a>Deep Reinforcement Learning

<a name='DeepBayesianLearningandProbabilisticProgrammming'></a>Deep Bayesian Learning and Probabilistic Programmming

<a name='SpikingNeuralNetworks'></a>Spiking Neural Networks

<a name='AnomalyDetection'></a>Anomaly Detection

<a name='RegressionTypes'></a>Regression Types

<a name='TimeSeries'></a>Time Series

<a name='SyntheticDatasets'></a>Synthetic Datasets

<a name='NeuralNetworkGeneralImprovements'></a>Neural Network General Improvements

<a name='DNNApplicationsinChemistryandPhysics'></a>DNN Applications in Chemistry and Physics

<a name='NewThinkingonGeneralNeuralNetworkArchitecture'></a>New Thinking on General Neural Network Architecture

<a name='LinearAlgebra'></a>Linear Algebra

<a name='APIAbstraction'></a>API Abstraction

<a name='LowLevelUtilities'></a>Low Level Utilities

<a name='PyTorchUtilities'></a>PyTorch Utilities

<a name='PyTorchVideoTutorials'></a>PyTorch Video Tutorials

<a name='Community'></a>Community

<a name='TobeClassified'></a>To be Classified

<a name='LinkstoThisRepository'></a>Links to This Repository

<a name='Contributions'></a>Contributions

Do feel free to contribute!

You can raise an issue or submit a pull request, whichever is more convenient for you. The guideline is simple: just follow the format of the previous bullet point or create a new section if it's a new category.