Awesome GitHub RepositoriesTutorials

This project is a collection of educational examples and code for implementing deep learning architectures using the PyTorch framework. It serves as a tutorial and implementation guide for building various neural network architectures for machine learning tasks. The project provides practical implementations for computer vision, including image classification and neural style transfer, as well as natural language processing examples for building sequence models and language predictors. It also covers generative models using adversarial and variational networks to synthesize or transform visua

This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

This repository serves as a comprehensive collection of reference implementations for the PyTorch machine learning library. It provides practical examples for building, training, and deploying deep learning models, functioning as a toolkit for developers to explore neural network architectures and training workflows. The project distinguishes itself by offering concrete demonstrations of complex machine learning operations, ranging from computer vision tasks like object detection and depth estimation to the training of large-scale transformer models. These examples illustrate how to implement

This repository is an educational collection of deep learning implementations designed to demonstrate the fundamental principles of neural network architecture and optimization. It provides a comprehensive resource for understanding machine learning through hands-on code examples, ranging from basic multilayer perceptrons to complex generative models. The project distinguishes itself by emphasizing the manual construction of models, including the implementation of backpropagation from scratch to illustrate core mathematical mechanics. It covers a wide array of architectural design patterns, s

The PyTorch Tutorials repository is a collection of educational resources that provides step-by-step guidance on building, training, and deploying neural networks using the PyTorch framework. It covers the complete machine learning workflow, from data loading and model definition through optimization loops and model persistence, with dedicated guides for distributed training, model fine-tuning, and deployment. The tutorials offer practical demonstrations of adapting pre-trained models to new tasks through transfer learning, scaling training across multiple GPUs or machines using PyTorch's dis

Il s'agit d'un programme éducatif pour construire et entraîner des réseaux de neurones avec PyTorch. Il sert de guide d'apprentissage pour le deep learning et de ressource, fournissant une série structurée de leçons sur le calcul tensoriel et le développement d'architectures. Le cours utilise un modèle d'apprentissage interactif qui synchronise la théorie académique avec la pratique. Il associe des diapositives de cours théoriques à des notebooks d'exercices, demandant aux étudiants d'implémenter la logique des modèles dans des templates prédéfinis pour valider leur compréhension conceptuelle. Le programme couvre un large éventail de capacités en deep learning, incluant l'optimisation de modèles via la descente de gradient et la régularisation, ainsi que l'implémentation d'architectures convolutionnelles, récurrentes et de type transformer. Il inclut également des instructions pour traiter des données multimodales et appliquer l'apprentissage auto-supervisé via des méthodes contrastives et des auto-encodeurs. Le contenu est délivré via une séquence modulaire de Jupyter Notebooks.

This repository provides a curated collection of self-contained Python code examples that demonstrate the core capabilities of the PyTorch deep learning framework. The examples cover automatic differentiation, dynamic computational graphs, GPU‑accelerated tensor operations, and training of neural network models using gradient‑based optimization. The code samples illustrate PyTorch’s dynamic graph construction, where models can change structure with native control flow, and its automatic gradient computation through reverse‑mode differentiation. Additional examples show how to work with tensor

Practical PyTorch is a collection of deep learning tutorials and guides focused on implementing recurrent neural networks. The project provides practical code for building sequence models and sequence-to-sequence architectures using the PyTorch framework. The repository covers the implementation of models for neural machine translation, character-level text generation, and text classification. It includes examples for transforming input sequences into output sequences for machine translation and synthesizing new text. The project also extends to sequence data prediction and time series analy

PyTorchZeroToAll est une ressource pédagogique et une collection de tutoriels axés sur le deep learning et le framework PyTorch. Il fournit un parcours d'apprentissage structuré pour implémenter des architectures de réseaux de neurones, allant de la syntaxe de base et des fondamentaux jusqu'à la conception de modèles complexes. Le projet sert de guide d'implémentation pour construire divers types de réseaux, incluant les réseaux linéaires, logistiques, convolutifs et récurrents. Il couvre spécifiquement le workflow pour la modélisation de séquences via l'utilisation de mécanismes d'attention et de réseaux au niveau des caractères. La ressource couvre également la préparation des données de machine learning, incluant l'utilisation de chargeurs de jeux de données personnalisés, d'outils de traitement de texte et la traduction de fichiers sources bruts en tenseurs pour les pipelines d'entraînement.

Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)

C++ Implementation of PyTorch Tutorials for Everyone

An IPython Notebook tutorial on deep learning for natural language processing, including structure prediction.

PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

PyTorch tutorials and best practices.

Various tutorials given for welcoming new students at MILA.

A scalable template for PyTorch projects, with examples in Image Segmentation, Object classification, GANs and Reinforcement Learning.

Open source guides/codes for mastering deep learning to deploying deep learning in production in PyTorch, Python, Apptainer, and more.

Quick, visual, principled introduction to pytorch code through five colab notebooks.

Neural information retrieval / semantic search / bi-encoders

Revive your old S60 3rd FP1 Nokia N95 2GB with this guide from 2021 !