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

这是一个使用 PyTorch 构建和训练神经网络的教育课程。它作为深度学习训练指南和资源，提供了一系列关于张量计算和架构开发的结构化课程。 该课程采用交互式学习模型，将学术理论与实践同步。它将理论讲义与练习驱动的 Notebook 相结合，要求学生在预定义的模板中实现模型逻辑，以验证其对概念的理解。 该课程涵盖了广泛的深度学习能力，包括通过梯度下降和正则化进行模型优化，以及卷积、循环和 Transformer 架构的实现。它还包括处理多模态数据以及通过对比学习方法和自动编码器应用自监督学习的说明。 内容通过一系列模块化的 Jupyter Notebook 提供。

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 是一个深度学习教程和指南集合，专注于实现循环神经网络。该项目提供了使用 PyTorch 框架构建序列模型和序列到序列（sequence-to-sequence）架构的实用代码。 该仓库涵盖了用于神经机器翻译、字符级文本生成和文本分类的模型实现。它包括将输入序列转换为输出序列以进行机器翻译和合成新文本的示例。 该项目还扩展到序列数据预测和时间序列分析，提供了基于历史序列模式预测未来事件的方法。

PyTorchZeroToAll 是一个教育资源和教程合集，专注于深度学习和 PyTorch 框架。它为实现神经网络架构提供了一条结构化的学习路径，范围从基础语言语法和原理到复杂的模型设计。 该项目作为一个实现指南，用于构建各种网络类型，包括线性、逻辑、卷积和循环网络。它专门涵盖了通过使用注意力机制和字符级网络进行序列建模的工作流。 该资源还涵盖机器学习数据准备，包括使用自定义数据集加载器、文本处理工具以及将原始源文件转换为张量以用于训练流水线。

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 !