What does jcjohnson/pytorch-examples do?

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.

What are the main features of jcjohnson/pytorch-examples?

The main features of jcjohnson/pytorch-examples are: Learning Examples, Automatic Differentiation, Custom Neural Network Layers, Dynamic Graph Builders, Tensor Computing Libraries, Neural Network Layers, Neural Network Modules, Neural Network Training.

Which projects share features with jcjohnson/pytorch-examples?

Projects with overlapping indexed features include: d2l-ai/d2l-en — This project is an educational platform and research toolkit designed to teach deep learning through a combination of… tingsongyu/pytorch_tutorial — This project is a comprehensive collection of educational examples and reference implementations for building vision… ml-explore/mlx — This project is a machine learning array framework and tensor computation library designed for high-performance… flashlight/flashlight — Flashlight is a standalone C++ machine learning library and tensor library used for building and training neural… morvanzhou/pytorch-tutorial — This project is a collection of PyTorch learning resources and educational guides designed to teach the construction… morvanzhou/tensorflow-tutorial — This project is a collection of educational resources and reference implementations for neural network development…

Pytorch Examples - learn PyTorch with code

Name: jcjohnson/pytorch-examples
Author: jcjohnson

Features

Learning Examples - Provides a curated collection of self-contained PyTorch code examples for learning.
Automatic Differentiation - Computes gradients automatically via computational graph and backpropagation.
Custom Neural Network Layers - Creates custom neural network layers with learnable parameters via subclassing.
Dynamic Graph Builders - Illustrates dynamic computational graphs built on-the-fly with native Python control flow.
Tensor Computing Libraries - Demonstrates performing tensor operations on GPUs for accelerated numerical computation.
Neural Network Layers - Shows how to construct neural networks using pre-built layers like dense and convolutional ones.
Neural Network Modules - Demonstrates creating custom neural network layers by subclassing PyTorch's Module class.
Neural Network Training - Trains neural networks by defining models, computing gradients, and applying optimizers.
Neural Network Optimizers - Provides examples of training neural networks with various optimizers including SGD, Adam, and RMSProp.
Reverse-Mode Differentiation - Implements reverse-mode automatic differentiation for computing gradients via backpropagation.
Stochastic Gradient Descent Optimizations - Applies stochastic gradient descent and Adam optimizers to minimize loss functions.
GPU-Accelerated Processing - Enables GPU-accelerated tensor operations via CUDA backend for parallel computation.
Neural Network Module Abstractions - Provides module-based abstractions for encapsulating neural network layers and parameters.
Eager Execution Modes - Demonstrates eager execution mode for immediate evaluation and debugging.
GPU Tensor Mapping - Demonstrates GPU tensor mapping and computation with code samples.
Custom Autograd Functions - Defines custom autograd functions with user-specified forward and backward operations.
Model Training Optimizers - Illustrates training loops with optimizers like SGD and Adam.
Tutorials - Listed in the “Tutorials” section of the The Incredible Pytorch awesome list.

Projects sharing features with Pytorch Examples

These projects share indexed features with Pytorch Examples. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.

d2l-ai/d2l-en
d2l-ai/d2l-en
30,012View on GitHub
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
Pythonbookcomputer-visiondata-science
View on GitHub30,012
tingsongyu/pytorch_tutorial
TingsongYu/PyTorch_Tutorial
8,018View on GitHub
This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene
Python
View on GitHub8,018
ml-explore/mlx
ml-explore/mlx
29,087View on GitHub
This project is a machine learning array framework and tensor computation library designed for high-performance numerical computing. It provides a comprehensive suite of tools for constructing and training neural networks, featuring an automatic differentiation engine that facilitates gradient-based optimization and complex mathematical modeling. The library distinguishes itself through a unified memory architecture that allows data to be shared across CPU and GPU devices without explicit copies, significantly reducing data movement overhead. Its execution model relies on a lazy evaluation en
C++mlx
View on GitHub29,087
flashlight/flashlight
flashlight/flashlight
5,479View on GitHub
Flashlight is a standalone C++ machine learning library and tensor library used for building and training neural networks. It functions as a comprehensive neural network framework and automatic differentiation engine, providing the tools to construct computation graphs and calculate gradients via backpropagation. The project serves as a distributed training framework, utilizing all-reduce operations to synchronize gradients and parameters across multiple compute nodes and devices. It distinguishes itself through deep integration of high-performance tensor manipulation, native device memory in
C++
View on GitHub5,479

Compare all 30 related projects

Projects sharing features with Pytorch Examples

These projects share indexed features with Pytorch Examples. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.

d2l-ai/d2l-en
d2l-ai/d2l-en
30,012View on GitHub
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
Pythonbookcomputer-visiondata-science
View on GitHub30,012
tingsongyu/pytorch_tutorial
TingsongYu/PyTorch_Tutorial
8,018View on GitHub
This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene
Python
View on GitHub8,018
ml-explore/mlx
ml-explore/mlx
29,087View on GitHub
This project is a machine learning array framework and tensor computation library designed for high-performance numerical computing. It provides a comprehensive suite of tools for constructing and training neural networks, featuring an automatic differentiation engine that facilitates gradient-based optimization and complex mathematical modeling. The library distinguishes itself through a unified memory architecture that allows data to be shared across CPU and GPU devices without explicit copies, significantly reducing data movement overhead. Its execution model relies on a lazy evaluation en
C++mlx
View on GitHub29,087
flashlight/flashlight
flashlight/flashlight
5,479View on GitHub
Flashlight is a standalone C++ machine learning library and tensor library used for building and training neural networks. It functions as a comprehensive neural network framework and automatic differentiation engine, providing the tools to construct computation graphs and calculate gradients via backpropagation. The project serves as a distributed training framework, utilizing all-reduce operations to synchronize gradients and parameters across multiple compute nodes and devices. It distinguishes itself through deep integration of high-performance tensor manipulation, native device memory in
C++
View on GitHub5,479

Compare all 30 related projects

jcjohnsonpytorch-examples