[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-2289":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":24,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":26,"readmeContent":27,"aiSummary":28,"trendingCount":16,"starSnapshotCount":16,"syncStatus":29,"lastSyncTime":30,"discoverSource":31},2289,"examples","pytorch\u002Fexamples","pytorch","A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.","https:\u002F\u002Fpytorch.org\u002Fexamples",null,"Python",23918,9818,392,198,0,6,44,3,45,"BSD 3-Clause \"New\" or \"Revised\" License",false,"main",true,[],"2026-06-12 02:00:39","# PyTorch Examples\n\nhttps:\u002F\u002Fpytorch.org\u002Fexamples\u002F\n\n`pytorch\u002Fexamples` is a repository showcasing examples of using [PyTorch](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fpytorch). The goal is to have curated, short, few\u002Fno dependencies _high quality_ examples that are substantially different from each other that can be emulated in your existing work.\n\n- For tutorials: https:\u002F\u002Fgithub.com\u002Fpytorch\u002Ftutorials\n- For changes to pytorch.org: https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fpytorch.github.io\n- For a general model hub: https:\u002F\u002Fpytorch.org\u002Fhub\u002F or https:\u002F\u002Fhuggingface.co\u002Fmodels\n- For recipes on how to run PyTorch in production: https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Frecipes\n- For general Q&A and support: https:\u002F\u002Fdiscuss.pytorch.org\u002F\n\n## Available models\n\n- [Image classification (MNIST) using Convnets](.\u002Fmnist\u002FREADME.md)\n- [Word-level Language Modeling using RNN and Transformer](.\u002Fword_language_model\u002FREADME.md)\n- [Training Imagenet Classifiers with Popular Networks](.\u002Fimagenet\u002FREADME.md)\n- [Generative Adversarial Networks (DCGAN)](.\u002Fdcgan\u002FREADME.md)\n- [Variational Auto-Encoders](.\u002Fvae\u002FREADME.md)\n- [Superresolution using an efficient sub-pixel convolutional neural network](.\u002Fsuper_resolution\u002FREADME.md)\n- [Hogwild training of shared ConvNets across multiple processes on MNIST](mnist_hogwild)\n- [Training a CartPole to balance with actor-critic](.\u002Freinforcement_learning\u002FREADME.md)\n- [Natural Language Inference (SNLI) with GloVe vectors, LSTMs, and torchtext](snli)\n- [Time sequence prediction - use an LSTM to learn Sine waves](.\u002Ftime_sequence_prediction\u002FREADME.md)\n- [Implement the Neural Style Transfer algorithm on images](.\u002Ffast_neural_style\u002FREADME.md)\n- [Reinforcement Learning with Actor Critic and REINFORCE algorithms on OpenAI gym](.\u002Freinforcement_learning\u002FREADME.md)\n- [PyTorch Module Transformations using fx](.\u002Ffx\u002FREADME.md)\n- Distributed PyTorch examples with [Distributed Data Parallel](.\u002Fdistributed\u002Fddp\u002FREADME.md) and [RPC](.\u002Fdistributed\u002Frpc)\n- [Several examples illustrating the C++ Frontend](cpp)\n- [Image Classification Using Forward-Forward](.\u002Fmnist_forward_forward\u002FREADME.md)\n- [Language Translation using Transformers](.\u002Flanguage_translation\u002FREADME.md)\n\nAdditionally, a list of good examples hosted in their own repositories:\n\n- [Neural Machine Translation using sequence-to-sequence RNN with attention (OpenNMT)](https:\u002F\u002Fgithub.com\u002FOpenNMT\u002FOpenNMT-py)\n\n## Contributing\n\nIf you'd like to contribute your own example or fix a bug please make sure to take a look at [CONTRIBUTING.md](CONTRIBUTING.md).\n","`pytorch\u002Fexamples` 是一个展示如何使用 PyTorch 进行视觉、文本和强化学习等领域开发的示例集合。该项目提供了多种高质量且相对独立的模型实现，包括图像分类、语言建模、生成对抗网络、变分自编码器等，并覆盖了从基础到高级的应用场景。这些例子旨在帮助开发者快速上手并理解 PyTorch 的核心功能与特性，如动态计算图、自动微分以及分布式训练支持。无论是对于初学者学习深度学习基础知识，还是经验丰富的工程师探索特定领域内的先进技术，该仓库都是一个非常有价值的资源。",2,"2026-06-11 02:49:17","top_language"]