[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-1976":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":22,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":29,"readmeContent":30,"aiSummary":31,"trendingCount":16,"starSnapshotCount":16,"syncStatus":32,"lastSyncTime":33,"discoverSource":34},1976,"pytorch-tutorial","yunjey\u002Fpytorch-tutorial","yunjey","PyTorch Tutorial for Deep Learning Researchers","",null,"Python",32372,8246,620,66,0,11,55,6,81,"MIT License",false,"master",true,[26,27,28,5],"deep-learning","neural-networks","pytorch","2026-06-12 04:00:12","\u003Cp align=\"center\">\u003Cimg width=\"40%\" src=\"logo\u002Fpytorch_logo_2018.svg\" \u002F>\u003C\u002Fp>\n\n--------------------------------------------------------------------------------\n\nThis repository provides tutorial code for deep learning researchers to learn [PyTorch](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fpytorch). In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish [Official Pytorch Tutorial](http:\u002F\u002Fpytorch.org\u002Ftutorials\u002Fbeginner\u002Fdeep_learning_60min_blitz.html).\n\n\n\u003Cbr\u002F>\n\n## Table of Contents\n\n#### 1. Basics\n* [PyTorch Basics](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial\u002Ftree\u002Fmaster\u002Ftutorials\u002F01-basics\u002Fpytorch_basics\u002Fmain.py)\n* [Linear Regression](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial\u002Ftree\u002Fmaster\u002Ftutorials\u002F01-basics\u002Flinear_regression\u002Fmain.py#L22-L23)\n* [Logistic Regression](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial\u002Ftree\u002Fmaster\u002Ftutorials\u002F01-basics\u002Flogistic_regression\u002Fmain.py#L33-L34)\n* [Feedforward Neural Network](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial\u002Ftree\u002Fmaster\u002Ftutorials\u002F01-basics\u002Ffeedforward_neural_network\u002Fmain.py#L37-L49)\n\n#### 2. Intermediate\n* [Convolutional Neural Network](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial\u002Ftree\u002Fmaster\u002Ftutorials\u002F02-intermediate\u002Fconvolutional_neural_network\u002Fmain.py#L35-L56)\n* [Deep Residual Network](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial\u002Ftree\u002Fmaster\u002Ftutorials\u002F02-intermediate\u002Fdeep_residual_network\u002Fmain.py#L76-L113)\n* [Recurrent Neural Network](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial\u002Ftree\u002Fmaster\u002Ftutorials\u002F02-intermediate\u002Frecurrent_neural_network\u002Fmain.py#L39-L58)\n* [Bidirectional Recurrent Neural Network](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial\u002Ftree\u002Fmaster\u002Ftutorials\u002F02-intermediate\u002Fbidirectional_recurrent_neural_network\u002Fmain.py#L39-L58)\n* [Language Model (RNN-LM)](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial\u002Ftree\u002Fmaster\u002Ftutorials\u002F02-intermediate\u002Flanguage_model\u002Fmain.py#L30-L50)\n\n#### 3. Advanced\n* [Generative Adversarial Networks](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial\u002Fblob\u002Fmaster\u002Ftutorials\u002F03-advanced\u002Fgenerative_adversarial_network\u002Fmain.py#L41-L57)\n* [Variational Auto-Encoder](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial\u002Fblob\u002Fmaster\u002Ftutorials\u002F03-advanced\u002Fvariational_autoencoder\u002Fmain.py#L38-L65)\n* [Neural Style Transfer](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial\u002Ftree\u002Fmaster\u002Ftutorials\u002F03-advanced\u002Fneural_style_transfer)\n* [Image Captioning (CNN-RNN)](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial\u002Ftree\u002Fmaster\u002Ftutorials\u002F03-advanced\u002Fimage_captioning)\n\n#### 4. Utilities\n* [TensorBoard in PyTorch](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial\u002Ftree\u002Fmaster\u002Ftutorials\u002F04-utils\u002Ftensorboard)\n\n\n\u003Cbr\u002F>\n\n## Getting Started\n```bash\n$ git clone https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial.git\n$ cd pytorch-tutorial\u002Ftutorials\u002FPATH_TO_PROJECT\n$ python main.py\n```\n\n\u003Cbr\u002F>\n\n## Dependencies\n* [Python 2.7 or 3.5+](https:\u002F\u002Fwww.continuum.io\u002Fdownloads)\n* [PyTorch 0.4.0+](http:\u002F\u002Fpytorch.org\u002F)\n\n\n\n\n","yunjey\u002Fpytorch-tutorial 是一个面向深度学习研究者的 PyTorch 教程项目。该项目通过简洁的代码（大多数模型实现少于30行）帮助用户快速掌握PyTorch框架下的各种神经网络模型构建技巧，包括基础的线性回归、逻辑回归到进阶的卷积神经网络、循环神经网络以及生成对抗网络等。此外，还提供了TensorBoard在PyTorch中的使用教程，增强了模型训练过程中的可视化能力。适合有一定编程基础并对深度学习感兴趣的开发者或研究人员用于自学提升或是作为教学资源。",2,"2026-06-11 02:47:10","top_all"]