# code-of-learn-deep-learning-with-pytorch **Repository Path**: lg21c/code-of-learn-deep-learning-with-pytorch ## Basic Information - **Project Name**: code-of-learn-deep-learning-with-pytorch - **Description**: This is code of book "Learn Deep Learning with PyTorch" - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 1 - **Created**: 2021-06-18 - **Last Updated**: 2025-02-28 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # 深度学习入门之PyTorch Learn Deep Learning with PyTorch 非常感谢您能够购买此书，这个github repository包含有[深度学习入门之PyTorch](https://item.jd.com/17915495606.html)的实例代码。由于本人水平有限，在写此书的时候参考了一些网上的资料，在这里对他们表示敬意。由于深度学习的技术在飞速的发展，同时PyTorch也在不断更新，且本人在完成此书的时候也有诸多领域没有涉及，所以这个repository会不断更新作为购买次书的一个后续服务，希望我能够在您深度学习的入门道路上提供绵薄之力。 **注意：由于PyTorch版本更迭，书中的代码可能会出现bug，所以一切代码以该github中的为主。** ![image.png](http://upload-images.jianshu.io/upload_images/3623720-7cc3a383f486d157.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240) ## 配置环境 书中已经详细给出了如何基于Anaconda配置python环境，以及PyTorch的安装，如果你使用自己的电脑，并且有Nvidia的显卡，那么你可以愉快地进入深度学习的世界了，如果你没有Nvidia的显卡，那么我们需要一个云计算的平台来帮助我们学习深度学习之旅。[如何配置aws计算平台](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/aws.md) **以下的课程目录和书中目录有出入，因为内容正在更新到第二版，第二版即将上线！！** ## 课程目录 ### part1: 深度学习基础 - Chapter 2: PyTorch基础 - [Tensor和Variable](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter2_PyTorch-Basics/Tensor-and-Variable.ipynb) - [自动求导机制](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter2_PyTorch-Basics/autograd.ipynb) - [动态图与静态图](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter2_PyTorch-Basics/dynamic-graph.ipynb) - Chapter 3: 神经网络 - [线性模型与梯度下降](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter3_NN/linear-regression-gradient-descend.ipynb) - [Logistic 回归与优化器](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter3_NN/logistic-regression/logistic-regression.ipynb) - [多层神经网络，Sequential 和 Module](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter3_NN/nn-sequential-module.ipynb) - [深层神经网络](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter3_NN/deep-nn.ipynb) - [参数初始化方法](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter3_NN/param_initialize.ipynb) - 优化算法 - [SGD](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter3_NN/optimizer/sgd.ipynb) - [动量法](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter3_NN/optimizer/momentum.ipynb) - [Adagrad](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter3_NN/optimizer/adagrad.ipynb) - [RMSProp](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter3_NN/optimizer/rmsprop.ipynb) - [Adadelta](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter3_NN/optimizer/adadelta.ipynb) - [Adam](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter3_NN/optimizer/adam.ipynb) - Chapter 4: 卷积神经网络 - [PyTorch 中的卷积模块](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter4_CNN/basic_conv.ipynb) - [批标准化，batch normalization](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter4_CNN/batch-normalization.ipynb) - [使用重复元素的深度网络，VGG](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter4_CNN/vgg.ipynb) - [更加丰富化结构的网络，GoogLeNet](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter4_CNN/googlenet.ipynb) - [深度残差网络，ResNet](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter4_CNN/resnet.ipynb) - [稠密连接的卷积网络，DenseNet](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter4_CNN/densenet.ipynb) - 更好的训练卷积网络 - [数据增强](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter4_CNN/data-augumentation.ipynb) - [正则化](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter4_CNN/regularization.ipynb) - [学习率衰减](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter4_CNN/lr-decay.ipynb) - Chapter 5: 循环神经网络 - [循环神经网络模块：LSTM 和 GRU](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter5_RNN/pytorch-rnn.ipynb) - [使用 RNN 进行图像分类](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter5_RNN/rnn-for-image.ipynb) - [使用 RNN 进行时间序列分析](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter5_RNN/time-series/lstm-time-series.ipynb) - 自然语言处理的应用： - [Word Embedding](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter5_RNN/nlp/word-embedding.ipynb) - [N-Gram 模型](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter5_RNN/nlp/n-gram.ipynb) - [Seq-LSTM 做词性预测](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter5_RNN/nlp/seq-lstm.ipynb) - Chapter 6: 生成对抗网络 - [自动编码器](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter6_GAN/autoencoder.ipynb) - [变分自动编码器](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter6_GAN/vae.ipynb) - [生成对抗网络](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter6_GAN/gan.ipynb) - 深度卷积对抗网络 (DCGANs) 生成人脸 - Chapter 7: 深度强化学习 - [Q Learning](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter7_RL/q-learning-intro.ipynb) - [Open AI gym](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter7_RL/open_ai_gym.ipynb) - [Deep Q-networks](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter7_RL/dqn.ipynb) - Chapter 8: PyTorch高级 - [tensorboard 可视化](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter8_PyTorch-Advances/tensorboard.ipynb) - [灵活的数据读取介绍](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter8_PyTorch-Advances/data-io.ipynb) - autograd.function 的介绍 - 数据并行和多 GPU - 使用 ONNX 转化为 Caffe2 模型 - 如何部署训练好的神经网络 - 打造属于自己的 PyTorch 的使用习惯 ### part2: 深度学习的应用 - Chapter 9: 计算机视觉 - [Fine-tuning: 通过微调进行迁移学习](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter9_Computer-Vision/fine_tune/) - kaggle初体验:猫狗大战 - [语义分割: 通过 FCN 实现像素级别的分类](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/tree/master/chapter9_Computer-Vision/segmentation) - Pixel to Pixel 生成对抗网络 - Neural Transfer: 通过卷积网络实现风格迁移 - Deep Dream: 探索卷积网络眼中的世界 - Chapter 10: 自然语言处理 - [Char RNN 实现文本生成](https://github.com/SherlockLiao/code-of-learn-deep-learning-with-pytorch/blob/master/chapter10_Natural-Language-Process/char_rnn/) - Image Caption: 实现图片字幕生成 - seq2seq 实现机器翻译 - cnn + rnn + attention 实现文本识别 ## 一些别的资源 关于深度学习的一些公开课程以及学习资源，可以参考我的这个[repository](https://github.com/SherlockLiao/Roadmap-of-DL-and-ML) 可以关注我的[知乎专栏](https://zhuanlan.zhihu.com/c_94953554)和[博客](https://sherlockliao.github.io/)，会经常分享一些深度学习的文章 关于PyTorch的资源 我的github repo [pytorch-beginner](https://github.com/SherlockLiao/pytorch-beginner) [pytorch-tutorial](https://github.com/yunjey/pytorch-tutorial) [the-incredible-pytorch](https://github.com/ritchieng/the-incredible-pytorch) [practical-pytorch](https://github.com/spro/practical-pytorch) [PyTorchZeroToAll](https://github.com/hunkim/PyTorchZeroToAll) [Awesome-pytorch-list](https://github.com/bharathgs/Awesome-pytorch-list) ## Acknowledgement 本书的第二版内容其中一些部分参考了 mxnet gluon 的中文教程，[通过MXNet/Gluon来动手学习深度学习](https://zh.gluon.ai/)。 Gluon 是一个和 PyTorch 非常相似的框架，非常简单、易上手，推荐大家去学习一下，也安利一下 gluon 的中文课程，全中文授课，有视频，有代码练习，可以说是最全面的中文深度学习教程。