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This is code of book "Learn Deep Learning with PyTorch"
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# 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。**
## 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-learExcerpt of 7,998 characters
Read on GitHub
Sherlockxyliao · China
56
Jerome · National Chiao Tung University · Taiwan
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8599173431827c4f, topic:pytorch, readme:autograd