deeplearning-with-pytorch

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  • 2022-03-01 22:29
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用pytorch 来实现深度学习,包括卷积神经网络,循环神经网络,cv,nlp等
code-of-learn-deep-learning-with-pytorch.rar
内容介绍
# 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 - 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