铜灵 发自 凹非寺
量子位 出品 | 公众号 QbitAI
暑假即将到来，不用来充电学习岂不是亏大了。
有这么一份干货，汇集了机器学习架构和模型的经典知识点，还有各种TensorFlow和PyTorch的Jupyter Notebook笔记资源，地址都在，无需等待即可取用。
除了取用方便，这份名为Deep Learning Models的资源还尤其全面。
针对每个细分知识点的介绍还尤其全面的，比如在卷积神经网络部分，作者就由浅及深分别介绍了AlexNet、VGG、ResNet等。
干货发布后，在GitHub短时间获得了6000+颗星星，迅速聚集起大量人气。
图灵奖得主、AI大牛Yann LeCun也强烈推荐，夸赞其为一份不错的PyTorch和TensorFlow Jupyter笔记本推荐！
这份资源的作者来头也不小，他是威斯康星大学麦迪逊分校的助理教授Sebastian Raschka，此前还编写过Python Machine Learning一书。
话不多说现在进入干货时间，好东西太多篇幅较长，记得先码后看！
原资源地址：
https://github.com/rasbt/deeplearning-models
干货来也
1、多层感知机
多层感知机简称MLP，是一个打基础的知识点：
多层感知机：
TensorFlow版Jupyter Notebook
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mlp/mlp-basic.ipynb
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/mlp-basic.ipynb
增加了Dropout部分的多层感知机：
TensorFlow版Jupyter Notebook
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mlp/mlp-dropout.ipynb
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/mlp-dropout.ipynb
具备批标准化的多层感知机：
TensorFlow版Jupyter Notebook
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mlp/mlp-batchnorm.ipynb
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/mlp-batchnorm.ipynb
从零开始了解多层感知机与反向传播：
TensorFlow版Jupyter Notebook
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mlp/mlp-lowlevel.ipynb
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/mlp-fromscratch__sigmoid-mse.ipynb
2、卷积神经网络
在卷积神经网络这一部分，细碎的知识点很多，包含基础概念、全卷积网络、AlexNet、VGG等多个内容。来看干货：
卷积神经网络基础入门：
TensorFlow版Jupyter Notebook
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/cnn/cnn-basic.ipynb
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-basic.ipynb
卷积神经网络的初始化：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-he-init.ipynb
想用等效卷积层替代全连接的话看看下面这个：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/fc-to-conv.ipynb
全卷积神经网络基础知识在这里：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-allconv.ipynb
Alexnet网络模型在CIFAR-10数据集上的实现:
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-alexnet-cifar10.ipynb
关于VGG模型，你可能需要了解VGG-16架构：
TensorFlow版Jupyter Notebook
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/cnn/cnn-vgg16.ipynb
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg16.ipynb
在CelebA上训练的VGG-16性别分类器：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg16-celeba.ipynb
VGG19网络架构：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg19.ipynb
关于2015年被提出的经典CNN模型ResNet，最厉害的资源也在这了。
比如ResNet和残差块：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/resnet-ex-1.ipynb
用MNIST数据集训练的ResNet-18数字分类器：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet18-mnist.ipynb
用人脸属性数据集CelebA训练的ResNet-18性别分类器：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet18-celeba-dataparallel.ipynb
在MNIST上训练的ResNet-34：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet34-mnist.ipynb
在CelebA上训练ResNet-34性别分类器：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet34-celeba-dataparallel.ipynb
在MNIST上训练的ResNet-50数字分类器：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet50-mnist.ipynb
在CelebA上训练ResNet-50性别分类器：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet50-celeba-dataparallel.ipynb
在CelebA上训练ResNet-101性别分类器：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet101-celeba.ipynb
在CelebA上训练ResNet-152性别分类器：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet152-celeba.ipynb
CIFAR-10分类器中的网络：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/nin-cifar10.ipynb
3、指标学习
具有多层感知机的孪生网络：
TensorFlow版Jupyter Notebook
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/metric/siamese-1.ipynb
4、自编码器
在自编码器这一部分，同样有很多细分类别需要学习，注意留出充足时间学习这一内容。
自编码器的种类很多，比如全连接自编码器：
TensorFlow版Jupyter Notebook
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/autoencoder/ae-basic.ipynb
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-basic.ipynb
还有卷积自编码器。比如这个反卷积（转置卷积）卷积自编码器：
TensorFlow版Jupyter Notebook
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/autoencoder/ae-deconv.ipynb
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-deconv.ipynb
没有进行池化的反卷积自编码器：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-deconv-nopool.ipynb
有最近邻插值的卷积自编码器：
TensorFlow版Jupyter Notebook
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/autoencoder/ae-conv-nneighbor.ipynb
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-nneighbor.ipynb
在CelebA上训练过的有最近邻插值的卷积自编码器：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-nneighbor-celeba.ipynb
在谷歌涂鸦数据集Quickdraw上训练过的有最近邻插值的卷积自编码器：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-nneighbor-quickdraw-1.ipynb
变分自编码器也是自编码器中的重要一类：
变分自编码器基础介绍：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-var.ipynb
卷积变分自编码器：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-var.ipynb
最后，还有条件变分自编码器也需要关注。比如在重建损失中有标签的：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cvae.ipynb
没有标签的：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cvae_no-out-concat.ipynb
有标签的条件变分自编码器：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cnn-cvae.ipynb
没有标签的条件变分自编码器：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cnn-cvae_no-out-concat.ipynb
5、生成对抗网络（GAN）
在MNIST上的全连接GAN：
TensorFlow版Jupyter Notebook
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/gan/gan.ipynb
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/gan/gan.ipynb
在MNIST上训练的条件GAN：
TensorFlow版Jupyter Notebook
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/gan/gan-conv.ipynb
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/gan/gan-conv.ipynb
用Label Smoothing方法优化过的条件GAN：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/gan/gan-conv-smoothing.ipynb
6、循环神经网络
针对多对一的情绪分析和分类问题中，包括简单单层RNN：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_simple_imdb.ipynb
压缩序列的简单单层RNN：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_simple_packed_imdb.ipynb
RNN和LSTM技术：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_lstm_packed_imdb.ipynb
基于GloVe预训练词向量的有LSTM核的RNN：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_lstm_packed_imdb-glove.ipynb
GRU核的RNN：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_gru_packed_imdb.ipynb
多层双向RNN：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_gru_packed_imdb.ipynb
一对多/序列到序列的生成新文本的字符RNN：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/char_rnn-charlesdickens.ipynb
7、有序回归
针对不同场景，有三类有序回归干货：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/ordinal/ordinal-cnn-coral-afadlite.ipynb
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/ordinal/ordinal-cnn-niu-afadlite.ipynb
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/ordinal/ordinal-cnn-niu-afadlite.ipynb
8、方法和技巧
关于周期性学习速率，这里也有一份小技巧：
PyTorch版
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/tricks/cyclical-learning-rate.ipynb
9、PyTorch Workflow和机制
用自定义数据集加载PyTorch，这里也有一些攻略：
比如用CelebA中的人脸图像：
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader-celeba.ipynb
比如用街景数据集：
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader-svhn.ipynb
比如用Quickdraw:
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader-quickdraw.ipynb
在训练和预处理环节，标准化图像可参考：
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-standardized.ipynb
图像信息样本：
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/torchvision-transform-examples.ipynb
有文本文档的Char-RNN ：
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/char_rnn-charlesdickens.ipynb
在CelebA上训练的VGG-16性别分类器的并行计算等：
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg16-celeba-data-parallel.ipynb
10、TensorFlow Workflow与机制
这是这份干货中的最后一个大分类，包含自定义数据集、训练和预处理两大部分。
内容包括：
将NumPy NPZ用于小批量训练图像数据集
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/image-data-chunking-npz.ipynb
用HDF5文件存储图像数据集，用于小规模训练
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/image-data-chunking-hdf5.ipynb
用输入pipeline从TFRecords文件中读取数据
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/tfrecords.ipynb
TensorFlow数据集API
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/dataset-api.ipynb
如果需要从TensorFlow Checkpoint文件和NumPy NPZ Archive中存储和加载训练模型，可移步：
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/saving-and-reloading-models.ipynb
11、传统机器学习
最后，如果你是从零开始入门，可以从传统机器学习看起。包括感知机、逻辑回归和Softmax回归等。
感知机部分TensorFlow版Jupyter Notebook
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/basic-ml/perceptron.ipynb
PyTorch版笔记
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/basic-ml/perceptron.ipynb
逻辑回归部分也是一样：
逻辑回归部分部分TensorFlow版Jupyter Notebooks
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/basic-ml/logistic-regression.ipynb
PyTorch版笔记
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/basic-ml/logistic-regression.ipynb
Softmax回归，也称为多项逻辑回归：
Softmax回归部分部分TensorFlow版Jupyter Notebook
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/basic-ml/softmax-regression.ipynb
PyTorch版笔记
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/basic-ml/softmax-regression.ipynb
传送门
这份干货满满的资源到这里就结束了，再次放上原文传送门：
https://github.com/rasbt/deeplearning-models
超强干货，记得收藏~
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