銅靈 發(fā)自 凹非寺

量子位 出品 | 公眾號(hào) QbitAI

暑假即將到來，不用來充電學(xué)習(xí)豈不是虧大了。

有這么一份干貨，匯集了機(jī)器學(xué)習(xí)架構(gòu)和模型的經(jīng)典知識(shí)點(diǎn)，還有各種TensorFlow和PyTorch的Jupyter Notebook筆記資源，地址都在，無(wú)需等待即可取用。

除了取用方便，這份名為Deep Learning Models的資源還尤其全面。

針對(duì)每個(gè)細(xì)分知識(shí)點(diǎn)的介紹還尤其全面的，比如在卷積神經(jīng)網(wǎng)絡(luò)部分，作者就由淺及深分別介紹了AlexNet、VGG、ResNet等。

干貨發(fā)布后，在GitHub短時(shí)間獲得了6000+顆星星，迅速聚集起大量人氣。

圖靈獎(jiǎng)得主、AI大牛Yann LeCun也強(qiáng)烈推薦，夸贊其為一份不錯(cuò)的PyTorch和TensorFlow Jupyter筆記本推薦！

這份資源的作者來頭也不小，他是威斯康星大學(xué)麥迪遜分校的助理教授Sebastian Raschka，此前還編寫過Python Machine Learning一書。

話不多說現(xiàn)在進(jìn)入干貨時(shí)間，好東西太多篇幅較長(zhǎng)，記得先碼后看！

原資源地址：

https://github.com/rasbt/deeplearning-models

干貨來也

1、多層感知機(jī)

多層感知機(jī)簡(jiǎn)稱MLP，是一個(gè)打基礎(chǔ)的知識(shí)點(diǎn)：

多層感知機(jī)：

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部分的多層感知機(jī)：

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

具備批標(biāo)準(zhǔn)化的多層感知機(jī)：

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

從零開始了解多層感知機(jī)與反向傳播：

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、卷積神經(jīng)網(wǎng)絡(luò)

在卷積神經(jīng)網(wǎng)絡(luò)這一部分，細(xì)碎的知識(shí)點(diǎn)很多，包含基礎(chǔ)概念、全卷積網(wǎng)絡(luò)、AlexNet、VGG等多個(gè)內(nèi)容。來看干貨：

卷積神經(jīng)網(wǎng)絡(luò)基礎(chǔ)入門：

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

卷積神經(jīng)網(wǎng)絡(luò)的初始化：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-he-init.ipynb

想用等效卷積層替代全連接的話看看下面這個(gè)：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/fc-to-conv.ipynb

全卷積神經(jīng)網(wǎng)絡(luò)基礎(chǔ)知識(shí)在這里：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-allconv.ipynb

Alexnet網(wǎng)絡(luò)模型在CIFAR-10數(shù)據(jù)集上的實(shí)現(xiàn):

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-alexnet-cifar10.ipynb

關(guān)于VGG模型，你可能需要了解VGG-16架構(gòu)：

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上訓(xùn)練的VGG-16性別分類器：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg16-celeba.ipynb

VGG19網(wǎng)絡(luò)架構(gòu)：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg19.ipynb

關(guān)于2015年被提出的經(jīng)典CNN模型ResNet，最厲害的資源也在這了。

比如ResNet和殘差塊：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/resnet-ex-1.ipynb

用MNIST數(shù)據(jù)集訓(xùn)練的ResNet-18數(shù)字分類器：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet18-mnist.ipynb

用人臉屬性數(shù)據(jù)集CelebA訓(xùn)練的ResNet-18性別分類器：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet18-celeba-dataparallel.ipynb

在MNIST上訓(xùn)練的ResNet-34：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet34-mnist.ipynb

在CelebA上訓(xùn)練ResNet-34性別分類器：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet34-celeba-dataparallel.ipynb

在MNIST上訓(xùn)練的ResNet-50數(shù)字分類器：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet50-mnist.ipynb

在CelebA上訓(xùn)練ResNet-50性別分類器：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet50-celeba-dataparallel.ipynb

在CelebA上訓(xùn)練ResNet-101性別分類器：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet101-celeba.ipynb

在CelebA上訓(xùn)練ResNet-152性別分類器：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet152-celeba.ipynb

CIFAR-10分類器中的網(wǎng)絡(luò)：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/nin-cifar10.ipynb

3、指標(biāo)學(xué)習(xí)

具有多層感知機(jī)的孿生網(wǎng)絡(luò)：

TensorFlow版Jupyter Notebook

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/metric/siamese-1.ipynb

4、自編碼器

在自編碼器這一部分，同樣有很多細(xì)分類別需要學(xué)習(xí)，注意留出充足時(shí)間學(xué)習(xí)這一內(nèi)容。

自編碼器的種類很多，比如全連接自編碼器：

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

還有卷積自編碼器。比如這個(gè)反卷積（轉(zhuǎn)置卷積）卷積自編碼器：

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

沒有進(jìn)行池化的反卷積自編碼器：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-deconv-nopool.ipynb

有最近鄰插值的卷積自編碼器：

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上訓(xùn)練過的有最近鄰插值的卷積自編碼器：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-nneighbor-celeba.ipynb

在谷歌涂鴉數(shù)據(jù)集Quickdraw上訓(xùn)練過的有最近鄰插值的卷積自編碼器：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-nneighbor-quickdraw-1.ipynb

變分自編碼器也是自編碼器中的重要一類：

變分自編碼器基礎(chǔ)介紹：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-var.ipynb

卷積變分自編碼器：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-var.ipynb

最后，還有條件變分自編碼器也需要關(guān)注。比如在重建損失中有標(biāo)簽的：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cvae.ipynb

沒有標(biāo)簽的：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cvae_no-out-concat.ipynb

有標(biāo)簽的條件變分自編碼器：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cnn-cvae.ipynb

沒有標(biāo)簽的條件變分自編碼器：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cnn-cvae_no-out-concat.ipynb

5、生成對(duì)抗網(wǎng)絡(luò)（GAN）

在MNIST上的全連接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上訓(xùn)練的條件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方法優(yōu)化過的條件GAN：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/gan/gan-conv-smoothing.ipynb

6、循環(huán)神經(jīng)網(wǎng)絡(luò)

針對(duì)多對(duì)一的情緒分析和分類問題中，包括簡(jiǎn)單單層RNN：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_simple_imdb.ipynb

壓縮序列的簡(jiǎn)單單層RNN：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_simple_packed_imdb.ipynb

RNN和LSTM技術(shù)：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_lstm_packed_imdb.ipynb

基于GloVe預(yù)訓(xùn)練詞向量的有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

多層雙向RNN：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_gru_packed_imdb.ipynb

一對(duì)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、有序回歸

針對(duì)不同場(chǎng)景，有三類有序回歸干貨：

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

關(guān)于周期性學(xué)習(xí)速率，這里也有一份小技巧：

PyTorch版

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/tricks/cyclical-learning-rate.ipynb

9、PyTorch Workflow和機(jī)制

用自定義數(shù)據(jù)集加載PyTorch，這里也有一些攻略：

比如用CelebA中的人臉圖像：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader-celeba.ipynb

比如用街景數(shù)據(jù)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

在訓(xùn)練和預(yù)處理環(huán)節(jié)，標(biāo)準(zhǔn)化圖像可參考：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-standardized.ipynb

圖像信息樣本：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/torchvision-transform-examples.ipynb

有文本文檔的Char-RNN ：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/char_rnn-charlesdickens.ipynb

在CelebA上訓(xùn)練的VGG-16性別分類器的并行計(jì)算等：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg16-celeba-data-parallel.ipynb

10、TensorFlow Workflow與機(jī)制

這是這份干貨中的最后一個(gè)大分類，包含自定義數(shù)據(jù)集、訓(xùn)練和預(yù)處理兩大部分。

內(nèi)容包括：

將NumPy NPZ用于小批量訓(xùn)練圖像數(shù)據(jù)集

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/image-data-chunking-npz.ipynb

用HDF5文件存儲(chǔ)圖像數(shù)據(jù)集，用于小規(guī)模訓(xùn)練

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/image-data-chunking-hdf5.ipynb

用輸入pipeline從TFRecords文件中讀取數(shù)據(jù)

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/tfrecords.ipynb

TensorFlow數(shù)據(jù)集API

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/dataset-api.ipynb

如果需要從TensorFlow Checkpoint文件和NumPy NPZ Archive中存儲(chǔ)和加載訓(xùn)練模型，可移步：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/saving-and-reloading-models.ipynb

11、傳統(tǒng)機(jī)器學(xué)習(xí)

最后，如果你是從零開始入門，可以從傳統(tǒng)機(jī)器學(xué)習(xí)看起。包括感知機(jī)、邏輯回歸和Softmax回歸等。

感知機(jī)部分TensorFlow版Jupyter Notebook

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/basic-ml/perceptron.ipynb

PyTorch版筆記

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/basic-ml/perceptron.ipynb

邏輯回歸部分也是一樣：

邏輯回歸部分部分TensorFlow版Jupyter Notebooks

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/basic-ml/logistic-regression.ipynb

PyTorch版筆記

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/basic-ml/logistic-regression.ipynb

Softmax回歸，也稱為多項(xiàng)邏輯回歸：

Softmax回歸部分部分TensorFlow版Jupyter Notebook

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/basic-ml/softmax-regression.ipynb

PyTorch版筆記

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/basic-ml/softmax-regression.ipynb

傳送門

這份干貨滿滿的資源到這里就結(jié)束了，再次放上原文傳送門：

https://github.com/rasbt/deeplearning-models

超強(qiáng)干貨，記得收藏~

— 完 —

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