这个项目火了！各种深度学习架构，模型和技巧的集合

来自：开源最前线（ID：OpenSourceTop）

打开GitHub Trending，排行第一的项目成功引起了我的注意——deeplearning-models

该项目是Jupyter Notebook中TensorFlow和PyTorch的各种深度学习架构，模型和技巧的集合。

这份集合的内容到底有多丰富呢？一起来看看

传统机器学习

感知器

TensorFlow 1：

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

PyTorch：

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

逻辑回归

TensorFlow 1：

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

PyTorch：

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

Softmax Regression (Multinomial Logistic Regression)

TensorFlow 1：

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

PyTorch：

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

多层感知器

多层感知器

TensorFlow 1：

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

PyTorch：

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

具有Dropout多层感知器

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mlp/mlp-dropout.ipynb

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/mlp-dropout.ipynb

具有批量归一化的多层感知器

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mlp/mlp-batchnorm.ipynb

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/mlp-batchnorm.ipynb

具有反向传播的多层感知器

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mlp/mlp-lowlevel.ipynb

PyTorch：

https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/mlp-fromscratch__sigmoid-mse.ipynb

CNN

基础

CNN

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/cnn/convnet.ipynb

PyTorch：

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

具有He初始化的CNN

PyTorch：

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

概念

用等效卷积层代替完全连接

PyTorch：

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

全卷积：全卷积神经网络

PyTorch：

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

AlexNet：AlexNet on CIFAR-10

PyTorch：

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

VGG：CNN VGG-16

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/cnn/cnn-vgg16.ipynb

PyTorch：

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

VGG-16 Gender Classifier Trained on CelebA

PyTorch：

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

CNN VGG-19

PyTorch：

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

ResNet：ResNet and Residual Blocks

PyTorch：

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

ResNet-18 Digit Classifier Trained on MNIST

PyTorch：

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

ResNet-18 Gender Classifier Trained on CelebA

PyTorch：

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

ResNet-34 Digit Classifier Trained on MNIST

PyTorch：

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

ResNet-34 Gender Classifier Trained on CelebA

PyTorch：

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

ResNet-50 Digit Classifier Trained on MNIST

PyTorch：

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

ResNet-50 Gender Classifier Trained on CelebA

PyTorch：

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

ResNet-101 Gender Classifier Trained on CelebA

PyTorch：

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

ResNet-152 Gender Classifier Trained on CelebA

PyTorch：

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

Network in Network

Network in Network CIFAR-10 Classifier

PyTorch：

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

度量学习：具有多层感知器的孪生网络

TensorFlow 1：

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

自动编码机

全连接自动编码机：自动编码机

TensorFlow 1：

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

PyTorch：

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

具有解卷积/转置卷积的卷积自动编码机

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/autoencoder/ae-deconv.ipynb

PyTorch：

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

具有解卷积的卷积自动编码机（无池化操作）

PyTorch：

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

具有最近邻插值的卷积自动编码机

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/autoencoder/autoencoder-conv-nneighbor.ipynb

PyTorch：

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

具有最近邻插值的卷积自动编码机 - 在CelebA上进行训练

PyTorch：

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

具有最近邻插值的卷积自动编码机 - 在Quickdraw上训练

PyTorch：

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

变分自动编码机

变分自动编码机

PyTorch：

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

卷积变分自动编码机

PyTorch：

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

条件变分自动编码机

条件变分自动编码机（重建丢失中带标签）

PyTorch：

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

条件变分自动编码机（重建损失中没有标签）

PyTorch：

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

卷积条件变分自动编码机（重建丢失中带标签）

PyTorch：

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

卷积条件变分自动编码机（重建损失中没有标签）

PyTorch：

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

GAN

MNIST上完全连接的GAN

TensorFlow 1：

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

PyTorch：

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

MNIST上的卷积GAN

TensorFlow 1：

https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/gan/gan-conv.ipynb

PyTorch：

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

具有标签平滑的MNIST上的卷积GAN

PyTorch：

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

RNN

Many-to-one: Sentiment Analysis / Classification

A simple single-layer RNN (IMDB)

PyTorch：

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

A simple single-layer RNN with packed sequences to ignore padding characters (IMDB)

PyTorch：

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

RNN with LSTM cells (IMDB)

PyTorch：

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

RNN with LSTM cells and Own Dataset in CSV Format (IMDB)

PyTorch：

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

RNN with GRU cells (IMDB)

PyTorch：

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

Multilayer bi-directional RNN (IMDB)

PyTorch：

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

以上列举的都只是冰山一角而已，喜欢的伙伴们可以自己到GitHub上一探究竟，最后附上GitHub地址：https://github.com/rasbt/deeplearning-models

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