【导读】深度学习在过去十年获得了极大进展，出现很多新的模型，并且伴随TensorFlow和Pytorch框架的出现，有很多实现，但对于初学者和很多从业人员，如何选择合适的实现，是个选择。rasbt大神在Github上整理了关于深度学习模型TensorFlow和Pytorch代码实现集合，含有100个，各种各样的深度学习架构，模型，和技巧的集合Jupyter Notebooks，从基础的逻辑回归到神经网络到CNN到GNN等，可谓一网打尽，值得收藏！
地址：https://github.com/rasbt/deeplearning-models
传统机器学习
感知器 Perceptron
[TensorFlow 1: GitHub | Nbviewer]
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/basic-ml/perceptron.ipynb
[PyTorch: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/pytorch_ipynb/basic-ml/perceptron.ipynb逻辑回归 Logistic Regression
[TensorFlow 1: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/basic-ml/logistic-regression.ipynb
[PyTorch: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/pytorch_ipynb/basic-ml/logistic-regression.ipynbSoftmax Regression (Multinomial Logistic Regression)
[TensorFlow 1: GitHub | Nbviewer]
https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/basic-ml/softmax-regression.ipynb
[PyTorch: GitHub | Nbviewer]
https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/basic-ml/softmax-regression.ipynbSoftmax Regression with MLxtend's plot_decision_regions on Iris
[PyTorch: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/pytorch_ipynb/basic-ml/softmax-regression-mlxtend-1.ipynb多层感知器
多层感知器 Multilayer Perceptron
[TensorFlow 1: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mlp/mlp-basic.ipynb
[PyTorch: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/mlp-basic.ipynb带Dropout的多层感知器 Multilayer Perceptron with Dropout
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]具有批处理规范化的多层感知器 Multilayer Perceptron with Batch Normalization
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]Multilayer Perceptron with Backpropagation from Scratch
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]卷积神经网络
基础卷积神经网络 Convolutional Neural Network
[TensorFlow 1: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/cnn/cnn-basic.ipynb
[PyTorch: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-basic.ipynbConvolutional Neural Network with He Initialization
[PyTorch: GitHub | Nbviewer]ConceptsReplacing Fully-Connnected by Equivalent Convolutional Layers
[PyTorch: GitHub | Nbviewer]Fully ConvolutionalFully Convolutional Neural Network
[PyTorch: GitHub | Nbviewer]LeNetLeNet-5 on MNIST
[PyTorch: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-lenet5-mnist.ipynbLeNet-5 on CIFAR-10
[PyTorch: GitHub | Nbviewer]LeNet-5 on QuickDraw
[PyTorch: GitHub | Nbviewer]AlexNetAlexNet on CIFAR-10
[PyTorch: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-alexnet-cifar10.ipynbVGGConvolutional Neural Network VGG-16
[TensorFlow 1: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/cnn/cnn-vgg16.ipynb
[PyTorch: GitHub | Nbviewer]VGG-16 Gender Classifier Trained on CelebA
[PyTorch: GitHub | Nbviewer]Convolutional Neural Network VGG-19
[PyTorch: GitHub | Nbviewer]DenseNetDenseNet-121 Digit Classifier Trained on MNIST
[PyTorch: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-densenet121-mnist.ipynbDenseNet-121 Image Classifier Trained on CIFAR-10
[PyTorch: GitHub | Nbviewer]ResNetResNet and Residual Blocks
[PyTorch: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/resnet-ex-1.ipynbResNet-18 Digit Classifier Trained on MNIST
[PyTorch: GitHub | Nbviewer]ResNet-18 Gender Classifier Trained on CelebA
[PyTorch: GitHub | Nbviewer]ResNet-34 Digit Classifier Trained on MNIST
[PyTorch: GitHub | Nbviewer]ResNet-34 Object Classifier Trained on QuickDraw
[PyTorch: GitHub | Nbviewer]ResNet-34 Gender Classifier Trained on CelebA
[PyTorch: GitHub | Nbviewer]ResNet-50 Digit Classifier Trained on MNIST
[PyTorch: GitHub | Nbviewer]ResNet-50 Gender Classifier Trained on CelebA
[PyTorch: GitHub | Nbviewer]ResNet-101 Gender Classifier Trained on CelebA
[PyTorch: GitHub | Nbviewer]ResNet-101 Trained on CIFAR-10
[PyTorch: GitHub | Nbviewer]ResNet-152 Gender Classifier Trained on CelebA
[PyTorch: GitHub | Nbviewer]Network in NetworkNetwork in Network CIFAR-10 Classifier
[PyTorch: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/nin-cifar10.ipynb归一化层 Normalization LayersBatchNorm before and after Activation for Network-in-Network CIFAR-10 Classifier
[PyTorch: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/nin-cifar10_batchnorm.ipynbFilter Response Normalization for Network-in-Network CIFAR-10 Classifier
[PyTorch: GitHub | Nbviewer] 度量学习 Metric LearningSiamese Network with Multilayer Perceptrons
[TensorFlow 1: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/metric/siamese-1.ipynb自编码器 Autoencoders全连接自编码器 Fully-connected Autoencoders
Autoencoder (MNIST)
[TensorFlow 1: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/autoencoder/ae-basic.ipynb
[PyTorch: GitHub | Nbviewer]Autoencoder (MNIST) + Scikit-Learn Random Forest Classifier
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]Convolutional AutoencodersConvolutional Autoencoder with Deconvolutions / Transposed Convolutions
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]Convolutional Autoencoder with Deconvolutions and Continuous Jaccard Distance
[PyTorch: GitHub | Nbviewer]Convolutional Autoencoder with Deconvolutions (without pooling operations)
[PyTorch: GitHub | Nbviewer]Convolutional Autoencoder with Nearest-neighbor Interpolation
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]Convolutional Autoencoder with Nearest-neighbor Interpolation -- Trained on CelebA
[PyTorch: GitHub | Nbviewer]Convolutional Autoencoder with Nearest-neighbor Interpolation -- Trained on Quickdraw
[PyTorch: GitHub | Nbviewer]Variational AutoencodersVariational Autoencoder
[PyTorch: GitHub | Nbviewer]Convolutional Variational Autoencoder
[PyTorch: GitHub | Nbviewer]Conditional Variational AutoencodersConditional Variational Autoencoder (with labels in reconstruction loss)
[PyTorch: GitHub | Nbviewer]Conditional Variational Autoencoder (without labels in reconstruction loss)
[PyTorch: GitHub | Nbviewer]Convolutional Conditional Variational Autoencoder (with labels in reconstruction loss)
[PyTorch: GitHub | Nbviewer]Convolutional Conditional Variational Autoencoder (without labels in reconstruction loss)
[PyTorch: GitHub | Nbviewer] 生成式对抗网络 Generative Adversarial Networks (GANs)Fully Connected GAN on MNIST
[TensorFlow 1: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/gan/gan.ipynb
[PyTorch: GitHub | Nbviewer]Fully Connected Wasserstein GAN on MNIST
[PyTorch: GitHub | Nbviewer]Convolutional GAN on MNIST
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]Convolutional GAN on MNIST with Label Smoothing
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]Convolutional Wasserstein GAN on MNIST
[PyTorch: GitHub | Nbviewer] 图神经网络 Graph Neural Networks (GNNs)Most Basic Graph Neural Network with Gaussian Filter on MNIST
[PyTorch: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/pytorch_ipynb/gnn/gnn-basic-1.ipynbBasic Graph Neural Network with Edge Prediction on MNIST
[PyTorch: GitHub | Nbviewer]Basic Graph Neural Network with Spectral Graph Convolution on MNIST
[PyTorch: GitHub | Nbviewer]循环神经网络 Recurrent Neural Networks (RNNs)Many-to-one: Sentiment Analysis / Classification
A simple single-layer RNN (IMDB)
[PyTorch: GitHub | Nbviewer]
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_simple_imdb.ipynbA simple single-layer RNN with packed sequences to ignore padding characters (IMDB)
[PyTorch: GitHub | Nbviewer]RNN with LSTM cells (IMDB)
[PyTorch: GitHub | Nbviewer]RNN with LSTM cells (IMDB) and pre-trained GloVe word vectors
[PyTorch: GitHub | Nbviewer]RNN with LSTM cells and Own Dataset in CSV Format (IMDB)
[PyTorch: GitHub | Nbviewer]RNN with GRU cells (IMDB)
[PyTorch: GitHub | Nbviewer]Multilayer bi-directional RNN (IMDB)
[PyTorch: GitHub | Nbviewer]Bidirectional Multi-layer RNN with LSTM with Own Dataset in CSV Format (AG News)
[PyTorch: GitHub | Nbviewer]Bidirectional Multi-layer RNN with LSTM with Own Dataset in CSV Format (Yelp Review Polarity)
[PyTorch: GitHub | Nbviewer]Bidirectional Multi-layer RNN with LSTM with Own Dataset in CSV Format (Amazon Review Polarity)
[PyTorch: GitHub | Nbviewer]Many-to-Many / Sequence-to-SequenceA simple character RNN to generate new text (Charles Dickens)
[PyTorch: GitHub | Nbviewer]Ordinal RegressionOrdinal Regression CNN -- CORAL w. ResNet34 on AFAD-Lite
[PyTorch: GitHub | Nbviewer]Ordinal Regression CNN -- Niu et al. 2016 w. ResNet34 on AFAD-Lite
[PyTorch: GitHub | Nbviewer]Ordinal Regression CNN -- Beckham and Pal 2016 w. ResNet34 on AFAD-Lite
[PyTorch: GitHub | Nbviewer]Tips and TricksCyclical Learning Rate
[PyTorch: GitHub | Nbviewer]Annealing with Increasing the Batch Size (w. CIFAR-10 & AlexNet)
[PyTorch: GitHub | Nbviewer]Gradient Clipping (w. MLP on MNIST)
[PyTorch: GitHub | Nbviewer] 迁移学习 Transfer LearningTransfer Learning Example (VGG16 pre-trained on ImageNet for Cifar-10) [PyTorch: GitHub | Nbviewer
https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/pytorch_ipynb/transfer/transferlearning-vgg16-cifar10-1.ipynb