本资源整理了常见的各类深度学习模型和策略，涉及机器学习基础、神经网路基础、CNN、GNN、RNN、GAN等，并给出了基于TensorFlow或 PyTorch的实现细节，这些实现都是Jupyter Notebooks编写，可运行Debug且配有详细的讲解，可以帮助你体会算法实现的细节。
资源整理自网络，源地址：https://github.com/rasbt/deeplearning-models
带链接版资源下载地址：
链接: https://pan.baidu.com/s/1jsqqTPtA33UfEPUIJ0qCmg
提取码: 97nm
传统机器学习
•Perceptron
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]
•Logistic Regression
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]
•Softmax Regression (Multinomial Logistic Regression)
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]
•Softmax Regression with MLxtend's plot_decision_regions on Iris
[PyTorch: GitHub | Nbviewer]
多层感知器
•Multilayer Perceptron
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]
•Multilayer Perceptron with Dropout
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]
•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
Basic
•Convolutional Neural Network
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]
•Convolutional Neural Network with He Initialization
[PyTorch: GitHub | Nbviewer]
Concepts
•Replacing Fully-Connnected by Equivalent Convolutional Layers
[PyTorch: GitHub | Nbviewer]
Fully Convolutional
•Fully Convolutional Neural Network
[PyTorch: GitHub | Nbviewer]
LeNet
•LeNet-5 on MNIST
[PyTorch: GitHub | Nbviewer]
•LeNet-5 on CIFAR-10
[PyTorch: GitHub | Nbviewer]
•LeNet-5 on QuickDraw
[PyTorch: GitHub | Nbviewer]
AlexNet
•AlexNet on CIFAR-10
[PyTorch: GitHub | Nbviewer]
VGG
•Convolutional Neural Network VGG-16
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]
•VGG-16 Gender Classifier Trained on CelebA
[PyTorch: GitHub | Nbviewer]
•Convolutional Neural Network VGG-19
[PyTorch: GitHub | Nbviewer]
DenseNet
•DenseNet-121 Digit Classifier Trained on MNIST
[PyTorch: GitHub | Nbviewer]
•DenseNet-121 Image Classifier Trained on CIFAR-10
[PyTorch: GitHub | Nbviewer]
ResNet
•ResNet and Residual Blocks
[PyTorch: GitHub | Nbviewer]
•ResNet-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 Network
•Network in Network CIFAR-10 Classifier
[PyTorch: GitHub | Nbviewer]
归一化层
•BatchNorm before and after Activation for Network-in-Network CIFAR-10 Classifier
[PyTorch: GitHub | Nbviewer]
•Filter Response Normalization for Network-in-Network CIFAR-10 Classifier
[PyTorch: GitHub | Nbviewer]
度量学习
•Siamese Network with Multilayer Perceptrons
[TensorFlow 1: GitHub | Nbviewer]
自编码器
Fully-connected Autoencoders
•Autoencoder (MNIST)
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]
•Autoencoder (MNIST) + Scikit-Learn Random Forest Classifier
[TensorFlow 1: GitHub | Nbviewer]
[PyTorch: GitHub | Nbviewer]
Convolutional Autoencoders
•Convolutional 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 Autoencoders
•Variational Autoencoder
[PyTorch: GitHub | Nbviewer]
•Convolutional Variational Autoencoder
[PyTorch: GitHub | Nbviewer]
Conditional Variational Autoencoders
•Conditional 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]
生成对抗网络 (GANs)
•Fully Connected GAN on MNIST
[TensorFlow 1: GitHub | Nbviewer]
[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 (GNNs)
•Most Basic Graph Neural Network with Gaussian Filter on MNIST
[PyTorch: GitHub | Nbviewer]
•Basic 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(RNNs)
Many-to-one: Sentiment Analysis / Classification
•A simple single-layer RNN (IMDB)
[PyTorch: GitHub | Nbviewer]
•A 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-Sequence
•A simple character RNN to generate new text (Charles Dickens)
[PyTorch: GitHub | Nbviewer]
有序回归
•Ordinal 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]
策略和技巧
•Cyclical 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 Learning Example (VGG16 pre-trained on ImageNet for Cifar-10)
[PyTorch: GitHub | Nbviewer]
PyTorch工作流程和机制
Custom Datasets
•Custom Data Loader Example for PNG Files
[PyTorch: GitHub | Nbviewer]
•Using PyTorch Dataset Loading Utilities for Custom Datasets -- CSV files converted to HDF5
[PyTorch: GitHub | Nbviewer]
•Using PyTorch Dataset Loading Utilities for Custom Datasets -- Face Images from CelebA
[PyTorch: GitHub | Nbviewer]
•Using PyTorch Dataset Loading Utilities for Custom Datasets -- Drawings from Quickdraw
[PyTorch: GitHub | Nbviewer]
•Using PyTorch Dataset Loading Utilities for Custom Datasets -- Drawings from the Street View House Number (SVHN) Dataset
[PyTorch: GitHub | Nbviewer]
•Using PyTorch Dataset Loading Utilities for Custom Datasets -- Asian Face Dataset (AFAD)
[PyTorch: GitHub | Nbviewer]
•Using PyTorch Dataset Loading Utilities for Custom Datasets -- Dating Historical Color Images
[PyTorch: GitHub | Nbviewer]
Training and Preprocessing
•Generating Validation Set Splits
[PyTorch]: GitHub | Nbviewer]
•Dataloading with Pinned Memory
[PyTorch: GitHub | Nbviewer]
•Standardizing Images
[PyTorch: GitHub | Nbviewer]
•Image Transformation Examples
[PyTorch: GitHub | Nbviewer]
•Char-RNN with Own Text File
[PyTorch: GitHub | Nbviewer]
•Sentiment Classification RNN with Own CSV File
[PyTorch: GitHub | Nbviewer]
Parallel Computing
•Using Multiple GPUs with DataParallel -- VGG-16 Gender Classifier on CelebA
[PyTorch: GitHub | Nbviewer]
Other
•Sequential API and hooks
[PyTorch: GitHub | Nbviewer]
•Weight Sharing Within a Layer
[PyTorch: GitHub | Nbviewer]
•Plotting Live Training Performance in Jupyter Notebooks with just Matplotlib
[PyTorch: GitHub | Nbviewer]
Autograd
•Getting Gradients of an Intermediate Variable in PyTorch
[PyTorch: GitHub | Nbviewer]
TensorFlow工作流程和机制
Custom Datasets
•Chunking an Image Dataset for Minibatch Training using NumPy NPZ Archives
[TensorFlow 1: GitHub | Nbviewer]
•Storing an Image Dataset for Minibatch Training using HDF5
[TensorFlow 1: GitHub | Nbviewer]
•Using Input Pipelines to Read Data from TFRecords Files
[TensorFlow 1: GitHub | Nbviewer]
•Using Queue Runners to Feed Images Directly from Disk
[TensorFlow 1: GitHub | Nbviewer]
•Using TensorFlow's Dataset API
[TensorFlow 1: GitHub | Nbviewer]
Training and Preprocessing
•Saving and Loading Trained Models -- from TensorFlow Checkpoint Files and NumPy NPZ Archives
[TensorFlow 1: GitHub | Nbviewer]
