Yann LeCun都推荐的深度学习资料合集！
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感知器
TensorFlow：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：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 回归（多项逻辑回归）
TensorFlow：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
TensorFlow：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 的多层感知器
TensorFlow：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：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
具有从头开始反向传播的多层感知器
TensorFlow：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
卷积神经网络
TensorFlow：https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/cnn/cnn-basic.ipynb
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-basic.ipynb
具有 He 初始化的卷积神经网络
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-he-init.ipynb
用等效卷积层替换全连接
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/fc-to-conv.ipynb
全卷积神经网络
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-allconv.ipynb
CIFAR-10 上的 AlexNet
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-alexnet-cifar10.ipynb
卷积神经网络 VGG-16
TensorFlow：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
在 CelebA 上训练的 VGG-16 性别分类器
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg16-celeba.ipynb
卷积神经网络 VGG-19
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg19.ipynb
ResNet 与残差块
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/resnet-ex-1.ipynb
在 MNIST 上训练的 ResNet-18 数字分类器
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet18-mnist.ipynb
在 CelebA 上训练的 ResNet-18 性别分类器
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet18-celeba-dataparallel.ipynb
在 MNIST 上训练的 ResNet-34 数字分类器
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet34-mnist.ipynb
在 CelebA 上训练的 ResNet-34 性别分类器
PyTorch：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：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet50-mnist.ipynb
在 CelebA 上训练的 ResNet-50 性别分类器
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet50-celeba-dataparallel.ipynb
在 CelebA 上训练的 ResNet-101 性别分类器
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet101-celeba.ipynb
在 CIFAR-10 上训练的 ResNet-101
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet101-cifar10.ipynb
在 CelebA 上训练的 ResNet-152 性别分类器
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet152-celeba.ipynb
CIFAR-10 分类器网络中的网络
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/nin-cifar10.ipynb
具有多层感知器的孪生网络
TensorFlow：https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/metric/siamese-1.ipynb
自编码器
TensorFlow：https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/autoencoder/ae-basic.ipynb
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-basic.ipynb
具有解卷积 / 转置卷积的卷积自编码器
TensorFlow：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（不具有池化操作）的卷积自编码器
TensorFlow：https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/autoencoder/ae-conv-nneighbor.ipynb
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-nneighbor.ipynb
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-deconv-nopool.ipynb
具有最近邻插值的卷积自编码器
在 CelebA 上训练的具有最近邻插值的卷积自编码器
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-nneighbor-celeba.ipynb
在 Quickdraw 上训练的具有最近邻插值的卷积自编码器
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-nneighbor-quickdraw-1.ipynb
变分自编码器
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-var.ipynb
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-var.ipynb
卷积变分自编码器
条件变分自编码器（具有重构损失中的标签）
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cvae.ipynb
条件变分自编码器（不具有重构损失中的标签）
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cvae_no-out-concat.ipynb
卷积条件变分自编码器（具有重构损失中的标签）
PYTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cnn-cvae.ipynb
卷积条件变分自编码器（不具有重构损失中的标签）
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cnn-cvae_no-out-concat.ipynb
MNIST 上的全连接生成对抗网络
TensorFlow：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 上的卷积生成对抗网络
TensorFlow：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
MNIST 上具有标签平滑的卷积生成对抗网络
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/gan/gan-conv-smoothing.ipynb
简单的单层递归神经网络（IMDB）
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_simple_imdb.ipynb
打包序列以忽略填充字符的简单单层递归神经网络（IMDB）
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_simple_packed_imdb.ipynb
具有长短期记忆网络单元的递归神经网络（IMDB）
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_lstm_packed_imdb.ipynb
具有长短期记忆网络单元和经预训练的 GloVe 词向量的递归神经网络（IMDB）
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_lstm_packed_own_csv_imdb.ipynb
具有长短期记忆网络单元和 CSV 格式的自有数据集的递归神经网络（IMDB）
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_lstm_packed_own_csv_imdb.ipynb
具有 GRU 单元的递归神经网络（IMDB）
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_gru_packed_imdb.ipynb
多层双向递归神经网络（IMDB）
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_gru_packed_imdb.ipynb
为生成新文本（Charles Dickens）的简单字符递归神经网络
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/char_rnn-charlesdickens.ipynb
序数回归卷积神经网络——CORAL w. ResNet34 on AFAD-Lite
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/ordinal/ordinal-cnn-coral-afadlite.ipynb
序数回归卷积神经网络——Niu et al. 2016 w. ResNet34 on AFAD-Lite
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/ordinal/ordinal-cnn-niu-afadlite.ipynb
序数回归卷积神经网络——Beckham and Pal 2016 w. ResNet34 on AFAD-Lite
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/ordinal/ordinal-cnn-beckham2016-afadlite.ipynb
周期学习率
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/tricks/cyclical-learning-rate.ipynb
为自定义数据集使用 PyTorch 数据集加载实用程序——CSV 文件转换为 HDF5
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader-csv.ipynb
为自定义数据集使用 PyTorch 数据集加载使用程序——来自 CelebA 的面部图像
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader-celeba.ipynb
为自定义数据集使用 PyTorch 数据集加载使用程序——来自 Quickdraw 的图像
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader-quickdraw.ipynb
为自定义数据集使用 PyTorch 数据集加载使用程序——来自街景门牌号（SVHN）数据集的图像
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader-svhn.ipynb
为自定义数据集使用 PyTorch 数据集加载使用程序——亚洲人面部数据集（AFAD）
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader-afad.ipynb
为自定义数据集使用 PyTorch 数据集加载使用程序——历史彩色图像
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader_dating-historical-color-images.ipynb
生成验证集拆分
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/validation-splits.ipynb
具有固定内存的数据加载
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet34-cifar10-pinmem.ipynb
图像标准化
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-standardized.ipynb
图像转换示例
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/torchvision-transform-examples.ipynb
具有自己的文本文件的 Char-RNN
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/char_rnn-charlesdickens.ipynb
具有自己的 CSV 文件的情感分类递归神经网络
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_lstm_packed_own_csv_imdb.ipynb
使用数据并行的多 GPU——VGG-16 CelebA 上的性别分类器
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg16-celeba-data-parallel.ipynb
顺序 API 和钩子
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/mlp-sequential.ipynb
层内权重共享
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/cnn-weight-sharing.ipynb
只使用 Matplotlib 在 Jupyter Notebook 绘制实时训练性能
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/plot-jupyter-matplotlib.ipynb
在 PyTorch 中获取中间变量的梯度
PyTorch：https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/manual-gradients.ipynb
为 Mini-batch 训练使用 NumPy NPZ Archives 进行组块图像数据集
TensorFlow：https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/image-data-chunking-npz.ipynb
为 Mini-batch 使用 HDF5 进行存储图像数据集
TensorFlow：https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/image-data-chunking-hdf5.ipynb
使用输入管道从 TFRecords 文件读取数据
TensorFlow：https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/tfrecords.ipynb
使用 Queue Runners 从硬盘直接馈入图像
TensorFlow：https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/file-queues.ipynb
使用 TensorFlow 的数据集 API
TensorFlow：https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/dataset-api.ipynb
保存和加载训练过的模型——从 TensorFlow 检查点文件和 NumPy NPZ Archives
TensorFlow：https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/saving-and-reloading-models.ipynb
Sebastian Raschka，机器学习研究者、开源贡献者。《Python 机器学习》作者，威斯康星大学麦迪逊分校统计学助理教授。
原文链接：
https://github.com/rasbt/deeplearning-models
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