【AI前沿動態(tài)】GitHub趨勢榜第一：TensorFlow+PyTorch深度學(xué)習(xí)資源大匯總
      
    
      
        網(wǎng)友投稿
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        2025-03-31
		    
      
    
                    
      【新智元導(dǎo)讀】該項目是Jupyter Notebook中TensorFlow和PyTorch的各種深度學(xué)習(xí)架構(gòu)，模型和技巧的集合。內(nèi)容非常豐富，適用于Python 3.7，適合當(dāng)做工具書。
      本文搜集整理了Jupyter Notebook中TensorFlow和PyTorch的各種深度學(xué)習(xí)架構(gòu)，模型和技巧，內(nèi)容非常豐富，適用于Python 3.7，適合當(dāng)做工具書。
      大家可以將內(nèi)容按照需要進(jìn)行分割，打印出來，或者做成電子書等，隨時查閱。
      傳統(tǒng)機(jī)器學(xué)習(xí)
      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
      邏輯回歸
      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
      多層感知器
      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多層感知器
      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
      具有批量歸一化的多層感知器
      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
      具有反向傳播的多層感知器
      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
      基礎(chǔ)
      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
      用等效卷積層代替完全連接
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/fc-to-conv.ipynb
      全卷積
      全卷積神經(jīng)網(wǎng)絡(luò)
      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
      度量學(xué)習(xí)
      具有多層感知器的孿生網(wǎng)絡(luò)
      TensorFlow 1：
      https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/metric/siamese-1.ipynb
      自動編碼機(jī)
      全連接自動編碼機(jī)
      自動編碼機(jī)
      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
      具有解卷積/轉(zhuǎn)置卷積的卷積自動編碼機(jī)
      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
      具有解卷積的卷積自動編碼機(jī)（無池化操作）
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/aer-deconv-nopool.ipynb
      具有最近鄰插值的卷積自動編碼機(jī)
      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
      具有最近鄰插值的卷積自動編碼機(jī) - 在CelebA上進(jìn)行訓(xùn)練
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-nneighbor-celeba.ipynb
      具有最近鄰插值的卷積自動編碼機(jī) - 在Quickdraw上訓(xùn)練
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-nneighbor-quickdraw-1.ipynb
      變分自動編碼機(jī)
      變分自動編碼機(jī)
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-var.ipynb
      卷積變分自動編碼機(jī)
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-conv-var.ipynb
      條件變分自動編碼機(jī)
      條件變分自動編碼機(jī)（重建丟失中帶標(biāo)簽）
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cvae.ipynb
      條件變分自動編碼機(jī)（重建損失中沒有標(biāo)簽）
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cvae_no-out-concat.ipynb
      卷積條件變分自動編碼機(jī)（重建丟失中帶標(biāo)簽）
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cnn-cvae.ipynb
      卷積條件變分自動編碼機(jī)（重建損失中沒有標(biāo)簽）
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/autoencoder/ae-cnn-cvae_no-out-concat.ipynb
      GAN
      MNIST上完全連接的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上的卷積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
      【AI前沿動態(tài)】GitHub趨勢榜第一：TensorFlow+PyTorch深度學(xué)習(xí)資源大匯總
      具有標(biāo)簽平滑的MNIST上的卷積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
      Many-to-Many / Sequence-to-Sequence
      A simple character RNN to generate new text (Charles Dickens)
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/char_rnn-charlesdickens.ipynb
      序數(shù)回歸
      Ordinal Regression CNN -CORAL w. ResNet34 on AFAD-Lite
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/ordinal/ordinal-cnn-coral-afadlite.ipynb
      Ordinal Regression CNN -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
      Ordinal Regression CNN -Beckham and Pal 2016 w. ResNet34 on AFAD-Lite
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/ordinal/ordinal-cnn-niu-afadlite.ipynb
      技巧和竅門
      Cyclical Learning Rate
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/tricks/cyclical-learning-rate.ipynb
      PyTorch工作流程和機(jī)制
      自定義數(shù)據(jù)集
      使用PyTorch數(shù)據(jù)集加載實用程序用于自定義數(shù)據(jù)集-CSV文件轉(zhuǎn)換為HDF5
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader-csv.ipynb
      使用PyTorch數(shù)據(jù)集加載自定義數(shù)據(jù)集的實用程序 - 來自CelebA的圖像
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader-celeba.ipynb
      使用PyTorch數(shù)據(jù)集加載自定義數(shù)據(jù)集的實用程序 - 從Quickdraw中提取
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/custom-data-loader-quickdraw.ipynb
      使用PyTorch數(shù)據(jù)集加載實用程序用于自定義數(shù)據(jù)集 - 從街景房號（SVHN）數(shù)據(jù)集中繪制
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/custom-data-loader-svhn.ipynb
      訓(xùn)練和預(yù)處理
      帶固定內(nèi)存的數(shù)據(jù)加載
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-resnet34-cifar10-pinmem.ipynb
      標(biāo)準(zhǔn)化圖像
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-standardized.ipynb
      圖像轉(zhuǎn)換示例
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/torchvision-transform-examples.ipynb
      Char-RNN with Own Text File
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/char_rnn-charlesdickens.ipynb
      Sentiment Classification RNN with Own CSV File
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/rnn/rnn_lstm_packed_own_csv_imdb.ipynb
      并行計算
      在CelebA上使用具有DataParallel -VGG-16性別分類器的多個GPU
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/cnn/cnn-vgg16-celeba-data-parallel.ipynb
      其它
      Sequential API and hooks
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/mlp-sequential.ipynb
      圖層內(nèi)的權(quán)重共享
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/cnn-weight-sharing.ipynb
      僅使用Matplotlib在Jupyter Notebook中繪制實時訓(xùn)練性能
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mlp/plot-jupyter-matplotlib.ipynb
      Autograd
      在PyTorch中獲取中間變量的漸變
      PyTorch：
      https://github.com/rasbt/deeplearning-models/blob/master/pytorch_ipynb/mechanics/manual-gradients.ipynb
      TensorFlow工作流及機(jī)制
      自定義數(shù)據(jù)集
      使用NumPy NPZ Archives為Minibatch訓(xùn)練添加圖像數(shù)據(jù)集
      TensorFlow 1：
      https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/image-data-chunking-npz.ipynb
      使用HDF5存儲用于Minibatch培訓(xùn)的圖像數(shù)據(jù)集
      TensorFlow 1：
      https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/image-data-chunking-hdf5.ipynb
      使用輸入Pipeline從TFRecords文件中讀取數(shù)據(jù)
      TensorFlow 1：
      https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/tfrecords.ipynb
      使用隊列運行器直接從磁盤提供圖像
      TensorFlow 1：
      https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/file-queues.ipynb
      使用TensorFlow的Dataset API
      TensorFlow 1：
      https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/dataset-api.ipynb
      訓(xùn)練和預(yù)處理
      保存和加載訓(xùn)練模型 - 來自TensorFlow Checkpoint文件和NumPy NPZ Archives
      TensorFlow 1：
      https://github.com/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/mechanics/saving-and-reloading-models.ipynb
      參考鏈接：
      https://github.com/rasbt/deeplearning-models
      轉(zhuǎn)自：新智元
      EI 人工智能 AI
      
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