├── .github └── ISSUE_TEMPLATE │ └── bug_report.md ├── .gitignore ├── Dockerfile ├── LICENSE ├── code ├── chapter02_prerequisite │ ├── 2.2_tensor.ipynb │ └── 2.3_autograd.ipynb ├── chapter03_DL-basics │ ├── 3.10_mlp-pytorch.ipynb │ ├── 3.11_underfit-overfit.ipynb │ ├── 3.12_weight-decay.ipynb │ ├── 3.13_dropout.ipynb │ ├── 3.16_kaggle-house-price.ipynb │ ├── 3.1_linear-regression.ipynb │ ├── 3.2_linear-regression-scratch.ipynb │ ├── 3.3_linear-regression-pytorch.ipynb │ ├── 3.5_fashion-mnist.ipynb │ ├── 3.6_softmax-regression-scratch.ipynb │ ├── 3.7_softmax-regression-pytorch.ipynb │ ├── 3.8_mlp.ipynb │ ├── 3.9_mlp-scratch.ipynb │ └── submission.csv ├── chapter04_DL_computation │ ├── 4.1_model-construction.ipynb │ ├── 4.2_parameters.ipynb │ ├── 4.4_custom-layer.ipynb │ ├── 4.5_read-write.ipynb │ └── 4.6_use-gpu.ipynb ├── chapter05_CNN │ ├── 5.10_batch-norm.ipynb │ ├── 5.11_resnet.ipynb │ ├── 5.12_densenet.ipynb │ ├── 5.1_conv-layer.ipynb │ ├── 5.2_padding-and-strides.ipynb │ ├── 5.3_channels.ipynb │ ├── 5.4_pooling.ipynb │ ├── 5.5_lenet.ipynb │ ├── 5.6_alexnet.ipynb │ ├── 5.7_vgg.ipynb │ ├── 5.8_nin.ipynb │ └── 5.9_googlenet.ipynb ├── chapter06_RNN │ ├── 6.2_rnn.ipynb │ ├── 6.3_lang-model-dataset.ipynb │ ├── 6.4_rnn-scratch.ipynb │ ├── 6.5_rnn-pytorch.ipynb │ ├── 6.7_gru.ipynb │ └── 6.8_lstm.ipynb ├── chapter07_optimization │ ├── 7.1_optimization-intro.ipynb │ ├── 7.2_gd-sgd.ipynb │ ├── 7.3_minibatch-sgd.ipynb │ ├── 7.4_momentum.ipynb │ ├── 7.5_adagrad.ipynb │ ├── 7.6_rmsprop.ipynb │ ├── 7.7_adadelta.ipynb │ └── 7.8_adam.ipynb ├── chapter08_computational-performance │ ├── 8.1_hybridize.ipynb │ ├── 8.3_auto-parallelism.ipynb │ ├── 8.4_model.pt │ └── 8.4_multiple-gpus.ipynb ├── chapter09_computer-vision │ ├── 9.11_neural-style.ipynb │ ├── 9.1_image-augmentation.ipynb │ ├── 9.2_fine-tuning.ipynb │ ├── 9.3_bounding-box.ipynb │ ├── 9.4_anchor.ipynb │ ├── 9.5_multiscale-object-detection.ipynb │ ├── 9.6.0_prepare_pikachu.ipynb │ ├── 9.6_object-detection-dataset.ipynb │ ├── 9.8_rcnn.ipynb │ └── 9.9_semantic-segmentation-and-dataset.ipynb ├── chapter10_natural-language-processing │ ├── 10.12_machine-translation.ipynb │ ├── 10.3_word2vec-pytorch.ipynb │ ├── 10.6_similarity-analogy.ipynb │ ├── 10.7_sentiment-analysis-rnn.ipynb │ └── 10.8_sentiment-analysis-cnn.ipynb └── d2lzh_pytorch │ ├── __init__.py │ └── utils.py ├── data ├── airfoil_self_noise.dat ├── autumn_oak.jpg ├── fr-en-small.txt ├── jaychou_lyrics.txt.zip ├── kaggle_house │ ├── data_description.txt │ ├── sample_submission.csv │ ├── test.csv │ └── train.csv ├── ptb │ ├── README │ ├── ptb.test.txt │ ├── ptb.train.txt │ └── ptb.valid.txt └── rainier.jpg └── docs ├── .nojekyll ├── README.md ├── _sidebar.md ├── chapter01_DL-intro └── deep-learning-intro.md ├── chapter02_prerequisite ├── 2.1_install.md ├── 2.2_tensor.md └── 2.3_autograd.md ├── chapter03_DL-basics ├── 3.10_mlp-pytorch.md ├── 3.11_underfit-overfit.md ├── 3.12_weight-decay.md ├── 3.13_dropout.md ├── 3.14_backprop.md ├── 3.15_numerical-stability-and-init.md ├── 3.16_kaggle-house-price.md ├── 3.1_linear-regression.md ├── 3.2_linear-regression-scratch.md ├── 3.3_linear-regression-pytorch.md ├── 3.4_softmax-regression.md ├── 3.5_fashion-mnist.md ├── 3.6_softmax-regression-scratch.md ├── 3.7_softmax-regression-pytorch.md ├── 3.8_mlp.md └── 3.9_mlp-scratch.md ├── chapter04_DL_computation ├── 4.1_model-construction.md ├── 4.2_parameters.md ├── 4.3_deferred-init.md ├── 4.4_custom-layer.md ├── 4.5_read-write.md └── 4.6_use-gpu.md ├── chapter05_CNN ├── 5.10_batch-norm.md ├── 5.11_resnet.md ├── 5.12_densenet.md ├── 5.1_conv-layer.md ├── 5.2_padding-and-strides.md ├── 5.3_channels.md ├── 5.4_pooling.md ├── 5.5_lenet.md ├── 5.6_alexnet.md ├── 5.7_vgg.md ├── 5.8_nin.md └── 5.9_googlenet.md ├── chapter06_RNN ├── 6.10_bi-rnn.md ├── 6.1_lang-model.md ├── 6.2_rnn.md ├── 6.3_lang-model-dataset.md ├── 6.4_rnn-scratch.md ├── 6.5_rnn-pytorch.md ├── 6.6_bptt.md ├── 6.7_gru.md ├── 6.8_lstm.md └── 6.9_deep-rnn.md ├── chapter07_optimization ├── 7.1_optimization-intro.md ├── 7.2_gd-sgd.md ├── 7.3_minibatch-sgd.md ├── 7.4_momentum.md ├── 7.5_adagrad.md ├── 7.6_rmsprop.md ├── 7.7_adadelta.md └── 7.8_adam.md ├── chapter08_computational-performance ├── 8.1_hybridize.md ├── 8.2_async-computation.md ├── 8.3_auto-parallelism.md └── 8.4_multiple-gpus.md ├── chapter09_computer-vision ├── 9.11_neural-style.md ├── 9.1_image-augmentation.md ├── 9.2_fine-tuning.md ├── 9.3_bounding-box.md ├── 9.4_anchor.md ├── 9.5_multiscale-object-detection.md ├── 9.6_object-detection-dataset.md ├── 9.8_rcnn.md └── 9.9_semantic-segmentation-and-dataset.md ├── chapter10_natural-language-processing ├── 10.10_beam-search.md ├── 10.11_attention.md ├── 10.12_machine-translation.md ├── 10.1_word2vec.md ├── 10.2_approx-training.md ├── 10.3_word2vec-pytorch.md ├── 10.4_fasttext.md ├── 10.5_glove.md ├── 10.6_similarity-analogy.md ├── 10.7_sentiment-analysis-rnn.md ├── 10.8_sentiment-analysis-cnn.md └── 10.9_seq2seq.md ├── docsify.js ├── img ├── book-org.svg ├── book_cover.jpg ├── cat1.jpg ├── catdog.jpg ├── chapter01 │ └── 1.1_koebel.jpg ├── chapter02 │ └── 2.1_jupyter.jpg ├── chapter03 │ ├── 3.11_capacity_vs_error.svg │ ├── 3.11_output1.png │ ├── 3.11_output2.png │ ├── 3.11_output3.png │ ├── 3.12_output1.png │ ├── 3.12_output2.png │ ├── 3.12_output3.png │ ├── 3.12_output4.png │ ├── 3.13_dropout.svg │ ├── 3.14_forward.svg │ ├── 3.16_house_pricing.png │ ├── 3.16_kaggle.png │ ├── 3.16_kaggle_submit.png │ ├── 3.16_output1.png │ ├── 3.16_output2.png │ ├── 3.16_output3.png │ ├── 3.1_linreg.svg │ ├── 3.2_output1.png │ ├── 3.4_softmaxreg.svg │ ├── 3.5_output1.png │ ├── 3.6_output1.png │ ├── 3.8_mlp.svg │ ├── 3.8_relu.png │ ├── 3.8_relu_grad.png │ ├── 3.8_sigmoid.png │ ├── 3.8_sigmoid_grad.png │ ├── 3.8_tanh.png │ └── 3.8_tanh_grad.png ├── chapter05 │ ├── 5.11_residual-block.svg │ ├── 5.12_densenet.svg │ ├── 5.1_correlation.svg │ ├── 5.2_conv_pad.svg │ ├── 5.2_conv_stride.svg │ ├── 5.3_conv_1x1.svg │ ├── 5.3_conv_multi_in.svg │ ├── 5.4_pooling.svg │ ├── 5.5_lenet.png │ ├── 5.6_alexnet.png │ ├── 5.8_nin.svg │ └── 5.9_inception.svg ├── chapter06 │ ├── 6.10_birnn.svg │ ├── 6.2_rnn-train.svg │ ├── 6.2_rnn.svg │ ├── 6.5.png │ ├── 6.6_rnn-bptt.svg │ ├── 6.7_gru_1.svg │ ├── 6.7_gru_2.svg │ ├── 6.7_gru_3.svg │ ├── 6.8_lstm_0.svg │ ├── 6.8_lstm_1.svg │ ├── 6.8_lstm_2.svg │ ├── 6.8_lstm_3.svg │ └── 6.9_deep-rnn.svg ├── chapter07 │ ├── 7.1_output1.svg │ ├── 7.1_output2.svg │ ├── 7.1_output3.svg │ ├── 7.2_output1.svg │ ├── 7.2_output2.svg │ ├── 7.2_output3.svg │ ├── 7.2_output4.svg │ ├── 7.2_output5.svg │ ├── 7.3_output1.png │ ├── 7.3_output2.png │ ├── 7.3_output3.png │ ├── 7.3_output4.png │ ├── 7.4_output1.png │ ├── 7.4_output2.png │ ├── 7.4_output3.png │ ├── 7.4_output4.png │ ├── 7.4_output5.png │ ├── 7.4_output6.png │ ├── 7.4_output7.png │ ├── 7.4_output8.png │ ├── 7.5_output1.png │ ├── 7.5_output2.png │ ├── 7.5_output3.png │ ├── 7.5_output4.png │ ├── 7.6_output1.png │ ├── 7.6_output2.png │ ├── 7.6_output3.png │ ├── 7.7_output1.png │ ├── 7.7_output2.png │ ├── 7.8_output1.png │ └── 7.8_output2.png ├── chapter09 │ ├── 9.11_neural-style.svg │ ├── 9.11_output1.png │ ├── 9.11_output2.png │ ├── 9.11_output3.png │ ├── 9.11_output4.png │ ├── 9.11_style-transfer.svg │ ├── 9.1_output1.png │ ├── 9.1_output10.png │ ├── 9.1_output2.png │ ├── 9.1_output3.png │ ├── 9.1_output4.png │ ├── 9.1_output5.png │ ├── 9.1_output6.png │ ├── 9.1_output7.png │ ├── 9.1_output8.png │ ├── 9.1_output9.png │ ├── 9.2_finetune.svg │ ├── 9.2_output1.png │ ├── 9.3_output1.png │ ├── 9.3_output2.png │ ├── 9.4_anchor-label.svg │ ├── 9.4_iou.svg │ ├── 9.4_output1.png │ ├── 9.4_output2.png │ ├── 9.4_output3.png │ ├── 9.4_output4.png │ ├── 9.5_output1.png │ ├── 9.5_output2.png │ ├── 9.5_output3.png │ ├── 9.6_output1.png │ ├── 9.8_fast-rcnn.svg │ ├── 9.8_faster-rcnn.svg │ ├── 9.8_mask-rcnn.svg │ ├── 9.8_r-cnn.svg │ ├── 9.8_roi.svg │ ├── 9.9_output1.png │ ├── 9.9_output2.png │ └── 9.9_segmentation.svg ├── chapter10 │ ├── 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