├── .gitattributes ├── .github ├── PULL_REQUEST_TEMPLATE.md ├── actions │ ├── setup_env_vars │ │ └── action.yml │ └── submit-job │ │ └── action.yml ├── workflow_scripts │ ├── build_and_deploy.sh │ ├── build_html.sh │ ├── build_jax.sh │ ├── build_mxnet.sh │ ├── build_pytorch.sh │ ├── build_tf.sh │ ├── setup_git.sh │ └── utils.sh └── workflows │ ├── build-docker.yml │ ├── ci.yml │ └── clear-cache.yml ├── .gitignore ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── INFO.md ├── LICENSE ├── LICENSE-SAMPLECODE ├── LICENSE-SUMMARY ├── README.md ├── STYLE_GUIDE.md ├── chapter_appendix-mathematics-for-deep-learning ├── distributions.md ├── eigendecomposition.md ├── geometry-linear-algebraic-ops.md ├── index.md ├── information-theory.md ├── integral-calculus.md ├── maximum-likelihood.md ├── multivariable-calculus.md ├── naive-bayes.md ├── random-variables.md ├── single-variable-calculus.md └── statistics.md ├── chapter_appendix-tools-for-deep-learning ├── aws.md ├── colab.md ├── contributing.md ├── d2l.md ├── index.md ├── jupyter.md ├── sagemaker.md ├── selecting-servers-gpus.md └── utils.md ├── chapter_attention-mechanisms-and-transformers ├── attention-pooling.md ├── attention-scoring-functions.md ├── bahdanau-attention.md ├── index.md ├── large-pretraining-transformers.md ├── multihead-attention.md ├── queries-keys-values.md ├── self-attention-and-positional-encoding.md ├── transformer.md └── vision-transformer.md ├── chapter_builders-guide ├── custom-layer.md ├── index.md ├── init-param.md ├── lazy-init.md ├── model-construction.md ├── parameters.md ├── read-write.md └── use-gpu.md ├── chapter_computational-performance ├── async-computation.md ├── auto-parallelism.md ├── hardware.md ├── hybridize.md ├── index.md ├── multiple-gpus-concise.md ├── multiple-gpus.md └── parameterserver.md ├── chapter_computer-vision ├── anchor.md ├── bounding-box.md ├── fcn.md ├── fine-tuning.md ├── image-augmentation.md ├── index.md ├── kaggle-cifar10.md ├── kaggle-dog.md ├── multiscale-object-detection.md ├── neural-style.md ├── object-detection-dataset.md ├── rcnn.md ├── semantic-segmentation-and-dataset.md ├── ssd.md └── transposed-conv.md ├── chapter_convolutional-modern ├── alexnet.md ├── batch-norm.md ├── cnn-design.md ├── densenet.md ├── googlenet.md ├── index.md ├── nin.md ├── resnet.md └── vgg.md ├── chapter_convolutional-neural-networks ├── channels.md ├── conv-layer.md ├── index.md ├── lenet.md ├── padding-and-strides.md ├── pooling.md └── why-conv.md ├── chapter_gaussian-processes ├── gp-inference.md ├── gp-intro.md ├── gp-priors.md └── index.md ├── chapter_generative-adversarial-networks ├── dcgan.md ├── gan.md └── index.md ├── chapter_hyperparameter-optimization ├── hyperopt-api.md ├── hyperopt-intro.md ├── index.md ├── rs-async.md ├── sh-async.md └── sh-intro.md ├── chapter_installation └── index.md ├── chapter_introduction └── index.md ├── chapter_linear-classification ├── classification.md ├── environment-and-distribution-shift.md ├── generalization-classification.md ├── image-classification-dataset.md ├── index.md ├── softmax-regression-concise.md ├── softmax-regression-scratch.md └── softmax-regression.md ├── chapter_linear-regression ├── generalization.md ├── index.md ├── linear-regression-concise.md ├── linear-regression-scratch.md ├── linear-regression.md ├── oo-design.md ├── synthetic-regression-data.md └── weight-decay.md ├── chapter_multilayer-perceptrons ├── backprop.md ├── dropout.md ├── generalization-deep.md ├── index.md ├── kaggle-house-price.md ├── mlp-implementation.md ├── mlp.md └── numerical-stability-and-init.md ├── chapter_natural-language-processing-applications ├── finetuning-bert.md ├── index.md ├── natural-language-inference-and-dataset.md ├── natural-language-inference-attention.md ├── natural-language-inference-bert.md ├── sentiment-analysis-and-dataset.md ├── sentiment-analysis-cnn.md └── sentiment-analysis-rnn.md ├── chapter_natural-language-processing-pretraining ├── approx-training.md ├── bert-dataset.md ├── bert-pretraining.md ├── bert.md ├── glove.md ├── index.md ├── similarity-analogy.md ├── subword-embedding.md ├── word-embedding-dataset.md ├── word2vec-pretraining.md └── word2vec.md ├── chapter_notation └── index.md ├── chapter_optimization ├── adadelta.md ├── adagrad.md ├── adam.md ├── convexity.md ├── gd.md ├── index.md ├── lr-scheduler.md ├── minibatch-sgd.md ├── momentum.md ├── optimization-intro.md ├── rmsprop.md └── sgd.md ├── chapter_preface └── index.md ├── chapter_preliminaries ├── autograd.md ├── calculus.md ├── index.md ├── linear-algebra.md ├── lookup-api.md ├── ndarray.md ├── pandas.md └── probability.md ├── chapter_recommender-systems ├── autorec.md ├── ctr.md ├── deepfm.md ├── fm.md ├── index.md ├── mf.md ├── movielens.md ├── neumf.md ├── ranking.md ├── recsys-intro.md └── seqrec.md ├── chapter_recurrent-modern ├── beam-search.md ├── bi-rnn.md ├── deep-rnn.md ├── encoder-decoder.md ├── gru.md ├── index.md ├── lstm.md ├── machine-translation-and-dataset.md └── seq2seq.md ├── chapter_recurrent-neural-networks ├── bptt.md ├── index.md ├── language-model.md ├── rnn-concise.md ├── rnn-scratch.md ├── rnn.md ├── sequence.md └── text-sequence.md ├── chapter_references └── zreferences.md ├── chapter_reinforcement-learning ├── index.md ├── mdp.md ├── qlearning.md └── value-iter.md ├── ci ├── docker │ ├── Dockerfile.d2l-builder │ ├── Dockerfile.d2l-jax │ ├── Dockerfile.d2l-mxnet │ ├── Dockerfile.d2l-tf │ ├── Dockerfile.d2l-torch │ ├── d2l_job.sh │ ├── entry.sh │ ├── git-timesync │ ├── login_ecr.sh │ └── print_versions.py └── submit-job.py ├── config.ini ├── contrib ├── chapter_crashcourse │ └── chapter-one-problem-set.md ├── chapter_machine-learning-fundamentals │ ├── index.md │ ├── model-selection.md │ └── underfit-overfit.md ├── fasttext-pretraining.md ├── glove-pretraining.md ├── key-value.md ├── seq2seq.md └── similarity-analogy.md ├── d2l.bib ├── d2l ├── __init__.py ├── jax.py ├── mxnet.py ├── tensorflow.py └── torch.py ├── graffle ├── appendix │ ├── 3dFunc.graffle │ ├── ChainNet1.graffle │ ├── ChainNet2.graffle │ ├── GridPoints.graffle │ ├── GridTransform.graffle │ ├── GridTransformFilled.graffle │ ├── GridWithArrow.graffle │ ├── Marginal.graffle │ ├── ParVec.graffle │ ├── ProjVec.graffle │ ├── RectTrans.graffle │ ├── SpaceDivision.graffle │ ├── SpaceDivision3D.graffle │ ├── SubArea.graffle │ ├── SumOrder.graffle │ ├── VecAdd.graffle │ ├── VecAngle.graffle │ ├── comparing_estimators.graffle │ ├── mutual_information.graffle │ ├── negSecDer.graffle │ ├── posSecDer.graffle │ ├── statistical_power.graffle │ ├── statistical_significance.graffle │ └── zeroSecDer.graffle ├── attention │ ├── add_norm.graffle │ ├── attention-output.graffle │ ├── attention.graffle │ ├── bert-encoder-only.graffle │ ├── bert-finetune-classification.graffle │ ├── cnn-rnn-self-attention.graffle │ ├── encoder-decoder.graffle │ ├── eye-book.graffle │ ├── eye-coffee.graffle │ ├── gpt-3-xshot.graffle │ ├── gpt-decoder-only.graffle │ ├── gpt-finetune-classification.graffle │ ├── multi-head-attention.graffle │ ├── positional_encoding.graffle │ ├── qkv.graffle │ ├── self-attention-predict.graffle │ ├── self-attention.graffle │ ├── seq2seq-attention-details.graffle │ ├── seq2seq-details.graffle │ ├── seq2seq_attention.graffle │ ├── t5-encoder-decoder.graffle │ ├── t5-finetune-summarization.graffle │ ├── transformer.graffle │ └── vit.graffle ├── book-org.graffle ├── cnn-basic │ ├── conv-1x1.graffle │ ├── conv-multi-in.graffle │ ├── conv-pad.graffle │ ├── conv-reuse.graffle │ ├── conv-stride.graffle │ ├── correlation.graffle │ ├── lenet-vert.graffle │ ├── lenet.graffle │ ├── pooling.graffle │ └── waldo-mask.graffle │ │ ├── data.plist │ │ └── image1.jpg ├── cnn-modern │ ├── ResNetManyFlavor.graffle │ ├── alexnet.graffle │ ├── anynet-old.graffle │ ├── anynet.graffle │ ├── densenet-block.graffle │ ├── densenet.graffle │ ├── functionclasses.graffle │ ├── inception-full.graffle │ ├── inception.graffle │ ├── nin-compare.graffle │ ├── nin.graffle │ ├── residual-block.graffle │ ├── resnet-block.graffle │ ├── resnet18.graffle │ ├── resnext-block.graffle │ └── vgg.graffle ├── computation │ ├── asyncgraph.graffle │ ├── blocks.graffle │ ├── computegraph.graffle │ ├── copyto.graffle │ ├── frontends.graffle │ ├── threading.graffle │ └── twogpu.graffle ├── contribute.graffle ├── convert.sh ├── gan │ └── gan.graffle ├── hpo │ ├── asha.graffle │ ├── distributed_scheduling.graffle │ ├── ml_workflow.graffle │ └── sync_sh.graffle ├── intro │ ├── data-collection.graffle │ ├── diveintodl.graffle │ ├── ml-loop.graffle │ ├── rl-environment.graffle │ ├── supervised-learning.graffle │ └── wake-word.graffle ├── linear │ ├── fit_linreg.graffle │ ├── singlelayer.graffle │ ├── singleneuron.graffle │ └── softmaxreg.graffle ├── mlp │ ├── add_norm.graffle │ ├── capacity_vs_error.graffle │ ├── dropout2.graffle │ ├── forward.graffle │ └── mlp.graffle ├── nlp │ ├── bert-input.graffle │ ├── bert-one-seq.graffle │ ├── bert-qa.graffle │ ├── bert-tagging.graffle │ ├── bert-two-seqs.graffle │ ├── cbow.graffle │ ├── conv1d-2d.graffle │ ├── conv1d-channel.graffle │ ├── conv1d.graffle │ ├── elmo-gpt-bert.graffle │ ├── hi-softmax.graffle │ ├── nli_attention.graffle │ ├── nlp-map-app.graffle │ ├── nlp-map-nli-attention.graffle │ ├── nlp-map-nli-bert.graffle │ ├── nlp-map-pretrain.graffle │ ├── nlp-map-sa-cnn.graffle │ ├── nlp-map-sa-rnn.graffle │ ├── sentiment-rnn.graffle │ ├── skip-gram.graffle │ └── textcnn.graffle ├── optimization │ └── convex.graffle ├── performance │ ├── a77.graffle │ ├── data-parallel.graffle │ ├── falseshare.graffle │ ├── mobo.graffle │ │ ├── data.plist │ │ ├── image1.tiff │ │ └── preview.jpeg │ ├── neon128.graffle │ └── splitting.graffle ├── preliminaries │ └── polygon_circle.graffle ├── recsys │ ├── rec-caser.graffle │ ├── rec-deepfm.graffle │ ├── rec-intro.graffle │ ├── rec-mf.graffle │ ├── rec-neumf.graffle │ ├── rec-ranking.graffle │ └── rec-seq-data.graffle ├── rnn │ ├── beam-search.graffle │ ├── birnn-ORIGINAL.graffle │ ├── birnn.graffle │ ├── deep-rnn-ORIGINAL.graffle │ ├── deep-rnn.graffle │ ├── hmm.graffle │ ├── lang-model-data.graffle │ ├── rnn-bptt.graffle │ ├── rnn-train.graffle │ ├── rnn.graffle │ ├── s2s-prob1.graffle │ ├── s2s-prob2.graffle │ ├── seq2seq.graffle │ ├── seq2seq_predict.graffle │ ├── sequence-model.graffle │ ├── timemachine-5gram.graffle │ ├── truncated-bptt.graffle │ └── unfolded-rnn.graffle ├── transformer.graffle └── vision │ ├── anchor-label.graffle │ ├── fast-rcnn.graffle │ ├── faster-rcnn.graffle │ ├── fcn.graffle │ ├── finetune.graffle │ ├── iou.graffle │ ├── mask-rcnn.graffle │ ├── neural-style.graffle │ ├── r-cnn.graffle │ ├── roi.graffle │ ├── segmentation.graffle │ ├── ssd.graffle │ ├── style-transfer.graffle │ ├── trans_conv.graffle │ ├── trans_conv_2.graffle │ ├── trans_conv_pad1_2.graffle │ ├── trans_conv_stride2.graffle │ └── trans_conv_stride2_2.graffle ├── img ├── RL_main.png ├── a77.svg ├── alexnet-original.svg ├── alexnet.svg ├── anchor-label.svg ├── anynet.svg ├── asha.svg ├── asyncgraph.svg ├── attention-output.svg ├── attention.svg ├── autumn-oak.jpg ├── aws.png ├── banana.jpg ├── beam-search.svg ├── bert-encoder-only.svg ├── bert-finetune-classification.svg ├── bert-input.svg ├── bert-one-seq.svg ├── bert-qa.svg ├── bert-tagging.svg ├── bert-two-seqs.svg ├── birnn.svg ├── blocks.svg ├── book-org.svg ├── bw-hierarchy.svg ├── capacity-vs-error.svg ├── cat-dog-pixels.png ├── cat-dog-test.png ├── cat-dog-test.svg ├── cat-dog-train.png ├── cat-dog-train.svg ├── cat1.jpg ├── cat2.jpg ├── cat3.jpg ├── catdog.jpg ├── cbow.svg ├── chain-net1.svg ├── chain-net2.svg ├── chmod.png ├── cnn-rnn-self-attention.svg ├── colab-2.png ├── colab.png ├── computegraph.svg ├── connect.png ├── contribute.svg ├── conv-1x1.svg ├── conv-multi-in.svg ├── conv-pad.svg ├── conv-reuse.svg ├── conv-stride.svg ├── conv1d-2d.svg ├── conv1d-channel.svg ├── conv1d.svg ├── convex-intersect.svg ├── copyto.svg ├── correlation.svg ├── cuda101.png ├── cuda121.png ├── data-collection.svg ├── data-parallel.svg ├── death-cap.jpg ├── deep-rnn.svg ├── deeplearning-amazon.jpg ├── densenet-block.svg ├── densenet.svg ├── disk.png ├── distributed_scheduling.svg ├── dog1.jpg ├── dog2.jpg ├── dropout2.svg ├── ec2.png ├── edit-file.png ├── elmo-gpt-bert.svg ├── encoder-decoder.svg ├── example_anytime_performance.svg ├── eye-book.png ├── eye-coffee.png ├── falsesharing.svg ├── fast-rcnn.svg ├── faster-rcnn.svg ├── fcn.svg ├── field-visual.png ├── filters.png ├── finetune.svg ├── fit-linreg.svg ├── flopsvsprice.svg ├── forward.svg ├── frontends.png ├── frontends.svg ├── ftse100.png ├── functionclasses.svg ├── gan.svg ├── git-clone.png ├── git-createpr.png ├── git-fork.png ├── git-forked.png ├── git-newpr.png ├── gp-observed-data.svg ├── gp-point-predictions.svg ├── gp-post2.svg ├── gp-post5.svg ├── gp-posta2.svg ├── gp-posta8.svg ├── gp-postapoint1.svg ├── gp-posterior-samples-95.svg ├── gp-posterior-samples.svg ├── gp-postpoint1.svg ├── gp-postpoint5.svg ├── gp-prior2.svg ├── gp-prior5.svg ├── gp-priora2.svg ├── gp-priora8.svg ├── gp-priorap1.svg ├── gp-priorpoint1.svg ├── gp-priorpoint5.svg ├── gp-sample-posterior-functions.svg ├── gp-sample-prior-functions.svg ├── gpt-3-xshot.svg ├── gpt-decoder-only.svg ├── gpt-finetune-classification.svg ├── gpt3-xshot-scaling.png ├── grid-points.svg ├── grid-transform-filled.svg ├── grid-transform.svg ├── gru-1.svg ├── gru-2.svg ├── gru-3.svg ├── hb.svg ├── hi-softmax.svg ├── hmm.svg ├── house-pricing.png ├── imagen.png ├── inception-full-90.svg ├── inception-full.svg ├── inception.svg ├── iou.svg ├── jupyter.png ├── jupyter00.png ├── jupyter01.png ├── jupyter02.png ├── jupyter03.png ├── jupyter04.png ├── jupyter05.png ├── jupyter06.png ├── kaggle-cifar10.png ├── kaggle-dog.jpg ├── kaggle-submit2.png ├── kaggle.png ├── keypair.png ├── koebel.jpg ├── lang-model-data.svg ├── latencynumbers.png ├── launching.png ├── lenet-vert.svg ├── lenet.svg ├── limits.png ├── lstm-0.svg ├── lstm-1.svg ├── lstm-2.svg ├── lstm-3.svg ├── marginal.svg ├── mask-rcnn.svg ├── mdp.png ├── ml-loop.svg ├── ml_workflow.svg ├── mlp.svg ├── mobo-symbol.svg ├── multi-head-attention.svg ├── mutual-information.svg ├── negSecDer.svg ├── neon128.svg ├── neural-style.svg ├── neuron.svg ├── nin.svg ├── nli-attention.svg ├── nli_attention.svg ├── nlp-map-app.svg ├── nlp-map-nli-attention.svg ├── nlp-map-nli-bert.svg ├── nlp-map-pretrain.svg ├── nlp-map-sa-cnn.svg ├── nlp-map-sa-rnn.svg ├── nonconvex.svg ├── nvlink-twoloop.svg ├── nvlink.svg ├── p2x.png ├── pacman.svg ├── par-vec.svg ├── parti.png ├── pikachu.jpg ├── polygon-circle.svg ├── pooling.svg ├── popvssoda.png ├── posSecDer.svg ├── proj-vec.svg ├── projections.svg ├── ps-distributed.svg ├── ps-multimachine.svg ├── ps-multips.svg ├── ps.svg ├── qkv.svg ├── r-cnn.svg ├── rainier.jpg ├── rec-caser.svg ├── rec-deepfm.svg ├── rec-intro.svg ├── rec-mf.svg ├── rec-neumf.svg ├── rec-ranking.svg ├── rec-seq-data.svg ├── rect-trans.svg ├── regnet-fig.png ├── regnet-paper-fig5.png ├── regnet-paper-fig7.png ├── residual-block.svg ├── resnet-block.svg ├── resnet18-90.svg ├── resnet18.svg ├── resnext-block.svg ├── ringsync.svg ├── rl-environment.svg ├── rnn-bptt.svg ├── rnn-train.svg ├── rnn.svg ├── roi.svg ├── s2s-prob1.svg ├── s2s-prob2.svg ├── sagemaker-create-2.png ├── sagemaker-create-3.png ├── sagemaker-create.png ├── sagemaker-open.png ├── sagemaker-stop.png ├── sagemaker-terminal.png ├── sagemaker.png ├── samples_lc.svg ├── scaling-gpt3.png ├── scaling-power-law.png ├── scaling-sample-conv.png ├── segmentation.svg ├── self-attention.svg ├── seq2seq-attention-details.svg ├── seq2seq-attention.svg ├── seq2seq-details-attention.svg ├── seq2seq-details.svg ├── seq2seq-predict.svg ├── seq2seq-state.svg ├── seq2seq.svg ├── sequence-model.svg ├── sh.png ├── sh.svg ├── singleneuron.svg ├── skip-gram.svg ├── skylake.svg ├── softmaxreg.svg ├── space-division-3d.svg ├── space-division.svg ├── speech.png ├── splitting.svg ├── ssd.svg ├── stackedanimals.png ├── statistical-significance.svg ├── style-transfer.svg ├── sub-area.svg ├── successive-halving.svg ├── sum-order.svg ├── supervised-learning.svg ├── sync_sh.svg ├── t5-encoder-decoder.svg ├── t5-finetune-summarization.svg ├── tensorcore.jpg ├── textcnn.svg ├── threading.svg ├── timemachine-5gram.svg ├── trans_conv.svg ├── trans_conv_stride2.svg ├── transformer.svg ├── truncated-bptt.svg ├── turing-processing-block.png ├── turing.png ├── twogpu.svg ├── ubuntu-new.png ├── unfolded-rnn.svg ├── vec-add.svg ├── vec-angle.svg ├── vgg.svg ├── vit.svg ├── wake-word.svg ├── waldo-football.jpg ├── waldo-mask.jpg ├── wattvsprice.svg ├── where-wally-walker-books.jpg └── zeroSecDer.svg ├── index.md ├── setup.py ├── static ├── build.yml ├── cache.sh ├── clean_img.sh ├── d2l.css ├── favicon-blue-background.jpg ├── favicon.png ├── frontpage │ ├── _images │ │ ├── aaron.jpg │ │ ├── alex.jpg │ │ ├── andrew.jpg │ │ ├── anirudh.jpg │ │ ├── aston.jpg │ │ ├── brent.jpg │ │ ├── cedric.png │ │ ├── code.jpg │ │ ├── eq.jpg │ │ ├── figure.jpg │ │ ├── forum.gif │ │ ├── forum.jpg │ │ ├── forum.mp4 │ │ ├── front-cup.jpg │ │ ├── front.png │ │ ├── kavosh.jpg │ │ ├── laptop_jupyter.png │ │ ├── logos │ │ │ ├── Alexandria-University.svg │ │ │ ├── Amity-University.png │ │ │ ├── Ateneo-de-Naga-University.png │ │ │ ├── Australian-National-University.png │ │ │ ├── Bar-Ilan-University.svg │ │ │ ├── Barnard-College.svg │ │ │ ├── Birla-Institute-of-Technology-and-Science,-Hyderabad.svg │ │ │ ├── Boston-University.svg │ │ │ ├── Brandeis-University.svg │ │ │ ├── Brown-University.png │ │ │ ├── Cairo-University.png │ │ │ ├── Carnegie-Mellon-University.png │ │ │ ├── College-of-Engineering-Pune.png │ │ │ ├── Columbia-University.png │ │ │ ├── Cornell-University.svg │ │ │ ├── Cyprus-Institute.png │ │ │ ├── Duke-University.svg │ │ │ ├── Durban-University-of-Technology.png │ │ │ ├── Ecole-Nationale-Superieure-dInformatique.png │ │ │ ├── Emory-University.png │ │ │ ├── Eotvos-Lorand-University.png │ │ │ ├── Escuela-Politecnica-Nacional.png │ │ │ ├── Federal-University-Lokoja.png │ │ │ ├── Fudan-University.svg │ │ │ ├── Gayatri-Vidya-Parishad-College-of-Engineering-(Autonomous).png │ │ │ ├── Gazi-Universitesi.png │ │ │ ├── George-Mason-University.svg │ │ │ ├── Georgetown-University.svg │ │ │ ├── Georgia-Institute-of-Technology.svg │ │ │ ├── Golden-Gate-University.png │ │ │ ├── Habib-University.png │ │ │ ├── Hamad-Bin-Khalifa-University.png │ │ │ ├── Hangzhou-Dianzi-University.png │ │ │ ├── Hankuk-University-of-Foreign-Studies.png │ │ │ ├── Harbin-Institute-of-Technology.png │ │ │ ├── Harvard-University.png │ │ │ ├── Hasso-Plattner-Institut.png │ │ │ ├── Heinrich-Heine-Universitat-Dusseldorf.svg │ │ │ ├── Hertie-School.png │ │ │ ├── Hiroshima-University.png │ │ │ ├── Ho-Chi-Minh-City-University-of-Foreign-Languages-and-Information-Technology.png │ │ │ ├── Hochschule-Bremen.png │ │ │ ├── Hochschule-fur-Technik-und-Wirtschaft.svg │ │ │ ├── Hong-Kong-University-of-Science-and-Technology.png │ │ │ ├── Huazhong-University-of-Science-and-Technology.png │ │ │ ├── IMT-Mines-Ales.png │ │ │ ├── IT-Universitetet-i-Kobenhavn.svg │ │ │ ├── Imperial-College-London.svg │ │ │ ├── Indian-Institute-of-Technology-Bombay.png │ │ │ ├── Indian-Institute-of-Technology-Hyderabad.png │ │ │ ├── Indian-Institute-of-Technology-Jodhpur.png │ │ │ ├── Indian-Institute-of-Technology-Kanpur.svg │ │ │ ├── Indian-Institute-of-Technology-Kharagpur.svg │ │ │ ├── Indian-Institute-of-Technology-Mandi.png │ │ │ ├── Indian-Institute-of-Technology-Ropar.png │ │ │ ├── Indira-Gandhi-National-Open-University.svg │ │ │ ├── Indraprastha-Institute-of-Information-Technology,-Delhi.png │ │ │ ├── Institut-Superieur-De-L'electronique-Et-Du-Numerique.png │ │ │ ├── Institut-Superieur-d'Informatique-et-des-Techniques-de-Communication.png │ │ │ ├── Institut-Teknologi-Bandung.svg │ │ │ ├── Institut-catholique-d'arts-et-metiers-(ICAM).svg │ │ │ ├── Institut-de-recherche-en-informatique-de-Toulouse.svg │ │ │ ├── Instituto-Tecnologico-Autonomo-de-Mexico.png │ │ │ ├── Instituto-Tecnologico-de-Buenos-Aires.png │ │ │ ├── Istanbul-Teknik-Universitesi.svg │ │ │ ├── Johns-Hopkins-University.png │ │ │ ├── KPR-Institute-of-Engineering-and-Technology.png │ │ │ ├── Keio-University.png │ │ │ ├── King-Abdullah-University-of-Science-and-Technology.svg │ │ │ ├── King-Fahd-University-of-Petroleum-and-Minerals.svg │ │ │ ├── Kongu-Engineering-College.png │ │ │ ├── Kyungpook-National-University.png │ │ │ ├── Lancaster-University.png │ │ │ ├── Leading-Unviersity.png │ │ │ ├── Leibniz-Universitat-Hannover.png │ │ │ ├── Leuphana-University-of-Luneburg.png │ │ │ ├── London-School-of-Economics-&-Political-Science.png │ │ │ ├── Make-School.png │ │ │ ├── Masaryk-University.svg │ │ │ ├── Massachusetts-Institute-of-Technology.svg │ │ │ ├── McGill-University.png │ │ │ ├── Menoufia-University.png │ │ │ ├── Milwaukee-School-of-Engineering.svg │ │ │ ├── Minia-University.png │ │ │ ├── Mohammed-V-University-in-Rabat.png │ │ │ ├── Monash-University.png │ │ │ ├── Multimedia-University.png │ │ │ ├── Nanjing-University.png │ │ │ ├── National-Chung-Hsing-University.png │ │ │ ├── National-Institute-of-Technical-Teachers-Training-&-Research.png │ │ │ ├── National-Institute-of-Technology,-Warangal.png │ │ │ ├── National-Sun-Yat-sen-University.png │ │ │ ├── National-Taiwan-University.png │ │ │ ├── National-Technical-University-of-Athens.png │ │ │ ├── National-Technical-University-of-Ukraine.png │ │ │ ├── National-United-University.svg │ │ │ ├── National-University-of-Singapore.png │ │ │ ├── Nazarbayev-University.png │ │ │ ├── New-York-University.png │ │ │ ├── Newman-University.png │ │ │ ├── North-Ossetian-State-University.png │ │ │ ├── Northeastern-University.svg │ │ │ ├── Northwestern-University.png │ │ │ ├── Ohio-University.svg │ │ │ ├── Pakuan-University.png │ │ │ ├── Peking-University.svg │ │ │ ├── Pennsylvania-State-University.png │ │ │ ├── Pohang-University-of-Science-and-Technology.png │ │ │ ├── Politecnico-di-Milano.png │ │ │ ├── Pontificia-Universidad-Catolica-de-Chile.svg │ │ │ ├── Pontificia-Universidad-Catolica-del-Peru.png │ │ │ ├── Portland-State-University.svg │ │ │ ├── Purdue-University.svg │ │ │ ├── Quaid-e-Azam-University.png │ │ │ ├── Queen's-University.png │ │ │ ├── Radboud-Universiteit.png │ │ │ ├── Rowan-University.svg │ │ │ ├── Rutgers,-The-State-University-of-New-Jersey.svg │ │ │ ├── Santa-Clara-University.svg │ │ │ ├── Sapienza-Universita-di-Roma.png │ │ │ ├── Seoul-National-University-of-Science-and-Technology.png │ │ │ ├── Seoul-National-University.svg │ │ │ ├── Shanghai-Jiao-Tong-University.png │ │ │ ├── Shanghai-University-of-Finance-and-Economics.png │ │ │ ├── Sharif-University-of-Technology.svg │ │ │ ├── Shenzhen-University.svg │ │ │ ├── Simon-Fraser-University.png │ │ │ ├── Singapore-University-of-Technology-and-Design.png │ │ │ ├── Sogang-University.png │ │ │ ├── Southern-New-Hampshire-University.svg │ │ │ ├── St.-Polten-University-of-Applied-Sciences.svg │ │ │ ├── Stanford-University.png │ │ │ ├── State-University-of-New-York-at-Binghamton.png │ │ │ ├── Stevens-Institute-of-Technology.svg │ │ │ ├── Sungkyunkwan-University.svg │ │ │ ├── Technion---Israel-Institute-of-Technology.svg │ │ │ ├── Technische-Universitat-Berlin.svg │ │ │ ├── Technische-Universiteit-Delft.png │ │ │ ├── Tekirdag-Namik-Kemal-Universitesi.png │ │ │ ├── Texas-A&M-University.png │ │ │ ├── Thapar-Institute-of-Engineering-and-Technology.png │ │ │ ├── Tsinghua-University.png │ │ │ ├── Tufts-University.svg │ │ │ ├── Umea-University.png │ │ │ ├── Universidad-Carlos-III-de-Madrid.svg │ │ │ ├── Universidad-Militar-Nueva-Granada.svg │ │ │ ├── Universidad-Nacional-Agraria-La-Molina.png │ │ │ ├── Universidad-Nacional-Autonoma-de-Mexico.svg │ │ │ ├── Universidad-Nacional-de-Colombia-Sede-Manizales.png │ │ │ ├── Universidad-Nacional-de-Tierra-del-Fuego.png │ │ │ ├── Universidad-San-Francisco-de-Quito.png │ │ │ ├── Universidad-Tecnologica-de-Pereira.png │ │ │ ├── Universidad-de-Zaragoza.png │ │ │ ├── Universidade-Catolica-de-Brasilia.png │ │ │ ├── Universidade-Estadual-de-Campinas.svg │ │ │ ├── Universidade-Federal-de-Minas-Gerais.jpg │ │ │ ├── Universidade-Federal-de-Ouro-Preto.png │ │ │ ├── Universidade-Federal-do-Rio-Grande.svg │ │ │ ├── Universidade-NOVA-de-Lisboa.png │ │ │ ├── Universidade-Presbiteriana-Mackenzie.png │ │ │ ├── Universita-degli-Studi-di-Bari-Aldo-Moro.png │ │ │ ├── Universita-degli-Studi-di-Brescia.svg │ │ │ ├── Universita-degli-Studi-di-Catania.png │ │ │ ├── Universita-degli-Studi-di-Padova.svg │ │ │ ├── Universitas-Andalas,-Padang.svg │ │ │ ├── Universitas-Udayana.png │ │ │ ├── Universitat-Heidelberg.png │ │ │ ├── Universitat-Politecnica-de-Catalunya.svg │ │ │ ├── Universitat-de-Barcelona.png │ │ │ ├── Universitatea-Babes-Bolyai.png │ │ │ ├── Universitatea-de-Vest-din-Timisoara.png │ │ │ ├── Universite-Cote-dAzur.png │ │ │ ├── Universite-Paris-Saclay.svg │ │ │ ├── Universite-de-technologie-de-Compiegne.png │ │ │ ├── University-of-Arkansas.svg │ │ │ ├── University-of-Augsburg.svg │ │ │ ├── University-of-British-Columbia.png │ │ │ ├── University-of-California,-Berkeley.png │ │ │ ├── University-of-California,-Irvine.svg │ │ │ ├── University-of-California,-Los-Angeles.png │ │ │ ├── University-of-California,-San-Diego.png │ │ │ ├── University-of-California,-Santa-Barbara.png │ │ │ ├── University-of-California,-Santa-Cruz.svg │ │ │ ├── University-of-Cambridge.png │ │ │ ├── University-of-Canberra.png │ │ │ ├── University-of-Cincinnati.png │ │ │ ├── University-of-Connecticut.svg │ │ │ ├── University-of-Copenhagen.svg │ │ │ ├── University-of-Florida.svg │ │ │ ├── University-of-Ghana.png │ │ │ ├── University-of-Groningen.png │ │ │ ├── University-of-Hamburg.svg │ │ │ ├── University-of-Hull.svg │ │ │ ├── University-of-Iceland.svg │ │ │ ├── University-of-Idaho.svg │ │ │ ├── University-of-Illinois-at-Urbana-Champaign.png │ │ │ ├── University-of-International-Business-and-Economics.png │ │ │ ├── University-of-Klagenfurt.png │ │ │ ├── University-of-Liege.svg │ │ │ ├── University-of-Maryland.svg │ │ │ ├── University-of-Michigan.png │ │ │ ├── University-of-Minnesota,-Twin-Cities.svg │ │ │ ├── University-of-Moratuwa.png │ │ │ ├── University-of-New-Hampshire.png │ │ │ ├── University-of-Newcastle.png │ │ │ ├── University-of-North-Carolina-at-Chapel-Hill.svg │ │ │ ├── University-of-North-Texas.png │ │ │ ├── University-of-Northern-Philippines.png │ │ │ ├── University-of-Nottingham.png │ │ │ ├── University-of-Oslo.png │ │ │ ├── University-of-Pennsylvania.png │ │ │ ├── University-of-Pittsburgh.svg │ │ │ ├── University-of-Sao-Paulo.png │ │ │ ├── University-of-Science-and-Technology-of-China.svg │ │ │ ├── University-of-Southern-California.png │ │ │ ├── University-of-Southern-Maine.svg │ │ │ ├── University-of-St-Andrews.svg │ │ │ ├── University-of-Szeged.png │ │ │ ├── University-of-Technology-Sydney.png │ │ │ ├── University-of-Tehran.svg │ │ │ ├── University-of-Texas-at-Austin.svg │ │ │ ├── University-of-Warsaw.png │ │ │ ├── University-of-Washington.png │ │ │ ├── University-of-Waterloo.png │ │ │ ├── University-of-Wisconsin-Madison.png │ │ │ ├── Univerzita-Komenskeho-v-Bratislave.png │ │ │ ├── Vardhaman-College-of-Engineering.png │ │ │ ├── Vardhman-Mahaveer-Open-University.png │ │ │ ├── Vietnamese-German-University.png │ │ │ ├── Wageningen-University.png │ │ │ ├── West-Virginia-University.svg │ │ │ ├── Western-University.png │ │ │ ├── Xavier-University-Bhubaneswar.png │ │ │ ├── Xi'an-Jiaotong-Liverpool-University.png │ │ │ ├── Xiamen-University.svg │ │ │ ├── Yale-University.svg │ │ │ ├── Yeshiva-University.png │ │ │ ├── Yonsei-University.svg │ │ │ ├── Yunnan-University.png │ │ │ ├── Zhejiang-University.svg │ │ │ ├── colab.png │ │ │ ├── hkust.gif │ │ │ ├── logoimg1.png │ │ │ ├── logoimg2.png │ │ │ ├── logoimg3.png │ │ │ ├── logoimg4.png │ │ │ ├── logoimg5.png │ │ │ ├── logoimg6.png │ │ │ ├── logoimg7.png │ │ │ ├── sagemaker-studio-lab.png │ │ │ └── sagemaker.png │ │ ├── map.png │ │ ├── map2.png │ │ ├── map3.png │ │ ├── matthias.jpg │ │ ├── mu.jpg │ │ ├── notebook.gif │ │ ├── notebook.jpg │ │ ├── notebook.mp4 │ │ ├── pratik.jpg │ │ ├── rachel.jpeg │ │ ├── rasool.jpg │ │ ├── shuai.jpg │ │ ├── yi.jpg │ │ ├── yuan.jpg │ │ └── zack.jpg │ ├── attachments │ │ ├── hardcopy.txt │ │ └── sagemaker.txt │ └── frontpage.html ├── latex-logo.png ├── latex_style │ ├── PT1header.eps │ ├── PT1secrule.pdf │ ├── sphinxlatexindbibtoc.sty │ ├── sphinxlatexlists.sty │ ├── sphinxlatexobjects.sty │ ├── sphinxlatexstyleheadings.sty │ ├── sphinxlatexstylepage.sty │ ├── sphinxmanual.cls │ ├── sphinxmessages.sty │ ├── sphinxoptionsgeometry.sty │ └── sphinxpackagefootnote.sty ├── logo-with-text.png ├── logo.png ├── post_latex │ └── main.py └── vol1 │ ├── root-index.md │ ├── tools-index.md │ └── vol1-config.ini └── vol1-activate.sh /.gitattributes: -------------------------------------------------------------------------------- 1 | * text=auto eol=lf 2 | -------------------------------------------------------------------------------- /.github/PULL_REQUEST_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | *Description of changes:* 2 | 3 | 4 | By submitting this pull request, I confirm that you can use, modify, 5 | copy, and redistribute this contribution, under the terms of your 6 | choice. 7 | -------------------------------------------------------------------------------- /.github/actions/setup_env_vars/action.yml: -------------------------------------------------------------------------------- 1 | name: "Setup Env Vars" 2 | 3 | runs: 4 | using: "composite" 5 | steps: 6 | - name: Get Commit SHA (For Push Event) 7 | if: ${{ github.event_name == 'push' }} 8 | shell: bash 9 | env: 10 | SHA: ${{ github.sha }} 11 | git_repo_full: ${{ github.repository }} 12 | pr_number: "" 13 | run: | 14 | short_sha=$(git rev-parse --short "$SHA") 15 | echo "SHORT_SHA=$short_sha" >> $GITHUB_ENV 16 | echo "PR_NUMBER=$pr_number" >> $GITHUB_ENV 17 | 18 | target_branch=${GITHUB_REF##*/} 19 | echo "TARGET_BRANCH=$target_branch" >> $GITHUB_ENV 20 | 21 | repo_name=${git_repo_full##*/} 22 | echo "REPO_NAME=$repo_name" >> $GITHUB_ENV 23 | 24 | IFS='-' read -ra name_parts <<< "$repo_name" 25 | echo "LANG=${name_parts[1]}" >> $GITHUB_ENV 26 | 27 | job_name=${repo_name}/${target_branch} 28 | echo "JOB_NAME=$job_name" >> $GITHUB_ENV 29 | 30 | echo "CACHE_DIR=ci_cache_push" >> $GITHUB_ENV 31 | 32 | 33 | - name: Get Commit SHA (For Pull Request) 34 | if: ${{ github.event_name == 'pull_request_target' }} 35 | shell: bash 36 | env: 37 | SHA: ${{ github.event.pull_request.head.sha }} 38 | target_branch: ${{ github.event.pull_request.base.ref }} 39 | git_repo_full: ${{ github.event.pull_request.base.repo.full_name }} 40 | pr_number: PR-${{ github.event.number }} 41 | run: | 42 | short_sha=$(git rev-parse --short "$SHA") 43 | echo "SHORT_SHA=$short_sha" >> $GITHUB_ENV 44 | echo "PR_NUMBER=$pr_number" >> $GITHUB_ENV 45 | 46 | echo "TARGET_BRANCH=$target_branch" >> $GITHUB_ENV 47 | 48 | repo_name=${git_repo_full##*/} 49 | echo "REPO_NAME=$repo_name" >> $GITHUB_ENV 50 | 51 | IFS='-' read -ra name_parts <<< "$repo_name" 52 | echo "LANG=${name_parts[1]}" >> $GITHUB_ENV 53 | 54 | job_name=${repo_name}/${pr_number}/${short_sha} 55 | echo "JOB_NAME=$job_name" >> $GITHUB_ENV 56 | 57 | echo "CACHE_DIR=ci_cache_pr" >> $GITHUB_ENV 58 | -------------------------------------------------------------------------------- /.github/workflow_scripts/build_and_deploy.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Move all scripts related to html here! 4 | 5 | set -ex 6 | 7 | REPO_NAME="$1" # Eg. 'd2l-en' 8 | TARGET_BRANCH="$2" # Eg. 'master' ; if PR raised to master 9 | JOB_NAME="$3" # Eg. 'd2l-en/master' or 'd2l-en/PR-2453/21be1a4' 10 | LANG="$4" # Eg. 'en','zh' etc. 11 | CACHE_DIR="$5" # Eg. 'ci_cache_pr' or 'ci_cache_push' 12 | 13 | pip3 install . 14 | mkdir _build 15 | 16 | source $(dirname "$0")/utils.sh 17 | 18 | # Move aws copy commands for cache restore outside 19 | measure_command_time "aws s3 sync s3://preview.d2l.ai/"$CACHE_DIR"/"$REPO_NAME"-"$TARGET_BRANCH"/_build _build --delete --quiet --exclude 'eval*/data/*'" 20 | 21 | # Build D2L Website 22 | ./.github/workflow_scripts/build_html.sh $TARGET_BRANCH $JOB_NAME 23 | 24 | # Build PDFs 25 | d2lbook build pdf 26 | d2lbook build pdf --tab mxnet 27 | 28 | 29 | # Check if the JOB_NAME is either "$REPO_NAME/release" or "$REPO_NAME/classic" 30 | if [[ "$JOB_NAME" == "$REPO_NAME/release" || "$JOB_NAME" == "$REPO_NAME/classic" ]]; then 31 | 32 | # Setup D2L Bot 33 | source $(dirname "$0")/setup_git.sh 34 | setup_git 35 | 36 | # Run d2lbook release deployment 37 | if [[ "$JOB_NAME" == *"/classic" ]]; then 38 | # Use classic s3 bucket for classic release 39 | LANG="classic" 40 | fi 41 | d2lbook build pkg 42 | d2lbook deploy html pdf pkg colab sagemaker slides --s3 "s3://${LANG}.d2l.ai/" 43 | 44 | else 45 | # Run d2lbook preview deployment 46 | d2lbook deploy html pdf --s3 "s3://preview.d2l.ai/${JOB_NAME}/" 47 | fi 48 | 49 | # Move aws copy commands for cache store outside 50 | measure_command_time "aws s3 sync _build s3://preview.d2l.ai/"$CACHE_DIR"/"$REPO_NAME"-"$TARGET_BRANCH"/_build --acl public-read --quiet --exclude 'eval*/data/*'" 51 | -------------------------------------------------------------------------------- /.github/workflow_scripts/build_html.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | set -e 4 | 5 | # Read arguments 6 | BRANCH_NAME=$1 7 | JOB_NAME=$2 8 | 9 | STABLE_BASE_PATH="https://d2l.ai" 10 | PREVIEW_BASE_PATH="http://preview.d2l.ai/$JOB_NAME" 11 | 12 | # Generate Headers 13 | if [[ "$BRANCH_NAME" == "release" ]]; then 14 | echo "Use release headers" 15 | alternate_text="Preview Version" 16 | alternate_base="${PREVIEW_BASE_PATH/release/master}" # Substitute "release" with "master" 17 | current_base=$STABLE_BASE_PATH 18 | else 19 | echo "Use ${JOB_NAME} headers" 20 | alternate_text="Stable Version" 21 | alternate_base=$STABLE_BASE_PATH 22 | current_base=$PREVIEW_BASE_PATH 23 | fi 24 | 25 | 26 | # Replace placeholders in ../config.ini 27 | sed -i -e "s@###_ALTERNATE_VERSION_###@$alternate_text@g" ./config.ini 28 | sed -i -e "s@###_ALTERNATE_VERSION_BASE_LINK_###@$alternate_base@g" ./config.ini 29 | sed -i -e "s@###_CURRENT_VERSION_BASE_LINK_###@$current_base@g" ./config.ini 30 | 31 | 32 | rm -rf _build/rst _build/html 33 | d2lbook build rst --tab all 34 | cp static/frontpage/frontpage.html _build/rst_all/ 35 | d2lbook build html --tab all 36 | cp -r static/frontpage/_images/* _build/html/_images/ 37 | 38 | for fn in `find _build/html/_images/ -iname '*.svg' `; do 39 | if [[ $fn == *'qr_'* ]] ; then # || [[ $fn == *'output_'* ]] 40 | continue 41 | fi 42 | # rsvg-convert installed on ubuntu changes unit from px to pt, so evening no 43 | # change of the size makes the svg larger... 44 | rsvg-convert -z 1 -f svg -o tmp.svg $fn 45 | mv tmp.svg $fn 46 | done 47 | 48 | # Add SageMaker Studio Lab buttons 49 | for f in _build/html/chapter*/*.html; do 50 | sed -i s/Open\ the\ notebook\ in\ Colab\<\\\/div\>\<\\\/div\>\<\\\/div\>\<\\\/h1\>/Open\ the\ notebook\ in\ Colab\<\\\/div\>\<\\\/div\>\<\\\/div\>\\ \\ \\<\\\/i\>\ SageMaker\ Studio\ Lab\ \<\\\/button\>\<\\\/a\>\\ Open\ the\ notebook\ in\ SageMaker\ Studio\ Lab\<\\\/div\>\<\\\/h1\>/g $f 51 | done 52 | 53 | -------------------------------------------------------------------------------- /.github/workflow_scripts/build_jax.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | set -ex 4 | 5 | # Used to capture status exit of build eval command 6 | ss=0 7 | 8 | REPO_NAME="$1" # Eg. 'd2l-en' 9 | TARGET_BRANCH="$2" # Eg. 'master' ; if PR raised to master 10 | CACHE_DIR="$3" # Eg. 'ci_cache_pr' or 'ci_cache_push' 11 | 12 | pip3 install . 13 | mkdir _build 14 | 15 | source $(dirname "$0")/utils.sh 16 | 17 | # Move sanity check outside 18 | d2lbook build outputcheck tabcheck 19 | 20 | # Move aws copy commands for cache restore outside 21 | if [ "$DISABLE_CACHE" = "false" ]; then 22 | echo "Retrieving jax build cache from "$CACHE_DIR"" 23 | measure_command_time "aws s3 sync s3://preview.d2l.ai/"$CACHE_DIR"/"$REPO_NAME"-"$TARGET_BRANCH"/_build/eval_jax/ _build/eval_jax/ --delete --quiet --exclude 'data/*'" 24 | fi 25 | 26 | export XLA_PYTHON_CLIENT_MEM_FRACTION=.70 27 | export TF_CPP_MIN_LOG_LEVEL=3 28 | export TF_FORCE_GPU_ALLOW_GROWTH=true 29 | # Continue the script even if some notebooks in build fail to 30 | # make sure that cache is copied to s3 for the successful notebooks 31 | d2lbook build eval --tab jax || ((ss=1)) 32 | 33 | # Move aws copy commands for cache store outside 34 | echo "Upload jax build cache to s3" 35 | measure_command_time "aws s3 sync _build s3://preview.d2l.ai/"$CACHE_DIR"/"$REPO_NAME"-"$TARGET_BRANCH"/_build --acl public-read --quiet --exclude 'eval*/data/*'" 36 | 37 | # Exit with a non-zero status if evaluation failed 38 | if [ "$ss" -ne 0 ]; then 39 | exit 1 40 | fi 41 | -------------------------------------------------------------------------------- /.github/workflow_scripts/build_mxnet.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | set -ex 4 | 5 | # Used to capture status exit of build eval command 6 | ss=0 7 | 8 | REPO_NAME="$1" # Eg. 'd2l-en' 9 | TARGET_BRANCH="$2" # Eg. 'master' ; if PR raised to master 10 | CACHE_DIR="$3" # Eg. 'ci_cache_pr' or 'ci_cache_push' 11 | 12 | pip3 install . 13 | mkdir _build 14 | 15 | source $(dirname "$0")/utils.sh 16 | 17 | # Move sanity check outside 18 | d2lbook build outputcheck tabcheck 19 | 20 | # Move aws copy commands for cache restore outside 21 | if [ "$DISABLE_CACHE" = "false" ]; then 22 | echo "Retrieving mxnet build cache from "$CACHE_DIR"" 23 | measure_command_time "aws s3 sync s3://preview.d2l.ai/"$CACHE_DIR"/"$REPO_NAME"-"$TARGET_BRANCH"/_build/eval_mxnet/ _build/eval_mxnet/ --delete --quiet --exclude 'data/*'" 24 | fi 25 | 26 | # MXNet training for the following notebooks is slow in the container; 27 | # Setting NTHREADS=4 below seems to fix the issue: 28 | # 1. chapter_multilayer-perceptrons/dropout.md 29 | # 2. chapter_multilayer-perceptrons/mlp-implementation.md 30 | # 3. chapter_linear-classification/softmax-regression-concise.md 31 | # 4. chapter_linear-classification/softmax-regression-scratch.md 32 | export MXNET_CPU_WORKER_NTHREADS=4 33 | # Continue the script even if some notebooks in build fail to 34 | # make sure that cache is copied to s3 for the successful notebooks 35 | d2lbook build eval --tab mxnet || ((ss=1)) 36 | 37 | # Move aws copy commands for cache store outside 38 | echo "Upload mxnet build cache to s3" 39 | measure_command_time "aws s3 sync _build s3://preview.d2l.ai/"$CACHE_DIR"/"$REPO_NAME"-"$TARGET_BRANCH"/_build --acl public-read --quiet --exclude 'eval*/data/*'" 40 | 41 | # Exit with a non-zero status if evaluation failed 42 | if [ "$ss" -ne 0 ]; then 43 | exit 1 44 | fi 45 | -------------------------------------------------------------------------------- /.github/workflow_scripts/build_pytorch.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | set -ex 4 | 5 | # Used to capture status exit of build eval command 6 | ss=0 7 | 8 | REPO_NAME="$1" # Eg. 'd2l-en' 9 | TARGET_BRANCH="$2" # Eg. 'master' ; if PR raised to master 10 | CACHE_DIR="$3" # Eg. 'ci_cache_pr' or 'ci_cache_push' 11 | 12 | pip3 install . 13 | mkdir _build 14 | 15 | source $(dirname "$0")/utils.sh 16 | 17 | # Move sanity check outside 18 | d2lbook build outputcheck tabcheck 19 | 20 | # Move aws copy commands for cache restore outside 21 | if [ "$DISABLE_CACHE" = "false" ]; then 22 | echo "Retrieving pytorch build cache from "$CACHE_DIR"" 23 | measure_command_time "aws s3 sync s3://preview.d2l.ai/"$CACHE_DIR"/"$REPO_NAME"-"$TARGET_BRANCH"/_build/eval _build/eval --delete --quiet --exclude 'data/*'" 24 | echo "Retrieving pytorch slides cache from "$CACHE_DIR"" 25 | aws s3 sync s3://preview.d2l.ai/"$CACHE_DIR"/"$REPO_NAME"-"$TARGET_BRANCH"/_build/slides _build/slides --delete --quiet --exclude 'data/*' 26 | fi 27 | 28 | # Continue the script even if some notebooks in build fail to 29 | # make sure that cache is copied to s3 for the successful notebooks 30 | d2lbook build eval || ((ss=1)) 31 | d2lbook build slides --tab pytorch 32 | 33 | # Move aws copy commands for cache store outside 34 | echo "Upload pytorch build cache to s3" 35 | measure_command_time "aws s3 sync _build s3://preview.d2l.ai/"$CACHE_DIR"/"$REPO_NAME"-"$TARGET_BRANCH"/_build --acl public-read --quiet --exclude 'eval*/data/*'" 36 | 37 | # Exit with a non-zero status if evaluation failed 38 | if [ "$ss" -ne 0 ]; then 39 | exit 1 40 | fi 41 | -------------------------------------------------------------------------------- /.github/workflow_scripts/build_tf.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | set -ex 4 | 5 | # Used to capture status exit of build eval command 6 | ss=0 7 | 8 | REPO_NAME="$1" # Eg. 'd2l-en' 9 | TARGET_BRANCH="$2" # Eg. 'master' ; if PR raised to master 10 | CACHE_DIR="$3" # Eg. 'ci_cache_pr' or 'ci_cache_push' 11 | 12 | pip3 install . 13 | mkdir _build 14 | 15 | source $(dirname "$0")/utils.sh 16 | 17 | # Move sanity check outside 18 | d2lbook build outputcheck tabcheck 19 | 20 | # Move aws copy commands for cache restore outside 21 | if [ "$DISABLE_CACHE" = "false" ]; then 22 | echo "Retrieving tensorflow build cache from "$CACHE_DIR"" 23 | measure_command_time "aws s3 sync s3://preview.d2l.ai/"$CACHE_DIR"/"$REPO_NAME"-"$TARGET_BRANCH"/_build/eval_tensorflow/ _build/eval_tensorflow/ --delete --quiet --exclude 'data/*'" 24 | fi 25 | 26 | export TF_CPP_MIN_LOG_LEVEL=3 27 | export TF_FORCE_GPU_ALLOW_GROWTH=true 28 | # Continue the script even if some notebooks in build fail to 29 | # make sure that cache is copied to s3 for the successful notebooks 30 | d2lbook build eval --tab tensorflow || ((ss=1)) 31 | 32 | # Move aws copy commands for cache store outside 33 | echo "Upload tensorflow build cache to s3" 34 | measure_command_time "aws s3 sync _build s3://preview.d2l.ai/"$CACHE_DIR"/"$REPO_NAME"-"$TARGET_BRANCH"/_build --acl public-read --quiet --exclude 'eval*/data/*'" 35 | 36 | # Exit with a non-zero status if evaluation failed 37 | if [ "$ss" -ne 0 ]; then 38 | exit 1 39 | fi 40 | -------------------------------------------------------------------------------- /.github/workflow_scripts/setup_git.sh: -------------------------------------------------------------------------------- 1 | function setup_git { 2 | # Turn off logging 3 | set +x 4 | mkdir -p $HOME/.ssh 5 | echo "yes" | ssh-keyscan -t rsa github.com >> ~/.ssh/known_hosts 6 | 7 | # Retrieve the SSH key securely from AWS Secrets Manager 8 | GIT_SSH_KEY=$(aws secretsmanager get-secret-value --secret-id d2l_bot_github --query SecretString --output text --region us-west-2) 9 | 10 | # Write the SSH key to a file 11 | echo "$GIT_SSH_KEY" > $HOME/.ssh/id_rsa 12 | chmod 600 $HOME/.ssh/id_rsa 13 | 14 | git config --global user.name "d2l-bot" 15 | git config --global user.email "100248899+d2l-bot@users.noreply.github.com" 16 | 17 | echo "Successfully Configured Bot" 18 | } 19 | -------------------------------------------------------------------------------- /.github/workflow_scripts/utils.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # By default, all builds are cached 4 | DISABLE_CACHE=false # Eg. 'true' or 'false' 5 | 6 | 7 | # Function to measure command execution time 8 | measure_command_time() { 9 | local command="$1" 10 | 11 | # Start timing 12 | local start_time=$(date +%s) 13 | 14 | # Run the command 15 | eval "$command" 16 | 17 | # Calculate the time taken 18 | local end_time=$(date +%s) 19 | local elapsed_time=$((end_time - start_time)) 20 | 21 | # Format the elapsed time for display 22 | local formatted_time=$(printf "%02dhr %02dmin %02dsec" $((elapsed_time / 3600)) $(((elapsed_time % 3600) / 60)) $((elapsed_time % 60))) 23 | 24 | # Print the elapsed time 25 | echo "Time taken for $command: $formatted_time" 26 | } 27 | -------------------------------------------------------------------------------- /.github/workflows/clear-cache.yml: -------------------------------------------------------------------------------- 1 | name: Clear CI Cache 2 | 3 | on: 4 | workflow_dispatch: 5 | inputs: 6 | target_branch: 7 | type: choice 8 | description: Choose the cache associated branch 9 | options: 10 | - master 11 | - r0.17 12 | - release 13 | - classic 14 | 15 | cache_dir: 16 | type: choice 17 | description: Choose cache type, pull requests or push events 18 | options: 19 | - ci_cache_pr 20 | - ci_cache_push 21 | 22 | permissions: 23 | id-token: write 24 | contents: read 25 | 26 | jobs: 27 | clear_cache: 28 | name: Clear CI Cache 29 | runs-on: ubuntu-latest 30 | steps: 31 | - name: Configure AWS Credentials 32 | uses: aws-actions/configure-aws-credentials@v2 33 | with: 34 | role-to-assume: arn:aws:iam::650140442593:role/D2L_CI_Clear_Cache 35 | role-duration-seconds: 3600 36 | aws-region: us-west-2 37 | - name: Delete Cache 38 | run: | 39 | CACHE_S3_URL="s3://preview.d2l.ai/${{ github.event.inputs.cache_dir }}/d2l-en-${{ github.event.inputs.target_branch }}/" 40 | echo "Removing cache in $CACHE_S3_URL" 41 | aws s3 rm --recursive $CACHE_S3_URL 42 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | **/.ipynb_checkpoints 2 | **/__pycache__ 3 | data/ 4 | *.json 5 | *.params 6 | *.DS_Store 7 | *.Rhistory 8 | *.csv 9 | build/README.md 10 | build/environment.yml 11 | *egg-info* 12 | dist/* 13 | build/chapter* 14 | build/_build 15 | build/img 16 | build/data 17 | build/d2l 18 | /chapter_attention-mechanisms-and-transformers/fra-eng.zip 19 | /chapter_recurrent-neural-networks/fra-eng.zip 20 | img/*.pdf 21 | aclImdb* 22 | /_build/ 23 | graffle/*/*.svg 24 | graffle/*/*.pdf 25 | build/ 26 | /chapter_builders-guide/mydict 27 | /chapter_builders-guide/x-file 28 | /chapter_builders-guide/x-files 29 | .idea 30 | .vscode 31 | .pytest_cache 32 | static/latex_style/PT1* 33 | /chapter_hyperparameter_optimization/std.out 34 | latex_style/* 35 | static/latex_style/* 36 | -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | ## Code of Conduct 2 | This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct). 3 | For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact 4 | opensource-codeofconduct@amazon.com with any additional questions or comments. 5 | -------------------------------------------------------------------------------- /LICENSE-SAMPLECODE: -------------------------------------------------------------------------------- 1 | Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | 3 | Permission is hereby granted, free of charge, to any person obtaining a copy of this 4 | software and associated documentation files (the "Software"), to deal in the Software 5 | without restriction, including without limitation the rights to use, copy, modify, 6 | merge, publish, distribute, sublicense, and/or sell copies of the Software, and to 7 | permit persons to whom the Software is furnished to do so. 8 | 9 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, 10 | INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A 11 | PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT 12 | HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION 13 | OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE 14 | SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 15 | -------------------------------------------------------------------------------- /LICENSE-SUMMARY: -------------------------------------------------------------------------------- 1 | Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved. 2 | 3 | The documentation is made available under the Creative Commons Attribution-ShareAlike 4.0 International License. See the LICENSE file. 4 | 5 | The sample code within this documentation is made available under a modified MIT license. See the LICENSE-SAMPLECODE file. 6 | -------------------------------------------------------------------------------- /chapter_appendix-mathematics-for-deep-learning/index.md: -------------------------------------------------------------------------------- 1 | # Appendix: Mathematics for Deep Learning 2 | :label:`chap_appendix_math` 3 | 4 | **Brent Werness** (*Amazon*), **Rachel Hu** (*Amazon*), and authors of this book 5 | 6 | 7 | One of the wonderful parts of modern deep learning is the fact that much of it can be understood and used without a full understanding of the mathematics below it. This is a sign that the field is maturing. Just as most software developers no longer need to worry about the theory of computable functions, neither should deep learning practitioners need to worry about the theoretical foundations of maximum likelihood learning. 8 | 9 | But, we are not quite there yet. 10 | 11 | In practice, you will sometimes need to understand how architectural choices influence gradient flow, or the implicit assumptions you make by training with a certain loss function. You might need to know what in the world entropy measures, and how it can help you understand exactly what bits-per-character means in your model. These all require deeper mathematical understanding. 12 | 13 | This appendix aims to provide you the mathematical background you need to understand the core theory of modern deep learning, but it is not exhaustive. We will begin with examining linear algebra in greater depth. We develop a geometric understanding of all the common linear algebraic objects and operations that will enable us to visualize the effects of various transformations on our data. A key element is the development of the basics of eigen-decompositions. 14 | 15 | We next develop the theory of differential calculus to the point that we can fully understand why the gradient is the direction of steepest descent, and why back-propagation takes the form it does. Integral calculus is then discussed to the degree needed to support our next topic, probability theory. 16 | 17 | Problems encountered in practice frequently are not certain, and thus we need a language to speak about uncertain things. We review the theory of random variables and the most commonly encountered distributions so we may discuss models probabilistically. This provides the foundation for the naive Bayes classifier, a probabilistic classification technique. 18 | 19 | Closely related to probability theory is the study of statistics. While statistics is far too large a field to do justice in a short section, we will introduce fundamental concepts that all machine learning practitioners should be aware of, in particular: evaluating and comparing estimators, conducting hypothesis tests, and constructing confidence intervals. 20 | 21 | Last, we turn to the topic of information theory, which is the mathematical study of information storage and transmission. This provides the core language by which we may discuss quantitatively how much information a model holds on a domain of discourse. 22 | 23 | Taken together, these form the core of the mathematical concepts needed to begin down the path towards a deep understanding of deep learning. 24 | 25 | ```toc 26 | :maxdepth: 2 27 | 28 | geometry-linear-algebraic-ops 29 | eigendecomposition 30 | single-variable-calculus 31 | multivariable-calculus 32 | integral-calculus 33 | random-variables 34 | maximum-likelihood 35 | distributions 36 | naive-bayes 37 | statistics 38 | information-theory 39 | ``` 40 | 41 | -------------------------------------------------------------------------------- /chapter_appendix-tools-for-deep-learning/colab.md: -------------------------------------------------------------------------------- 1 | # Using Google Colab 2 | :label:`sec_colab` 3 | 4 | We introduced how to run this book on AWS in :numref:`sec_sagemaker` and :numref:`sec_aws`. Another option is running this book on [Google Colab](https://colab.research.google.com/) 5 | if you have a Google account. 6 | 7 | To run the code of a section on Colab, simply click the `Colab` button as shown in :numref:`fig_colab`. 8 | 9 | ![Run the code of a section on Colab.](../img/colab.png) 10 | :width:`300px` 11 | :label:`fig_colab` 12 | 13 | 14 | If it is your first time to run a code cell, 15 | you will receive a warning message as shown in :numref:`fig_colab2`. 16 | Just click "RUN ANYWAY" to ignore it. 17 | 18 | ![Ignore the warning message by clicking "RUN ANYWAY".](../img/colab-2.png) 19 | :width:`300px` 20 | :label:`fig_colab2` 21 | 22 | Next, Colab will connect you to an instance to run the code of this section. 23 | Specifically, if a GPU is needed, 24 | Colab will be automatically requested 25 | for connecting to a GPU instance. 26 | 27 | 28 | ## Summary 29 | 30 | * You can use Google Colab to run each section's code in this book. 31 | * Colab will be requested to connect to a GPU instance if a GPU is needed in any section of this book. 32 | 33 | 34 | ## Exercises 35 | 36 | 1. Open any section of this book using Google Colab. 37 | 1. Edit and run any section that requires a GPU using Google Colab. 38 | 39 | 40 | [Discussions](https://discuss.d2l.ai/t/424) 41 | -------------------------------------------------------------------------------- /chapter_appendix-tools-for-deep-learning/index.md: -------------------------------------------------------------------------------- 1 | # Appendix: Tools for Deep Learning 2 | :label:`chap_appendix_tools` 3 | 4 | 5 | To get the most out of *Dive into Deep Learning*, 6 | we will talk you through different tools 7 | in this appendix, 8 | such as 9 | for running and contributing to this 10 | interactive open-source book. 11 | 12 | ```toc 13 | :maxdepth: 2 14 | 15 | jupyter 16 | sagemaker 17 | aws 18 | colab 19 | selecting-servers-gpus 20 | contributing 21 | utils 22 | d2l 23 | ``` 24 | 25 | -------------------------------------------------------------------------------- /chapter_builders-guide/index.md: -------------------------------------------------------------------------------- 1 | # Builders' Guide 2 | :label:`chap_computation` 3 | 4 | Alongside giant datasets and powerful hardware, 5 | great software tools have played an indispensable role 6 | in the rapid progress of deep learning. 7 | Starting with the pathbreaking Theano library released in 2007, 8 | flexible open-source tools have enabled researchers 9 | to rapidly prototype models, avoiding repetitive work 10 | when recycling standard components 11 | while still maintaining the ability to make low-level modifications. 12 | Over time, deep learning's libraries have evolved 13 | to offer increasingly coarse abstractions. 14 | Just as semiconductor designers went from specifying transistors 15 | to logical circuits to writing code, 16 | neural networks researchers have moved from thinking about 17 | the behavior of individual artificial neurons 18 | to conceiving of networks in terms of whole layers, 19 | and now often design architectures with far coarser *blocks* in mind. 20 | 21 | 22 | So far, we have introduced some basic machine learning concepts, 23 | ramping up to fully-functional deep learning models. 24 | In the last chapter, 25 | we implemented each component of an MLP from scratch 26 | and even showed how to leverage high-level APIs 27 | to roll out the same models effortlessly. 28 | To get you that far that fast, we *called upon* the libraries, 29 | but skipped over more advanced details about *how they work*. 30 | In this chapter, we will peel back the curtain, 31 | digging deeper into the key components of deep learning computation, 32 | namely model construction, parameter access and initialization, 33 | designing custom layers and blocks, reading and writing models to disk, 34 | and leveraging GPUs to achieve dramatic speedups. 35 | These insights will move you from *end user* to *power user*, 36 | giving you the tools needed to reap the benefits 37 | of a mature deep learning library while retaining the flexibility 38 | to implement more complex models, including those you invent yourself! 39 | While this chapter does not introduce any new models or datasets, 40 | the advanced modeling chapters that follow rely heavily on these techniques. 41 | 42 | ```toc 43 | :maxdepth: 2 44 | 45 | model-construction 46 | parameters 47 | init-param 48 | lazy-init 49 | custom-layer 50 | read-write 51 | use-gpu 52 | ``` 53 | 54 | -------------------------------------------------------------------------------- /chapter_computational-performance/index.md: -------------------------------------------------------------------------------- 1 | # Computational Performance 2 | :label:`chap_performance` 3 | 4 | In deep learning, 5 | datasets and models are usually large, 6 | which involves heavy computation. 7 | Therefore, computational performance matters a lot. 8 | This chapter will focus on the major factors that affect computational performance: 9 | imperative programming, symbolic programming, asynchronous computing, automatic parallelism, and multi-GPU computation. 10 | By studying this chapter, you may further improve computational performance of those models implemented in the previous chapters, 11 | for example, by reducing training time without affecting accuracy. 12 | 13 | ```toc 14 | :maxdepth: 2 15 | 16 | hybridize 17 | async-computation 18 | auto-parallelism 19 | hardware 20 | multiple-gpus 21 | multiple-gpus-concise 22 | parameterserver 23 | ``` 24 | 25 | -------------------------------------------------------------------------------- /chapter_computer-vision/index.md: -------------------------------------------------------------------------------- 1 | # Computer Vision 2 | :label:`chap_cv` 3 | 4 | Whether it is medical diagnosis, self-driving vehicles, camera monitoring, or smart filters, many applications in the field of computer vision are closely related to our current and future lives. 5 | In recent years, deep learning has been 6 | the transformative power for advancing the performance of computer vision systems. 7 | It can be said that the most advanced computer vision applications are almost inseparable from deep learning. 8 | In view of this, this chapter will focus on the field of computer vision, and investigate methods and applications that have recently been influential in academia and industry. 9 | 10 | 11 | In :numref:`chap_cnn` and :numref:`chap_modern_cnn`, we studied various convolutional neural networks that are 12 | commonly used in computer vision, and applied them 13 | to simple image classification tasks. 14 | At the beginning of this chapter, we will describe 15 | two methods that 16 | may improve model generalization, namely *image augmentation* and *fine-tuning*, 17 | and apply them to image classification. 18 | Since deep neural networks can effectively represent images in multiple levels, 19 | such layerwise representations have been successfully 20 | used in various computer vision tasks such as *object detection*, *semantic segmentation*, and *style transfer*. 21 | Following the key idea of leveraging layerwise representations in computer vision, 22 | we will begin with major components and techniques for object detection. Next, we will show how to use *fully convolutional networks* for semantic segmentation of images. Then we will explain how to use style transfer techniques to generate images like the cover of this book. 23 | In the end, we conclude this chapter 24 | by applying the materials of this chapter and several previous chapters on two popular computer vision benchmark datasets. 25 | 26 | ```toc 27 | :maxdepth: 2 28 | 29 | image-augmentation 30 | fine-tuning 31 | bounding-box 32 | anchor 33 | multiscale-object-detection 34 | object-detection-dataset 35 | ssd 36 | rcnn 37 | semantic-segmentation-and-dataset 38 | transposed-conv 39 | fcn 40 | neural-style 41 | kaggle-cifar10 42 | kaggle-dog 43 | ``` 44 | 45 | -------------------------------------------------------------------------------- /chapter_gaussian-processes/index.md: -------------------------------------------------------------------------------- 1 | # Gaussian Processes 2 | :label:`chap_gp` 3 | 4 | **Andrew Gordon Wilson** (*New York University and Amazon*) 5 | 6 | 7 | Gaussian processes (GPs) are ubitiquous. You have already encountered many examples of GPs without realizing it. Any model that is linear in its parameters with a Gaussian distribution over the parameters is a Gaussian process. This class spans discrete models, including random walks, and autoregressive processes, as well as continuous models, including Bayesian linear regression models, polynomials, Fourier series, radial basis functions, and even neural networks with an infinite number of hidden units. There is a running joke that "everything is a special case of a Gaussian process". 8 | 9 | Learning about Gaussian processes is important for three reasons: (1) they provide a _function space_ perspective of modelling, which makes understanding a variety of model classes, including deep neural networks, much more approachable; (2) they have an extraordinary range of applications where they are state-of-the-art, including active learning, hyperparameter learning, auto-ML, and spatiotemporal regression; (3) over the last few years, algorithmic advances have made Gaussian processes increasingly scalable and relevant, harmonizing with deep learning through frameworks such as [GPyTorch](https://gpytorch.ai) :cite:`Gardner.Pleiss.Weinberger.Bindel.Wilson.2018`. Indeed, GPs and and deep neural networks are not competing approaches, but highly complementary, and can be combined to great effect. These algorithmic advances are not just relevant to Gaussian processes, but provide a foundation in numerical methods that is broadly useful in deep learning. 10 | 11 | In this chapter, we introduce Gaussian processes. In the introductory notebook, we start by reasoning intuitively about what Gaussian processes are and how they directly model functions. In the priors notebook, we focus on how to specify Gaussian process priors. We directly connect the tradiational weight-space approach to modelling to function space, which will help us reason about constructing and understanding machine learning models, including deep neural networks. We then introduce popular covariance functions, also known as _kernels_, which control the generalization properties of a Gaussian process. A GP with a given kernel defines a prior over functions. In the inference notebook, we will show how to use data to infer a _posterior_, in order to make predictions. This notebook contains from-scratch code for making predictions with a Gaussian process, as well as an introduction to GPyTorch. In upcoming notebooks, we will introduce the numerics behind Gaussian processes, which is useful for scaling Gaussian processes but also a powerful general foundation for deep learning, and advanced use-cases such as hyperparameter tuning in deep learning. Our examples will make use of GPyTorch, which makes Gaussian processes scale, and is closely integrated with deep learning functionality and PyTorch. 12 | 13 | ```toc 14 | :maxdepth: 2 15 | 16 | gp-intro 17 | gp-priors 18 | gp-inference 19 | ``` 20 | 21 | -------------------------------------------------------------------------------- /chapter_generative-adversarial-networks/index.md: -------------------------------------------------------------------------------- 1 | # Generative Adversarial Networks 2 | :label:`chap_gans` 3 | 4 | ```toc 5 | :maxdepth: 2 6 | 7 | gan 8 | dcgan 9 | ``` 10 | -------------------------------------------------------------------------------- /chapter_hyperparameter-optimization/index.md: -------------------------------------------------------------------------------- 1 | # Hyperparameter Optimization 2 | :label:`chap_hyperopt` 3 | 4 | **Aaron Klein** (*Amazon*), **Matthias Seeger** (*Amazon*), and **Cedric Archambeau** (*Amazon*) 5 | 6 | The performance of every machine learning model depends on its hyperparameters. 7 | They control the learning algorithm or the structure of the underlying 8 | statistical model. However, there is no general way to choose hyperparameters 9 | in practice. Instead, hyperparameters are often set in a trial-and-error manner 10 | or sometimes left to their default values by practitioners, leading to 11 | suboptimal generalization. 12 | 13 | Hyperparameter optimization provides a systematic approach to this problem, by 14 | casting it as an optimization problem: a good set of hyperparameters should (at 15 | least) minimize a validation error. Compared to most other optimization problems 16 | arising in machine learning, hyperparameter optimization is a nested one, where 17 | each iteration requires training and validating a machine learning model. 18 | 19 | In this chapter, we will first introduce the basics of hyperparameter 20 | optimization. We will also present some recent advancements that improve the 21 | overall efficiency of hyperparameter optimization by exploiting cheap-to-evaluate 22 | proxies of the original objective function. At the end of this chapter, you 23 | should be able to apply state-of-the-art hyperparameter optimization techniques 24 | to optimize the hyperparameter of your own machine learning algorithm. 25 | 26 | ```toc 27 | :maxdepth: 2 28 | 29 | hyperopt-intro 30 | hyperopt-api 31 | rs-async.md 32 | sh-intro 33 | sh-async 34 | ``` 35 | 36 | -------------------------------------------------------------------------------- /chapter_linear-classification/index.md: -------------------------------------------------------------------------------- 1 | # Linear Neural Networks for Classification 2 | :label:`chap_classification` 3 | 4 | Now that you have worked through all of the mechanics 5 | you are ready to apply the skills you have learned to broader kinds of tasks. 6 | Even as we pivot towards classification, 7 | most of the plumbing remains the same: 8 | loading the data, passing it through the model, 9 | generating output, calculating the loss, 10 | taking gradients with respect to weights, 11 | and updating the model. 12 | However, the precise form of the targets, 13 | the parametrization of the output layer, 14 | and the choice of loss function will adapt 15 | to suit the *classification* setting. 16 | 17 | ```toc 18 | :maxdepth: 2 19 | 20 | softmax-regression 21 | image-classification-dataset 22 | classification 23 | softmax-regression-scratch 24 | softmax-regression-concise 25 | generalization-classification 26 | environment-and-distribution-shift 27 | ``` 28 | 29 | -------------------------------------------------------------------------------- /chapter_linear-regression/index.md: -------------------------------------------------------------------------------- 1 | # Linear Neural Networks for Regression 2 | :label:`chap_regression` 3 | 4 | Before we worry about making our neural networks deep, 5 | it will be helpful to implement some shallow ones, 6 | for which the inputs connect directly to the outputs. 7 | This will prove important for a few reasons. 8 | First, rather than getting distracted by complicated architectures, 9 | we can focus on the basics of neural network training, 10 | including parametrizing the output layer, handling data, 11 | specifying a loss function, and training the model. 12 | Second, this class of shallow networks happens 13 | to comprise the set of linear models, 14 | which subsumes many classical methods of statistical prediction, 15 | including linear and softmax regression. 16 | Understanding these classical tools is pivotal 17 | because they are widely used in many contexts 18 | and we will often need to use them as baselines 19 | when justifying the use of fancier architectures. 20 | This chapter will focus narrowly on linear regression 21 | and the next one will extend our modeling repertoire 22 | by developing linear neural networks for classification. 23 | 24 | ```toc 25 | :maxdepth: 2 26 | 27 | linear-regression 28 | oo-design 29 | synthetic-regression-data 30 | linear-regression-scratch 31 | linear-regression-concise 32 | generalization 33 | weight-decay 34 | ``` 35 | 36 | -------------------------------------------------------------------------------- /chapter_multilayer-perceptrons/index.md: -------------------------------------------------------------------------------- 1 | # Multilayer Perceptrons 2 | :label:`chap_perceptrons` 3 | 4 | In this chapter, we will introduce your first truly *deep* network. 5 | The simplest deep networks are called *multilayer perceptrons*, 6 | and they consist of multiple layers of neurons 7 | each fully connected to those in the layer below 8 | (from which they receive input) 9 | and those above (which they, in turn, influence). 10 | Although automatic differentiation 11 | significantly simplifies the implementation of deep learning algorithms, 12 | we will dive deep into how these gradients 13 | are calculated in deep networks. 14 | Then we will 15 | be ready to 16 | discuss issues relating to numerical stability and parameter initialization 17 | that are key to successfully training deep networks. 18 | When we train such high-capacity models we run the risk of overfitting. Thus, we will 19 | revisit regularization and generalization 20 | for deep networks. 21 | Throughout, we aim 22 | to give you a firm grasp not just of the concepts but also of the practice of using deep networks. 23 | At the end of this chapter, we apply what we have introduced so far to a real case: house price 24 | prediction. We punt matters relating to the computational performance, scalability, and efficiency 25 | of our models to subsequent chapters. 26 | 27 | ```toc 28 | :maxdepth: 2 29 | 30 | mlp 31 | mlp-implementation 32 | backprop 33 | numerical-stability-and-init 34 | generalization-deep 35 | dropout 36 | kaggle-house-price 37 | ``` 38 | 39 | -------------------------------------------------------------------------------- /chapter_natural-language-processing-pretraining/index.md: -------------------------------------------------------------------------------- 1 | # Natural Language Processing: Pretraining 2 | :label:`chap_nlp_pretrain` 3 | 4 | 5 | Humans need to communicate. 6 | Out of this basic need of the human condition, a vast amount of written text has been generated on an everyday basis. 7 | Given rich text in social media, chat apps, emails, product reviews, news articles, research papers, and books, it becomes vital to enable computers to understand them to offer assistance or make decisions based on human languages. 8 | 9 | *Natural language processing* studies interactions between computers and humans using natural languages. 10 | In practice, it is very common to use natural language processing techniques to process and analyze text (human natural language) data, such as language models in :numref:`sec_language-model` and machine translation models in :numref:`sec_machine_translation`. 11 | 12 | To understand text, we can begin by learning 13 | its representations. 14 | Leveraging the existing text sequences 15 | from large corpora, 16 | *self-supervised learning* 17 | has been extensively 18 | used to pretrain text representations, 19 | such as by predicting some hidden part of the text 20 | using some other part of their surrounding text. 21 | In this way, 22 | models learn through supervision 23 | from *massive* text data 24 | without *expensive* labeling efforts! 25 | 26 | 27 | As we will see in this chapter, 28 | when treating each word or subword as an individual token, 29 | the representation of each token can be pretrained 30 | using word2vec, GloVe, or subword embedding models 31 | on large corpora. 32 | After pretraining, representation of each token can be a vector, 33 | however, it remains the same no matter what the context is. 34 | For instance, the vector representation of "bank" is the same 35 | in both 36 | "go to the bank to deposit some money" 37 | and 38 | "go to the bank to sit down". 39 | Thus, many more recent pretraining models adapt representation of the same token 40 | to different contexts. 41 | Among them is BERT, a much deeper self-supervised model based on the Transformer encoder. 42 | In this chapter, we will focus on how to pretrain such representations for text, 43 | as highlighted in :numref:`fig_nlp-map-pretrain`. 44 | 45 | ![Pretrained text representations can be fed to various deep learning architectures for different downstream natural language processing applications. This chapter focuses on the upstream text representation pretraining.](../img/nlp-map-pretrain.svg) 46 | :label:`fig_nlp-map-pretrain` 47 | 48 | 49 | For sight of the big picture, 50 | :numref:`fig_nlp-map-pretrain` shows that 51 | the pretrained text representations can be fed to 52 | a variety of deep learning architectures for different downstream natural language processing applications. 53 | We will cover them in :numref:`chap_nlp_app`. 54 | 55 | ```toc 56 | :maxdepth: 2 57 | 58 | word2vec 59 | approx-training 60 | word-embedding-dataset 61 | word2vec-pretraining 62 | glove 63 | subword-embedding 64 | similarity-analogy 65 | bert 66 | bert-dataset 67 | bert-pretraining 68 | 69 | ``` 70 | 71 | -------------------------------------------------------------------------------- /chapter_optimization/index.md: -------------------------------------------------------------------------------- 1 | # Optimization Algorithms 2 | :label:`chap_optimization` 3 | 4 | If you read the book in sequence up to this point you already used a number of optimization algorithms to train deep learning models. 5 | They were the tools that allowed us to continue updating model parameters and to minimize the value of the loss function, as evaluated on the training set. Indeed, anyone content with treating optimization as a black box device to minimize objective functions in a simple setting might well content oneself with the knowledge that there exists an array of incantations of such a procedure (with names such as "SGD" and "Adam"). 6 | 7 | To do well, however, some deeper knowledge is required. 8 | Optimization algorithms are important for deep learning. 9 | On the one hand, training a complex deep learning model can take hours, days, or even weeks. 10 | The performance of the optimization algorithm directly affects the model's training efficiency. 11 | On the other hand, understanding the principles of different optimization algorithms and the role of their hyperparameters 12 | will enable us to tune the hyperparameters in a targeted manner to improve the performance of deep learning models. 13 | 14 | In this chapter, we explore common deep learning optimization algorithms in depth. 15 | Almost all optimization problems arising in deep learning are *nonconvex*. 16 | Nonetheless, the design and analysis of algorithms in the context of *convex* problems have proven to be very instructive. 17 | It is for that reason that this chapter includes a primer on convex optimization and the proof for a very simple stochastic gradient descent algorithm on a convex objective function. 18 | 19 | ```toc 20 | :maxdepth: 2 21 | 22 | optimization-intro 23 | convexity 24 | gd 25 | sgd 26 | minibatch-sgd 27 | momentum 28 | adagrad 29 | rmsprop 30 | adadelta 31 | adam 32 | lr-scheduler 33 | ``` 34 | 35 | -------------------------------------------------------------------------------- /chapter_preliminaries/index.md: -------------------------------------------------------------------------------- 1 | # Preliminaries 2 | :label:`chap_preliminaries` 3 | 4 | To prepare for your dive into deep learning, 5 | you will need a few survival skills: 6 | (i) techniques for storing and manipulating data; 7 | (ii) libraries for ingesting 8 | and preprocessing data from a variety of sources; 9 | (iii) knowledge of the basic linear algebraic operations 10 | that we apply to high-dimensional data elements; 11 | (iv) just enough calculus to determine 12 | which direction to adjust each parameter 13 | in order to decrease the loss function; 14 | (v) the ability to automatically compute derivatives 15 | so that you can forget much of 16 | the calculus you just learned; 17 | (vi) some basic fluency in probability, 18 | our primary language for reasoning under uncertainty; 19 | and (vii) some aptitude for finding answers 20 | in the official documentation when you get stuck. 21 | 22 | In short, this chapter provides a rapid introduction 23 | to the basics that you will need to follow 24 | *most* of the technical content in this book. 25 | 26 | ```toc 27 | :maxdepth: 2 28 | 29 | ndarray 30 | pandas 31 | linear-algebra 32 | calculus 33 | autograd 34 | probability 35 | lookup-api 36 | ``` 37 | 38 | -------------------------------------------------------------------------------- /chapter_recommender-systems/index.md: -------------------------------------------------------------------------------- 1 | # Recommender Systems 2 | :label:`chap_recsys` 3 | 4 | 5 | **Shuai Zhang** (*Amazon*), **Aston Zhang** (*Amazon*), and **Yi Tay** (*Google*) 6 | 7 | Recommender systems are widely employed in industry and are ubiquitous in our daily lives. These systems are utilized in a number of areas such as online shopping sites (e.g., amazon.com), music/movie services site (e.g., Netflix and Spotify), mobile application stores (e.g., IOS app store and google play), online advertising, just to name a few. 8 | 9 | The major goal of recommender systems is to help users discover relevant items such as movies to watch, text to read or products to buy, so as to create a delightful user experience. Moreover, recommender systems are among the most powerful machine learning systems that online retailers implement in order to drive incremental revenue. Recommender systems are replacements of search engines by reducing the efforts in proactive searches and surprising users with offers they never searched for. Many companies managed to position themselves ahead of their competitors with the help of more effective recommender systems. As such, recommender systems are central to not only our everyday lives but also highly indispensable in some industries. 10 | 11 | 12 | In this chapter, we will cover the fundamentals and advancements of recommender systems, along with exploring some common fundamental techniques for building recommender systems with different data sources available and their implementations. Specifically, you will learn how to predict the rating a user might give to a prospective item, how to generate a recommendation list of items and how to predict the click-through rate from abundant features. These tasks are commonplace in real-world applications. By studying this chapter, you will get hands-on experience pertaining to solving real world recommendation problems with not only classical methods but the more advanced deep learning based models as well. 13 | 14 | ```toc 15 | :maxdepth: 2 16 | 17 | recsys-intro 18 | movielens 19 | mf 20 | autorec 21 | ranking 22 | neumf 23 | seqrec 24 | ctr 25 | fm 26 | deepfm 27 | ``` 28 | 29 | -------------------------------------------------------------------------------- /chapter_recurrent-modern/index.md: -------------------------------------------------------------------------------- 1 | # Modern Recurrent Neural Networks 2 | :label:`chap_modern_rnn` 3 | 4 | The previous chapter introduced the key ideas 5 | behind recurrent neural networks (RNNs). 6 | However, just as with convolutional neural networks, 7 | there has been a tremendous amount of innovation 8 | in RNN architectures, culminating in several complex 9 | designs that have proven successful in practice. 10 | In particular, the most popular designs 11 | feature mechanisms for mitigating the notorious 12 | numerical instability faced by RNNs, 13 | as typified by vanishing and exploding gradients. 14 | Recall that in :numref:`chap_rnn` we dealt 15 | with exploding gradients by applying a blunt 16 | gradient clipping heuristic. 17 | Despite the efficacy of this hack, 18 | it leaves open the problem of vanishing gradients. 19 | 20 | In this chapter, we introduce the key ideas behind 21 | the most successful RNN architectures for sequences, 22 | which stem from two papers. 23 | The first, *Long Short-Term Memory* :cite:`Hochreiter.Schmidhuber.1997`, 24 | introduces the *memory cell*, a unit of computation that replaces 25 | traditional nodes in the hidden layer of a network. 26 | With these memory cells, networks are able 27 | to overcome difficulties with training 28 | encountered by earlier recurrent networks. 29 | Intuitively, the memory cell avoids 30 | the vanishing gradient problem 31 | by keeping values in each memory cell's internal state 32 | cascading along a recurrent edge with weight 1 33 | across many successive time steps. 34 | A set of multiplicative gates help the network 35 | to determine not only the inputs to allow 36 | into the memory state, 37 | but when the content of the memory state 38 | should influence the model's output. 39 | 40 | The second paper, *Bidirectional Recurrent Neural Networks* :cite:`Schuster.Paliwal.1997`, 41 | introduces an architecture in which information 42 | from both the future (subsequent time steps) 43 | and the past (preceding time steps) 44 | are used to determine the output 45 | at any point in the sequence. 46 | This is in contrast to previous networks, 47 | in which only past input can affect the output. 48 | Bidirectional RNNs have become a mainstay 49 | for sequence labeling tasks in natural language processing, 50 | among a myriad of other tasks. 51 | Fortunately, the two innovations are not mutually exclusive, 52 | and have been successfully combined for phoneme classification 53 | :cite:`Graves.Schmidhuber.2005` and handwriting recognition :cite:`graves2008novel`. 54 | 55 | 56 | The first sections in this chapter will explain the LSTM architecture, 57 | a lighter-weight version called the gated recurrent unit (GRU), 58 | the key ideas behind bidirectional RNNs 59 | and a brief explanation of how RNN layers 60 | are stacked together to form deep RNNs. 61 | Subsequently, we will explore the application of RNNs 62 | in sequence-to-sequence tasks, 63 | introducing machine translation 64 | along with key ideas such as *encoder--decoder* architectures and *beam search*. 65 | 66 | ```toc 67 | :maxdepth: 2 68 | 69 | lstm 70 | gru 71 | deep-rnn 72 | bi-rnn 73 | machine-translation-and-dataset 74 | encoder-decoder 75 | seq2seq 76 | beam-search 77 | ``` 78 | 79 | -------------------------------------------------------------------------------- /chapter_references/zreferences.md: -------------------------------------------------------------------------------- 1 | ```eval_rst 2 | 3 | .. only:: html 4 | 5 | References 6 | ========== 7 | 8 | ``` 9 | 10 | :bibliography:`../d2l.bib` 11 | 12 | -------------------------------------------------------------------------------- /ci/docker/Dockerfile.d2l-builder: -------------------------------------------------------------------------------- 1 | FROM ubuntu:latest 2 | 3 | RUN adduser --disabled-password --disabled-login ci 4 | WORKDIR /home/ci 5 | 6 | # Add d2l_job script 7 | ADD d2l_job.sh . 8 | RUN chmod +x d2l_job.sh; chown ci d2l_job.sh 9 | 10 | # Copy git timesync for caching 11 | ADD git-timesync /home/ci/.local/bin/ 12 | RUN chmod +x /home/ci/.local/bin/git-timesync 13 | 14 | RUN apt-get update && apt-get -y install build-essential git wget 15 | 16 | # Install pdf dependencies 17 | RUN DEBIAN_FRONTEND=noninteractive apt-get install -y texlive-full 18 | RUN apt-get install -y librsvg2-bin xindy pandoc 19 | 20 | # Install fonts 21 | RUN wget https://raw.githubusercontent.com/d2l-ai/utils/master/install_fonts.sh 22 | # Remove "sudo " from the fonts script 23 | RUN sed -i 's/sudo //g' install_fonts.sh 24 | RUN chmod +x install_fonts.sh; ./install_fonts.sh 25 | 26 | RUN apt-get install -y python3 python3-pip python-is-python3 27 | 28 | # Allow permissions for pip installations and git-timesync 29 | RUN chown -R ci:ci /home/ci/.local 30 | 31 | USER ci 32 | 33 | ENV PATH="/home/ci/.local/bin:$PATH" 34 | 35 | # Install d2lbook using pip 36 | # Install all libraries (cpu) to make sure API reference works for PDF builds 37 | RUN pip3 install d2lbook==1.0.0 torch torchvision tensorflow jax mxnet 38 | 39 | CMD ["/bin/bash"] 40 | -------------------------------------------------------------------------------- /ci/docker/Dockerfile.d2l-jax: -------------------------------------------------------------------------------- 1 | # Use JAX 0.4.13 & Flax (July 2023) 2 | FROM nvcr.io/nvdlfwea/jax/jax:23.07-py3 3 | 4 | RUN adduser --disabled-password --disabled-login ci 5 | WORKDIR /home/ci 6 | 7 | # Copy d2l_job script 8 | ADD d2l_job.sh . 9 | RUN chmod +x d2l_job.sh; chown ci d2l_job.sh 10 | 11 | # Copy git timesync for caching 12 | ADD git-timesync /home/ci/.local/bin/ 13 | RUN chmod +x /home/ci/.local/bin/git-timesync 14 | 15 | # Allow permissions for pip installations and git-timesync 16 | RUN chown -R ci:ci /home/ci/.local 17 | 18 | USER ci 19 | 20 | ENV PATH="/home/ci/.local/bin:$PATH" 21 | 22 | # Install d2lbook using pip + dependencies for jax d2l 23 | RUN pip3 install d2lbook==1.0.0 tensorflow==2.12.0 tensorflow-datasets==4.9.2 24 | 25 | # Python script to print framework versions 26 | ADD print_versions.py . 27 | 28 | CMD ["/bin/bash"] 29 | -------------------------------------------------------------------------------- /ci/docker/Dockerfile.d2l-mxnet: -------------------------------------------------------------------------------- 1 | # Use MXNet 1.9.1 (April 2023) 2 | FROM nvcr.io/nvidia/mxnet:23.04-py3 3 | 4 | RUN adduser --disabled-password --disabled-login ci 5 | WORKDIR /home/ci 6 | 7 | # Copy d2l_job script 8 | ADD d2l_job.sh . 9 | RUN chmod +x d2l_job.sh; chown ci d2l_job.sh 10 | 11 | # Copy git timesync for caching 12 | ADD git-timesync /home/ci/.local/bin/ 13 | RUN chmod +x /home/ci/.local/bin/git-timesync 14 | 15 | # Allow permissions for pip installations and git-timesync 16 | RUN chown -R ci:ci /home/ci/.local 17 | 18 | # Allow write permissions for downloading data to opt/mxnet/data 19 | RUN chown -R ci:ci /opt 20 | 21 | USER ci 22 | 23 | ENV PATH="/home/ci/.local/bin:$PATH" 24 | 25 | # Install d2lbook using pip 26 | RUN pip3 install d2lbook==1.0.0 27 | 28 | # Python script to print framework versions 29 | ADD print_versions.py . 30 | 31 | CMD ["/bin/bash"] 32 | -------------------------------------------------------------------------------- /ci/docker/Dockerfile.d2l-tf: -------------------------------------------------------------------------------- 1 | # Use Tensorflow 2.12 (July 2023) 2 | FROM nvcr.io/nvidia/tensorflow:23.07-tf2-py3 3 | 4 | RUN adduser --disabled-password --disabled-login ci 5 | WORKDIR /home/ci 6 | 7 | # Copy d2l_job script 8 | ADD d2l_job.sh . 9 | RUN chmod +x d2l_job.sh; chown ci d2l_job.sh 10 | 11 | # Copy git timesync for caching 12 | ADD git-timesync /home/ci/.local/bin/ 13 | RUN chmod +x /home/ci/.local/bin/git-timesync 14 | 15 | # Allow permissions for pip installations and git-timesync 16 | RUN chown -R ci:ci /home/ci/.local 17 | 18 | USER ci 19 | 20 | ENV PATH="/home/ci/.local/bin:$PATH" 21 | 22 | # Install d2lbook using pip + dependencies for tensorflow d2l 23 | RUN pip3 install d2lbook==1.0.0 tensorflow-probability==0.20.0 24 | 25 | # Python script to print framework versions 26 | ADD print_versions.py . 27 | 28 | CMD ["/bin/bash"] 29 | -------------------------------------------------------------------------------- /ci/docker/Dockerfile.d2l-torch: -------------------------------------------------------------------------------- 1 | # Use PyTorch 2.0 (April 2023) 2 | FROM nvcr.io/nvidia/pytorch:23.04-py3 3 | 4 | RUN adduser --disabled-password --disabled-login ci 5 | WORKDIR /home/ci 6 | 7 | # Copy d2l_job script 8 | ADD d2l_job.sh . 9 | RUN chmod +x d2l_job.sh; chown ci d2l_job.sh 10 | 11 | # Copy git timesync for caching 12 | ADD git-timesync /home/ci/.local/bin/ 13 | RUN chmod +x /home/ci/.local/bin/git-timesync 14 | 15 | # Allow permissions for pip installations and git-timesync 16 | RUN chown -R ci:ci /home/ci/.local 17 | 18 | USER ci 19 | 20 | ENV PATH="/home/ci/.local/bin:$PATH" 21 | 22 | # Install d2lbook using pip + dependencies for torch d2l 23 | RUN pip3 install d2lbook==1.0.0 gym==0.21.0 gpytorch scipy syne-tune[gpsearchers]==0.3.2 24 | 25 | # Python script to print framework versions 26 | ADD print_versions.py . 27 | 28 | CMD ["/bin/bash"] 29 | -------------------------------------------------------------------------------- /ci/docker/d2l_job.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | date 4 | echo "Args: $@" 5 | env 6 | echo "jobId: $AWS_BATCH_JOB_ID" 7 | echo "jobQueue: $AWS_BATCH_JQ_NAME" 8 | echo "computeEnvironment: $AWS_BATCH_CE_NAME" 9 | 10 | SOURCE_REF=$1 11 | WORK_DIR=$2 12 | COMMAND=$3 13 | ############### NOT USED ATM ################## 14 | SAVED_OUTPUT=$4 15 | SAVE_PATH=$5 16 | ############################################### 17 | REMOTE=$6 18 | SAFE_TO_USE_SCRIPT=$7 19 | ORIGINAL_REPO=${8:-'d2l-en'} 20 | # TODO @anirudhdagar: hardcode ORIGINAL_ORG 21 | # Avoid ability to change org by restricting 22 | # job definition arguments defined in d2l-infra 23 | # This is only changed for testing purposes 24 | ORIGINAL_ORG=${9:-'d2l-ai'} 25 | 26 | 27 | # Copy the workflow from master branch 28 | git clone https://github.com/"$ORIGINAL_ORG"/"$ORIGINAL_REPO".git 29 | 30 | WORKFLOW_SCRIPTS="$ORIGINAL_REPO"/.github/workflow_scripts 31 | if [ -d "$WORKFLOW_SCRIPTS" ]; then 32 | cp -R "$ORIGINAL_REPO"/.github/workflow_scripts . 33 | fi 34 | 35 | cd "$ORIGINAL_REPO" 36 | 37 | if [ ! -z $REMOTE ]; then 38 | git remote set-url origin $REMOTE 39 | fi 40 | 41 | git fetch origin $SOURCE_REF:working 42 | git checkout working 43 | 44 | # Reset modification times for all notebooks using git-timesync 45 | # We use this to make sure d2lbook build eval caching is valid 46 | # even after cloning the repo for each run 47 | # Modification times for original repo files are corrected and are now 48 | # good for comparing with modification times of build files coming 49 | # from the S3 bucket 50 | git timesync *.md **/*.md 51 | 52 | # If not safe to use script, we overwrite with the script from master branch 53 | TRUE=true 54 | if [[ ${SAFE_TO_USE_SCRIPT,,} != ${TRUE,,} ]]; then 55 | if [ -d ../workflow_scripts ]; then 56 | rm -rf .github/workflow_scripts 57 | mv ../workflow_scripts .github/ 58 | else 59 | echo Not safe to use user provided script, and could not find script from master branches 60 | exit 1 61 | fi 62 | fi 63 | 64 | cd $WORK_DIR 65 | /bin/bash -o pipefail -c "eval $COMMAND" 66 | COMMAND_EXIT_CODE=$? 67 | 68 | exit $COMMAND_EXIT_CODE 69 | -------------------------------------------------------------------------------- /ci/docker/entry.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | python3 print_versions.py $1 3 | /bin/bash 4 | -------------------------------------------------------------------------------- /ci/docker/login_ecr.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | aws ecr get-login-password --region us-west-2 | docker login --username AWS --password-stdin 650140442593.dkr.ecr.us-west-2.amazonaws.com 4 | -------------------------------------------------------------------------------- /ci/docker/print_versions.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | 4 | if len(sys.argv) > 1: 5 | framework_name = sys.argv[1] 6 | else: 7 | # Assume using d2l-builder docker container 8 | # Here all the frameworks are installed and no CUDA support 9 | framework_name = None 10 | 11 | print("*"*10, "D2L Framework Version Details", "*"*10) 12 | 13 | if framework_name: 14 | # Print CUDA version 15 | print("nvcc --version") 16 | print(os.system("nvcc --version")) 17 | 18 | if framework_name=="pytorch": 19 | # Print PyTorch versions 20 | print(f"Framework Name: {framework_name}") 21 | import torch; print(f"torch version: {torch.__version__}") 22 | import torchvision; print(f"torchvision version: {torchvision.__version__}") 23 | import gym; print(f"gym version: {gym.__version__}") 24 | import gpytorch; print(f"gpytorch version: {gpytorch.__version__}") 25 | import syne_tune; print(f"syne_tune version: {syne_tune.__version__}") 26 | 27 | 28 | if framework_name=="tensorflow": 29 | # Print TensorFlow versions 30 | print(f"Framework Name: {framework_name}") 31 | import tensorflow; print(f"tensorflow version: {tensorflow.__version__}") 32 | import tensorflow_probability; print(f"tensorflow_probability version: {tensorflow_probability.__version__}") 33 | 34 | if framework_name=="jax": 35 | # Print JAX versions 36 | print(f"Framework Name: {framework_name}") 37 | import jax; print(f"jax version: {jax.__version__}") 38 | import jaxlib; print(f"jaxlib version: {jaxlib.__version__}") 39 | import flax; print(f"flax version: {flax.__version__}") 40 | import tensorflow_datasets; print(f"tensorflow_datasets version: {tensorflow_datasets.__version__}") 41 | 42 | if framework_name=="mxnet": 43 | # Print MXNet versions 44 | print(f"Framework Name: {framework_name}") 45 | import mxnet; print(f"MXNet version: {mxnet.__version__}") 46 | 47 | 48 | # Print d2lbook version 49 | import d2lbook; print(f"d2lbook version: {d2lbook.__version__}") 50 | 51 | print("*"*10, "D2L Framework Version Details", "*"*10) 52 | -------------------------------------------------------------------------------- /contrib/chapter_machine-learning-fundamentals/index.md: -------------------------------------------------------------------------------- 1 | # Machine Learning Fundamentals 2 | :label:`chap_ml-fundamentals` 3 | 4 | As illustrated in :numref:`chap_introduction`, 5 | deep learning is just one among many popular methods for solving machine learning problems. 6 | As we have encountered when training 7 | linear regressions, softmax regressions, 8 | and multilayer perceptrons, 9 | optimization algorithms 10 | reduce loss function values 11 | by iteratively updating model parameters. 12 | However, 13 | when we train high-capacity models, 14 | such as deep neural networks, we run the risk of overfitting. 15 | Thus, we will need to provide your first rigorous introduction 16 | to the notions of overfitting, underfitting, and model selection. 17 | To help you combat these problems, 18 | we will introduce regularization techniques such as weight decay and dropout. 19 | In view of many failed machine learning *deployments*, 20 | it is necessary to 21 | expose some common concerns 22 | and stimulate the critical thinking required to detect these situations early, mitigate damage, and use machine learning responsibly. 23 | Throughout, we aim to give you a firm grasp not just of the concepts 24 | but also of the practice of using machine learning models. 25 | At the end of this chapter, 26 | we apply what we have introduced so far to a real case: house price prediction. 27 | We punt matters relating to the computational performance, 28 | scalability, and efficiency of our models to subsequent chapters. 29 | 30 | ```toc 31 | :maxdepth: 2 32 | 33 | model-selection 34 | underfit-overfit 35 | ``` 36 | 37 | -------------------------------------------------------------------------------- /d2l/__init__.py: -------------------------------------------------------------------------------- 1 | """Saved source code for "Dive into Deep Learning" (https://d2l.ai). 2 | 3 | Please import d2l by one of the following ways: 4 | 5 | from d2l import mxnet as d2l # Use MXNet as the backend 6 | from d2l import torch as d2l # Use PyTorch as the backend 7 | from d2l import tensorflow as d2l # Use TensorFlow as the backend 8 | from d2l import jax as d2l # Use Jax as the backend 9 | 10 | """ 11 | 12 | __version__ = "1.0.3" 13 | -------------------------------------------------------------------------------- /graffle/appendix/3dFunc.graffle: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/d2l-ai/d2l-en/23d7a5aecceee57d1292c56e90cce307f183bb0a/graffle/appendix/3dFunc.graffle 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https://raw.githubusercontent.com/d2l-ai/d2l-en/23d7a5aecceee57d1292c56e90cce307f183bb0a/img/where-wally-walker-books.jpg -------------------------------------------------------------------------------- /index.md: -------------------------------------------------------------------------------- 1 | Dive into Deep Learning 2 | ======================== 3 | 4 | ```eval_rst 5 | .. raw:: html 6 | :file: frontpage.html 7 | ``` 8 | 9 | 10 | ```toc 11 | :maxdepth: 1 12 | 13 | chapter_preface/index 14 | chapter_installation/index 15 | chapter_notation/index 16 | ``` 17 | 18 | 19 | ```toc 20 | :maxdepth: 2 21 | :numbered: 22 | 23 | chapter_introduction/index 24 | chapter_preliminaries/index 25 | chapter_linear-regression/index 26 | chapter_linear-classification/index 27 | chapter_multilayer-perceptrons/index 28 | chapter_builders-guide/index 29 | chapter_convolutional-neural-networks/index 30 | chapter_convolutional-modern/index 31 | chapter_recurrent-neural-networks/index 32 | chapter_recurrent-modern/index 33 | chapter_attention-mechanisms-and-transformers/index 34 | chapter_optimization/index 35 | chapter_computational-performance/index 36 | chapter_computer-vision/index 37 | chapter_natural-language-processing-pretraining/index 38 | chapter_natural-language-processing-applications/index 39 | chapter_reinforcement-learning/index 40 | chapter_gaussian-processes/index 41 | chapter_hyperparameter-optimization/index 42 | chapter_generative-adversarial-networks/index 43 | chapter_recommender-systems/index 44 | chapter_appendix-mathematics-for-deep-learning/index 45 | chapter_appendix-tools-for-deep-learning/index 46 | 47 | ``` 48 | 49 | 50 | ```toc 51 | :maxdepth: 1 52 | 53 | chapter_references/zreferences 54 | ``` 55 | 56 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, find_packages 2 | import d2l 3 | 4 | requirements = [ 5 | 'jupyter==1.0.0', 6 | 'numpy==1.23.5', 7 | 'matplotlib==3.7.2', 8 | 'matplotlib-inline==0.1.6', 9 | 'requests==2.31.0', 10 | 'pandas==2.0.3', 11 | 'scipy==1.10.1' 12 | ] 13 | 14 | setup( 15 | name='d2l', 16 | version=d2l.__version__, 17 | python_requires='>=3.8', 18 | author='D2L Developers', 19 | author_email='d2l.devs@gmail.com', 20 | url='https://d2l.ai', 21 | description='Dive into Deep Learning', 22 | license='MIT-0', 23 | packages=find_packages(), 24 | zip_safe=True, 25 | install_requires=requirements, 26 | ) 27 | -------------------------------------------------------------------------------- /static/build.yml: -------------------------------------------------------------------------------- 1 | dependencies: 2 | - python=3.9 3 | - pip 4 | - pip: 5 | - .. # d2l 6 | - mxnet-cu112==1.9.1 7 | - torch==2.0.0+cu118 8 | - -f https://download.pytorch.org/whl/torch_stable.html 9 | - torchvision==0.15.1+cu118 10 | - -f https://download.pytorch.org/whl/torch_stable.html 11 | # pip install CUDNN 8.6 required for TF & JAX 12 | - nvidia-cudnn-cu11==8.6.0.163 13 | - tensorflow==2.12.0 14 | - tensorflow-probability==0.20.0 15 | - tensorflow-datasets==4.8.3 16 | - "jax[cuda11_pip]==0.4.13" 17 | - -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html 18 | - flax==0.7.0 19 | # Chapter Specific Dependencies for PyTorch 20 | - syne-tune[gpsearchers]==0.3.2 21 | - gym==0.21.0 22 | - gpytorch 23 | -------------------------------------------------------------------------------- /static/cache.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | if [ $# -ne 2 ]; then 4 | echo "usage:" 5 | echo " $0 store dir" 6 | echo " $0 restore dir" 7 | exit -1 8 | fi 9 | 10 | cmd=$1 11 | dir=$2 12 | saved_dir="${dir///data/_data}" 13 | 14 | if [ $cmd == "store" ]; then 15 | if [ -e $dir ]; then 16 | rm -rf $saved_dir 17 | mv $dir $saved_dir 18 | fi 19 | echo "Saved $dir to $saved_dir" 20 | elif [ $cmd == "restore" ]; then 21 | if [ -e $saved_dir ]; then 22 | rm -rf $dir 23 | mv $saved_dir $dir 24 | fi 25 | echo "Restored $dir from $saved_dir" 26 | else 27 | echo "unknown command $1, should be either store or restore" 28 | exit -1 29 | fi 30 | -------------------------------------------------------------------------------- /static/clean_img.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | for fn in img/*; do 4 | if grep -qrl $fn */*.md; then 5 | continue 6 | fi 7 | echo "Remove $fn" 8 | rm $fn 9 | done 10 | -------------------------------------------------------------------------------- /static/d2l.css: -------------------------------------------------------------------------------- 1 | .globaltoc ul:first-of-type li.toctree-l1 .link-wrapper>a { 2 | padding-left: 18px; 3 | } 4 | -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /static/frontpage/attachments/sagemaker.txt: -------------------------------------------------------------------------------- 1 | If you plan to use D2L to teach your class in the 2021 Spring semester, you may apply for free computing resources on AWS for your class, such as Amazon SageMaker and other AWS ML/AI services. 2 | 3 | Please email to sagemaker-edu@amazon.com by 11/22/2020 with 4 | 5 | i) your name, email, title, department, and school; 6 | ii) your course name/level, class start/end date, and the number of students; 7 | iii) course content and how D2L and Amazon SageMaker will be used in teaching. 8 | -------------------------------------------------------------------------------- /static/latex-logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/d2l-ai/d2l-en/23d7a5aecceee57d1292c56e90cce307f183bb0a/static/latex-logo.png 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-------------------------------------------------------------------------------- 1 | %% OPTIONS FOR GEOMETRY 2 | %% Keep this empty to override the default sphinx style -------------------------------------------------------------------------------- /static/logo-with-text.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/d2l-ai/d2l-en/23d7a5aecceee57d1292c56e90cce307f183bb0a/static/logo-with-text.png -------------------------------------------------------------------------------- /static/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/d2l-ai/d2l-en/23d7a5aecceee57d1292c56e90cce307f183bb0a/static/logo.png -------------------------------------------------------------------------------- /static/vol1/root-index.md: -------------------------------------------------------------------------------- 1 | Dive into Deep Learning 2 | ======================== 3 | 4 | ```eval_rst 5 | .. raw:: html 6 | :file: frontpage.html 7 | ``` 8 | 9 | 10 | ```toc 11 | :maxdepth: 1 12 | 13 | chapter_preface/index 14 | chapter_installation/index 15 | chapter_notation/index 16 | ``` 17 | 18 | 19 | ```toc 20 | :maxdepth: 2 21 | :numbered: 22 | 23 | chapter_introduction/index 24 | chapter_preliminaries/index 25 | chapter_linear-regression/index 26 | chapter_linear-classification/index 27 | chapter_multilayer-perceptrons/index 28 | chapter_builders-guide/index 29 | chapter_convolutional-neural-networks/index 30 | chapter_convolutional-modern/index 31 | chapter_recurrent-neural-networks/index 32 | chapter_recurrent-modern/index 33 | chapter_attention-mechanisms-and-transformers/index 34 | chapter_appendix-tools-for-deep-learning/index 35 | 36 | ``` 37 | 38 | 39 | ```toc 40 | :maxdepth: 1 41 | 42 | chapter_references/zreferences 43 | ``` 44 | 45 | -------------------------------------------------------------------------------- /static/vol1/tools-index.md: -------------------------------------------------------------------------------- 1 | # Appendix: Tools for Deep Learning 2 | :label:`chap_appendix_tools` 3 | 4 | 5 | To get the most out of *Dive into Deep Learning*, 6 | we will talk you through different tools 7 | in this appendix, 8 | such as 9 | for running and contributing to this 10 | interactive open-source book. 11 | 12 | ```toc 13 | :maxdepth: 2 14 | 15 | jupyter 16 | sagemaker 17 | aws 18 | colab 19 | selecting-servers-gpus 20 | contributing 21 | d2l 22 | ``` 23 | 24 | -------------------------------------------------------------------------------- /vol1-activate.sh: -------------------------------------------------------------------------------- 1 | cp static/vol1/vol1-config.ini config.ini 2 | cp static/vol1/root-index.md index.md 3 | cp static/vol1/tools-index.md chapter_appendix-tools-for-deep-learning/index.md 4 | --------------------------------------------------------------------------------