PyTorch动手学深度学习 笔记目录

PyTorch动手学深度学习 目录
1_deep-learning-intro
2.1_install
2.2_tensor
2.3_autograd
3.1_linear-regression
3.2_linear-regression-scratch
3.3_linear-regression-pytorch
3.4_softmax-regression
3.5_fashion-mnist
3.6_softmax-regression-scratch
3.7_softmax-regression-pytorch
3.8_mlp
3.9_mlp-scratch
3.10_mlp-pytorch
3.11_underfit-overfit
3.12_weight-decay
3.13_dropout
3.14_backprop
3.15_numerical-stability-and-init
3.16_kaggle-house-price
4.1_model-construction
4.2_parameters
4.3_deferred-init
4.4_custom-layer
4.5_read-write
4.6_use-gpu
5.1_conv-layer
5.2_padding-and-strides
5.3_channels
5.4_pooling
5.5_lenet
5.6_alexnet
5.7_vgg
5.8_nin
5.9_googlenet
5.10_batch-norm
5.11_resnet
5.12_densenet
6.1_lang-model
6.2_rnn
6.3_lang-model-dataset
6.4_rnn-scratch
6.5_rnn-pytorch
6.6_bptt
6.7_gru
6.8_lstm
6.9_deep-rnn
6.10_bi-rnn
7.1_optimization-intro
7.2_gd-sgd
7.3_minibatch-sgd
7.4_momentum
7.5_adagrad
7.6_rmsprop
7.7_adadelta
7.8_adam
8.1_hybridize
8.2_async-computation
8.3_auto-parallelism
8.4_multiple-gpus
9.1_image-augmentation
9.2_fine-tuning
9.3_bounding-box
9.4_anchor
9.5_multiscale-object-detection
9.6_object-detection-dataset
9.8_rcnn
9.9_semantic-segmentation-and-dataset
9.11_neural-style
10.1_word2vec
10.2_approx-training
10.3_word2vec-pytorch
10.4_fasttext
10.5_glove
10.6_similarity-analogy
10.7_sentiment-analysis-rnn
10.8_sentiment-analysis-cnn
10.9_seq2seq
10.10_beam-search
10.11_attention
10.12_machine-translation

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