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文档d2l-ai/d2l-pytorch-slides
此仓库包含生成的笔记本幻灯片。要在本地打开它，我们建议您安装rise扩展。 你也可以在nbviewer中预览它们：
- chapter_preliminaries/ndarray.ipynb
- chapter_preliminaries/pandas.ipynb
- chapter_preliminaries/linear-algebra.ipynb
- chapter_preliminaries/calculus.ipynb
- chapter_preliminaries/autograd.ipynb
- chapter_preliminaries/lookup-api.ipynb
- chapter_linear-regression/linear-regression.ipynb
- chapter_linear-regression/synthetic-regression-data.ipynb
- chapter_linear-regression/linear-regression-scratch.ipynb
- chapter_linear-regression/linear-regression-concise.ipynb
- chapter_linear-regression/weight-decay.ipynb
- chapter_linear-classification/image-classification-dataset.ipynb
- chapter_linear-classification/classification.ipynb
- chapter_linear-classification/softmax-regression-scratch.ipynb
- chapter_linear-classification/softmax-regression-concise.ipynb
- chapter_multilayer-perceptrons/mlp.ipynb
- chapter_multilayer-perceptrons/mlp-implementation.ipynb
- chapter_multilayer-perceptrons/numerical-stability-and-init.ipynb
- chapter_multilayer-perceptrons/dropout.ipynb
- chapter_multilayer-perceptrons/kaggle-house-price.ipynb
- chapter_builders-guide/model-construction.ipynb
- chapter_builders-guide/parameters.ipynb
- chapter_builders-guide/init-param.ipynb
- chapter_builders-guide/custom-layer.ipynb
- chapter_builders-guide/read-write.ipynb
- chapter_builders-guide/use-gpu.ipynb
- chapter_convolutional-neural-networks/conv-layer.ipynb
- 卷积神经网络/填充和步幅.ipynb
- 卷积神经网络/通道.ipynb
- 卷积神经网络/池化.ipynb
- 卷积神经网络/LeNet.ipynb
- 现代卷积网络/AlexNet.ipynb
- 现代卷积网络/VGG.ipynb
- 现代卷积网络/NiN.ipynb
- 现代卷积网络/GoogLeNet.ipynb
- 现代卷积网络/批量归一化.ipynb
- 现代卷积网络/ResNet.ipynb
- 现代卷积网络/DenseNet.ipynb
- 循环神经网络/序列.ipynb
- 循环神经网络/文本序列.ipynb
- 循环神经网络/语言模型.ipynb
- 循环神经网络/循环神经网络从零实现.ipynb
- 循环神经网络/循环神经网络简洁实现.ipynb
- 现代循环神经网络/长短期记忆网络.ipynb
- 现代循环神经网络/门控循环单元.ipynb
- 现代循环神经网络/深度循环神经网络.ipynb
- 现代循环神经网络/机器翻译与数据集.ipynb
- 现代循环神经网络/编码器-解码器架构.ipynb
- 现代循环神经网络/序列到序列学习.ipynb
- 注意力机制和Transformer/注意力汇聚.ipynb
- 注意力机制和Transformer/注意力评分函数.ipynb
- 注意力机制和Transformer/Bahdanau注意力.ipynb
- 注意力机制和Transformer/多头注意力.ipynb
- chapter_attention-mechanisms-and-transformers/self-attention-and-positional-encoding.ipynb
- chapter_attention-mechanisms-and-transformers/transformer.ipynb
- chapter_computational-performance/multiple-gpus.ipynb
- chapter_computational-performance/multiple-gpus-concise.ipynb
- chapter_computer-vision/image-augmentation.ipynb
- chapter_computer-vision/fine-tuning.ipynb
- chapter_computer-vision/bounding-box.ipynb
- chapter_computer-vision/anchor.ipynb
- chapter_computer-vision/multiscale-object-detection.ipynb
- chapter_computer-vision/object-detection-dataset.ipynb
- chapter_computer-vision/ssd.ipynb
- chapter_computer-vision/semantic-segmentation-and-dataset.ipynb
- chapter_computer-vision/transposed-conv.ipynb
- chapter_computer-vision/fcn.ipynb
- chapter_computer-vision/neural-style.ipynb
- chapter_computer-vision/kaggle-cifar10.ipynb
- chapter_computer-vision/kaggle-dog.ipynb
- chapter_natural-language-processing-pretraining/bert.ipynb
- chapter_natural-language-processing-pretraining/bert-dataset.ipynb
- chapter_natural-language-processing-pretraining/bert-pretraining.ipynb
- chapter_natural-language-processing-applications/natural-language-inference-and-dataset.ipynb
- chapter_natural-language-processing-applications/natural-language-inference-bert.ipynb
