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Title: GitHub - M3Dade/Dive-into-DL-TensorFlow2.0: 本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的同意 · GitHub

Open Graph Title: GitHub - M3Dade/Dive-into-DL-TensorFlow2.0: 本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的同意

X Title: GitHub - M3Dade/Dive-into-DL-TensorFlow2.0: 本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的同意

Description: 本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的同意 - M3Dade/Dive-into-DL-TensorFlow2.0

Open Graph Description: 本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的同意 - M3Dade/Dive-into-DL-TensorFlow2.0

X Description: 本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的同意 - M3Dade/Dive-into-DL-TensorFlow2.0

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og:image:alt本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的同意 - M3Dade/Dive-into-DL-TensorFlow2.0
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简介https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs
阅读指南https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/read_guide.md
1. 深度学习简介https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter01_DL-intro/deep-learning-intro.md
2.1 环境配置https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter02_prerequisite/2.1_install.md
2.2 数据操作https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter02_prerequisite/2.2_tensor.md
2.3 自动求梯度https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter02_prerequisite/2.3_autograd.md
2.4 查阅文档https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter02_prerequisite/2.4_document.md
3.1 线性回归https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter03_DL-basics/3.1_linear-regression.md
3.2 线性回归的从零开始实现https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter03_DL-basics/3.2_linear-regression-scratch.md
3.3 线性回归的简洁实现https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter03_DL-basics/3.3_linear-regression-tensorflow2.0.md
3.4 softmax回归https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter03_DL-basics/3.4_softmax-regression.md
3.5 图像分类数据集（Fashion-MNIST）https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter03_DL-basics/3.5_fashion-mnist.md
3.6 softmax回归的从零开始实现https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter03_DL-basics/3.6_softmax-regression-scratch.md
3.7 softmax回归的简洁实现https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter03_DL-basics/3.7_softmax-regression-tensorflow2.0.md
3.8 多层感知机https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter03_DL-basics/3.8_mlp.md
3.9 多层感知机的从零开始实现https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter03_DL-basics/3.9_mlp-scratch.md
3.10 多层感知机的简洁实现https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter03_DL-basics/3.10_mlp-tensorflow2.0.md
3.11 模型选择、欠拟合和过拟合https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter03_DL-basics/3.11_underfit-overfit.md
3.12 权重衰减https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter03_DL-basics/3.12_weight-decay.md
3.13 丢弃法https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter03_DL-basics/3.13_dropout.md
3.14 正向传播、反向传播和计算图https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter03_DL-basics/3.14_backprop.md
3.15 数值稳定性和模型初始化https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter03_DL-basics/3.15_numerical-stability-and-init.md
3.16 实战Kaggle比赛：房价预测https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter03_DL-basics/3.16_kaggle-house-price.md
4.1 模型构造https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter04_DL-computation/4.1_model-construction.md
4.2 模型参数的访问、初始化和共享https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter04_DL-computation/4.2_parameters.md
4.3 模型参数的延后初始化https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter04_DL-computation/4.3_deferred-init.md
4.4 自定义层https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter04_DL-computation/4.4_custom-layer.md
4.5 读取和存储https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter04_DL-computation/4.5_read-write.md
4.6 GPU计算https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter04_DL-computation/4.6_use-gpu.md
5.1 二维卷积层https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter05_CNN/5.1_conv-layer.md
5.2 填充和步幅https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter05_CNN/5.2_padding-and-strides.md
5.3 多输入通道和多输出通道https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter05_CNN/5.3_channels.md
5.4 池化层https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter05_CNN/5.4_pooling.md
5.5 卷积神经网络（LeNet）https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter05_CNN/5.5_lenet.md
5.6 深度卷积神经网络（AlexNet）https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter05_CNN/5.6_alexnet.md
5.7 使用重复元素的网络（VGG）https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter05_CNN/5.7_vgg.md
5.8 网络中的网络（NiN）https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter05_CNN/5.8_nin.md
5.9 含并行连结的网络（GoogLeNet）https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter05_CNN/5.9_googlenet.md
5.10 批量归一化https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter05_CNN/5.10_batch-norm.md
5.11 残差网络（ResNet）https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter05_CNN/5.11_resnet.md
5.12 稠密连接网络（DenseNet）https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter05_CNN/5.12_densenet.md
6.1 语言模型https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter06_RNN/6.1_lang-model.md
6.2 循环神经网络https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter06_RNN/6.2_rnn.md
6.3 语言模型数据集（周杰伦专辑歌词）https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter06_RNN/6.3_lang-model-dataset.md
6.4 循环神经网络的从零开始实现https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter06_RNN/6.4_rnn-scratch.md
6.5 循环神经网络的简洁实现https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter06_RNN/6.5_rnn-pytorch.md
6.6 通过时间反向传播https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter06_RNN/6.6_bptt.md
6.7 门控循环单元（GRU）https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter06_RNN/6.7_gru.md
6.8 长短期记忆（LSTM）https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter06_RNN/6.8_lstm.md
6.9 深度循环神经网络https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter06_RNN/6.9_deep-rnn.md
6.10 双向循环神经网络https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter06_RNN/6.10_bi-rnn.md
7.1 优化与深度学习https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter07_optimization/7.1_optimization-intro.md
7.2 梯度下降和随机梯度下降https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter07_optimization/7.2_gd-sgd.md
7.3 小批量随机梯度下降https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter07_optimization/7.3_minibatch-sgd.md
7.4 动量法https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter07_optimization/7.4_momentum.md
7.5 AdaGrad算法https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter07_optimization/7.5_adagrad.md
7.6 RMSProp算法https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter07_optimization/7.6_rmsprop.md
7.7 AdaDelta算法https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter07_optimization/7.7_adadelta.md
7.8 Adam算法https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter07_optimization/7.8_adam.md
8.1 命令式和符号式混合编程https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter08_computational-performance/8.1_hybridize.md
8.2 异步计算https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter08_computational-performance/8.2_async-computation.md
8.3 自动并行计算https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter08_computational-performance/8.3_auto-parallelism.md
8.4 多GPU计算https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter08_computational-performance/8.4_multiple-gpus.md
9.1 图像增广https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter09_computer-vision/9.1_image-augmentation.md
9.2 微调https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter09_computer-vision/9.2_fine-tuning.md
9.3 目标检测和边界框https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter09_computer-vision/9.3_bounding-box.md
9.4 锚框https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter09_computer-vision/9.4_anchor.md
9.5 多尺度目标检测https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter09_computer-vision/9.5_multiscale-object-detection.md
9.6 目标检测数据集（皮卡丘）https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter09_computer-vision/9.6_object-detection-dataset.md
10.1 词嵌入（word2vec）https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter10_natural-language-processing/10.1_word2vec.md
10.2 近似训练https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter10_natural-language-processing/10.2_approx-training.md
10.3 word2vec的实现https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter10_natural-language-processing/10.3_word2vec-pytorch.md
10.4 子词嵌入（fastText）https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter10_natural-language-processing/10.4_fasttext.md
10.5 全局向量的词嵌入（GloVe）https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter10_natural-language-processing/10.5_glove.md
10.6 求近义词和类比词https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter10_natural-language-processing/10.6_similarity-analogy.md
10.7 文本情感分类：使用循环神经网络https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter10_natural-language-processing/10.7_sentiment-analysis-rnn.md
10.8 文本情感分类：使用卷积神经网络（textCNN）https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter10_natural-language-processing/10.8_sentiment-analysis-cnn.md
10.9 编码器—解码器（seq2seq）https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter10_natural-language-processing/10.9_seq2seq.md
10.10 束搜索https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter10_natural-language-processing/10.10_beam-search.md
10.11 注意力机制https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter10_natural-language-processing/10.11_attention.md
10.12 机器翻译https://github.com/M3Dade/Dive-into-DL-TensorFlow2.0/blob/master/docs/chapter10_natural-language-processing/10.12_machine-translation.md
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