# awesome-deep-learning
学习过程中，收集的深度学习资料，在不断更新中。
如果您有好的学习资料，请联系我们，QQ群：791193818
## 目录
- [1 深度学习基础知识](https://github.com/DenseAI/awesome-deep-learning#1-深度学习基础知识)
- [1.1 数学基础](https://github.com/DenseAI/awesome-deep-learning#11-数学基础)
- [1.2 网络元素](https://github.com/DenseAI/awesome-deep-learning#12-网络元素)
- [1.3 网络结构](https://github.com/DenseAI/awesome-deep-learning#13-网络结构)
- [1.4 优化算法](https://github.com/DenseAI/awesome-deep-learning#14-优化算法)
- [1.5 深度学习例子](https://github.com/DenseAI/awesome-deep-learning#15-深度学习例子)
- [2 目标检测](https://github.com/DenseAI/awesome-deep-learning#2-目标识别)
- [2.1 综述](https://github.com/DenseAI/awesome-deep-learning#21-综述)
- [2.2 计算机视觉基础](https://github.com/DenseAI/awesome-deep-learning#22-计算机视觉基础)
- [2.3 目标检测框架](https://github.com/DenseAI/awesome-deep-learning#23-目标检测框架)
- [2.4 代码详解](https://github.com/DenseAI/awesome-deep-learning#24-代码详解)
- [2.5 人脸识别](https://github.com/DenseAI/awesome-deep-learning#25-人脸识别)
- [3 强化学习](https://github.com/DenseAI/awesome-deep-learning#3-强化学习)
- [3.1 基础知识](https://github.com/DenseAI/awesome-deep-learning#31-基础知识)
- [3.2 强化学习基础](https://github.com/DenseAI/awesome-deep-learning#32-强化学习基础)
- [3.3 强化学习与Python](https://github.com/DenseAI/awesome-deep-learning#33-强化学习与python)
- [3.4 AlphaGo Zero](https://github.com/DenseAI/awesome-deep-learning#34-alphago-zero)
- [4 生成对抗网络（GAN）](https://github.com/DenseAI/awesome-deep-learning#4-生成对抗网络gan)
- [4.1 综述](https://github.com/DenseAI/awesome-deep-learning#41-综述)
- [4.2 各种类型的GAN](https://github.com/DenseAI/awesome-deep-learning#42-各种类型的gan)
- [4.3 生成模型](https://github.com/DenseAI/awesome-deep-learning#43-生成模型)
- [5 自然语言处理(NLP)](https://github.com/DenseAI/awesome-deep-learning#5-自然语言处理nlp)
- [5.1 词向量（Word2vec）](https://github.com/DenseAI/awesome-deep-learning#51-词向量word2vec)
- [5.2 注意力机制（Attention Mechanism）](https://github.com/DenseAI/awesome-deep-learning#52-注意力机制attention-mechanism)
## 0 更新 Update
#### 0.1 Causal Discovery
- [Causal Discovery with Reinforcement Learning](https://openreview.net/forum?id=S1g2skStPB) ShengyuZhu, IgnavierNg, ZhitangChen.
- [A Graph Autoencoder Approach to Causal Structure Learning](https://arxiv.org/abs/1911.07420) Ignavier Ng, Shengyu Zhu, Zhitang Chen, Zhuangyan Fang
##### 0.1.1 Software
- [Trustworthy AI](https://github.com/huawei-noah/trustworthyAI)
## 1 深度学习基础知识
#### 1.1 数学基础
包含深度神经网络、卷积神经网络、循环神经网络的前向传播、后向传播公式推导，以及损失函数和激活函数的推导，学习重点：前向、后向公式推导、激活函数、损失函数，特别是为什么使用交叉熵损失函数。
- [深度神经网络（DNN）模型与前向传播算法](https://www.cnblogs.com/pinard/p/6418668.html)
- [深度神经网络（DNN）反向传播算法(BP)](https://www.cnblogs.com/pinard/p/6422831.html)
- [深度神经网络（DNN）损失函数和激活函数的选择](https://www.cnblogs.com/pinard/p/6437495.html)
- [深度神经网络（DNN）的正则化](https://www.cnblogs.com/pinard/p/6472666.html)
- [卷积神经网络(CNN)模型结构](https://www.cnblogs.com/pinard/p/6483207.html)
- [卷积神经网络(CNN)前向传播算法](https://www.cnblogs.com/pinard/p/6489633.html)
- [卷积神经网络(CNN)反向传播算法](https://www.cnblogs.com/pinard/p/6494810.html)
- [循环神经网络(RNN)模型与前向反向传播算法](https://www.cnblogs.com/pinard/p/6509630.html)
- [LSTM模型与前向反向传播算法](https://www.cnblogs.com/pinard/p/6519110.html)
#### 1.2 网络元素
- [多层感知机MLP](http://zh.d2l.ai/chapter_deep-learning-basics/mlp.html)
卷积神经网络，学习重点：核、步幅、填充、池化
- [卷积神经网络CNN](https://www.jianshu.com/p/70b6f5653ac6)
- [二维卷积层](http://zh.d2l.ai/chapter_convolutional-neural-networks/conv-layer.html)
- [填充和步幅](http://zh.d2l.ai/chapter_convolutional-neural-networks/padding-and-strides.html)
- [多输入通道和多输出通道](http://zh.d2l.ai/chapter_convolutional-neural-networks/channels.html)
- [池化层](http://zh.d2l.ai/chapter_convolutional-neural-networks/pooling.html)
循环神经网络，学习重点：BPTT、LSTM、GRU
- [循环神经网络RNN](https://www.jianshu.com/p/39a99c88a565)
- [循环神经网络](http://zh.d2l.ai/chapter_recurrent-neural-networks/rnn.html)
- [通过时间反向传播BPTT](http://zh.d2l.ai/chapter_recurrent-neural-networks/bptt.html)
- [长短期记忆（LSTM）](http://zh.d2l.ai/chapter_recurrent-neural-networks/lstm.html)
- [门控循环单元（GRU）](http://zh.d2l.ai/chapter_recurrent-neural-networks/gru.html)
- [双向循环神经网络](http://zh.d2l.ai/chapter_recurrent-neural-networks/bi-rnn.html)
#### 1.3 网络结构
网络结构，学习重点：VGG、GooLeNet、ResNet、DPN
- [LeNet](https://blog.csdn.net/chenyuping333/article/details/82177677)
- [LeNet-Keras](https://github.com/DustinAlandzes/mnist-lenet-keras/blob/master/lenet.py)
- [AlexNet](https://blog.csdn.net/chenyuping333/article/details/82178335)
- [AlexNet-Keras](https://github.com/uestcsongtaoli/AlexNet/blob/master/model.py)
- [VGG](https://blog.csdn.net/chenyuping333/article/details/82250931)
- [VGG16-Keras](https://github.com/keras-team/keras-applications/blob/master/keras_applications/vgg16.py)
- [VGG19-Keras](https://github.com/keras-team/keras-applications/blob/master/keras_applications/vgg19.py)
- [GoogLeNet](https://blog.csdn.net/chenyuping333/article/details/82343608)
- [GoogLeNet-Keras](https://github.com/dingchenwei/googLeNet/blob/master/googLeNet.py)
- [inception_resnet_v2-Keras](https://github.com/keras-team/keras-applications/blob/master/keras_applications/inception_resnet_v2.py)
- [inception_v3-Keras](https://github.com/keras-team/keras-applications/blob/master/keras_applications/inception_v3.py)
- [ResNet](https://blog.csdn.net/chenyuping333/article/details/82344334)
- [resnet-Keras](https://github.com/keras-team/keras-applications/blob/master/keras_applications/resnet.py)
- [resnet_v2-Keras](https://github.com/keras-team/keras-applications/blob/master/keras_applications/resnet_v2.py)
- [DenseNet](https://blog.csdn.net/chenyuping333/article/details/82414542)
- [DenseNet-Keras](https://github.com/keras-team/keras-applications/blob/master/keras_applications/densenet.py)
- [ResNeXt](https://blog.csdn.net/chenyuping333/article/details/82453632)
- [ResNext-Keras](https://github.com/keras-team/keras-applications/blob/master/keras_applications/resnext.py)
- [DPN Dual Path Net](https://blog.csdn.net/chenyuping333/article/details/82453965)
- [DPN-Keras](https://github.com/titu1994/Keras-DualPathNetworks/blob/master/dual_path_network.py)
- [SeNet](http://www.sohu.com/a/161633191_465975)
- [SeNet-Caffe](https://github.com/hujie-frank/SENet)
#### 1.4 优化算法
优化算法，学习重点：SGD、AdaGrad、RMSProp、Adam
- [随机梯度下降SGD](http://zh.d2l.ai/chapter_optimization/gd-sgd.html)
- [动量法](http://zh.d2l.ai/chapter_optimization/momentum.html)
- [AdaGrad算法](http://zh.d2l.ai/chapter_optimization/adagrad.html)
- [RMSProp算法](http://zh.d2l.ai/chapter_optimization/rmsprop.html)
- [AdaDelta算法](http://zh.d2l.ai/chapter_optimization/adadelta.html)
- [Adam算法](http://zh.d2l.ai/chapter_optimization/adam.html)
#### 1.5 深度学习例子
使用Keras进行文本分类、数字分类、图像分类，了解基本深
