# DeepLearning.ai深度学习课程笔记

## DeepLearning.ai深度学习课程笔记

- [Introduction](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/master.md)
- [第一门课 神经网络和深度学习(Neural-Networks-and-Deep-Learning)](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning.md)
- [第一周：深度学习引言(Introduction to Deep Learning)](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/chapter1.md)
- [1.1 神经网络的监督学习(Supervised Learning with Neural Networks)](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/chapter1/logistic-regression-as-a-neural-network.md)
- [1.2 为什么神经网络会流行？(Why is Deep Learning taking off?)](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/chapter1/why-is-deep-learning-taking-off.md)
- [第二周：神经网络的编程基础(Basics of Neural Network programming)](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-2.md)
- [2.1 二分类(Binary Classification)](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-2/binary-classification.md)
- [2.2 逻辑回归(Logistic Regression)](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-2/logistic-regression.md)
- [2.3 逻辑回归的代价函数（Logistic Regression Cost Function）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-2/logistic-regression-cost-function.md)
- [2.4 逻辑回归的梯度下降（Logistic Regression Gradient Descent）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-2/computation-graph.md)
- [2.5 梯度下降的例子(Gradient Descent on m Examples)](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-2/gradient-descent-on-m-examples.md)
- [2.6 向量化 logistic 回归的梯度输出（Vectorizing Logistic Regression’s Gradient Output）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-2/vectorizing-logistic-regressions-gradient-output.md)
- [2.7 （选修）logistic 损失函数的解释（Explanation of logistic regression cost function ）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-2/explanation-of-logistic-regression-cost-functionoptional.md)
- [Logistic Regression with a Neural Network mindset 代码](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-2/logistic-regression-with-a-neural-network-mindset-v5.md)
- [lr\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-2/lrutils-py.md)
- [第三周：浅层神经网络(Shallow neural networks)](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-3.md)
- [3.1 神经网络概述（Neural Network Overview）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-3/neural-networks-overview.md)
- [3.2 神经网络的表示（Neural Network Representation ）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-3/neural-network-representation.md)
- [3.3 计算一个神经网络的输出（Computing a Neural Network's output ）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-3/computing-a-neural-networks-output.md)
- [3.4 多样本向量化（Vectorizing across multiple examples ）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-3/vectorizing-across-multiple-examples.md)
- [3.5 激活函数（Activation functions）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-3/activation-functions.md)
- [3.6 为什么需要（ 非线性激活函数？（why need a nonlinear activation function?）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-3/why-do-you-need-non-linear-activation-functions.md)
- [3.7 激活函数的导数（Derivatives of activation functions ）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-3/derivatives-of-activation-functions.md)
- [3.8 神经网络的梯度下降（Gradient descent for neural networks）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-3/gradient-descent-for-neural-networks.md)
- [3.9 （选修）直观理解反向传播（Backpropagation intuition ）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-3/backpropagation-intuition.md)
- [3.10 随机初始化（Random+Initialization）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-3/random-initialization.md)
- [Planar data classification with one hidden layer](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-3/planar-data-classification-with-one-hidden-layer-v5.md)
- [planar\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-3/planarutils-py.md)
- [testCases.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-3/testcasesv2-py.md)
- [第四周：深层神经网络(Deep Neural Networks)](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-4.md)
- [4.1 深层神经网络（Deep L-layer neural network）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-4/deep-l-layer-neural-network.md)
- [4.2 前向传播和反向传播（Forward and backward propagation）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-4/forward-and-backward-propagation.md)
- [4.3 深层网络中的前向传播（Forward propagation in a Deep Network ）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-4/forward-propagation-in-a-deep-network.md)
- [4.4 为什么使用深层表示？（Why deep representations?）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-4/why-deep-representations.md)
- [4.5 搭建神经网络块（Building blocks of deep neural networks）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-4/building-blocks-of-deep-neural-networks.md)
- [4.6 参数 VS 超参数（Parameters vs Hyperparameters）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-4/parameters-vs-hyperparameters.md)
- [Building your Deep Neural Network Step by Step](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-4/building-your-deep-neural-network-step-by-step.md)
- [dnn\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-4/dnnutils-v2-py.md)
- [testCases.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-4/testcases.md)
- [Deep Neural Network Application](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-4/deep-neural-network-application.md)
- [dnn\_app\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/neural-networks-and-deep-learning/week-4/dnnapputils-py.md)
- [第二门课 改善深层神经网络：超参数调试、正则化以及优化(Improving Deep Neural Networks:Hyperparameter tuning, Regularization and](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks.md)
- [第一周：深度学习的实用层面(Practical aspects of Deep Learning)](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning.md)
- [1.1 训练，验证，测试集（Train / Dev / Test sets）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/11-xun-lian-ff0c-yan-zheng-ff0c-ceshi-ji-ff08-train-dev-test-sets.md)
- [1.2 偏差，方差（Bias /Variance）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/12-pian-cha-ff0c-fang-cha-ff08-bias-variance.md)
- [1.3 机器学习基础（Basic Recipe for Machine Learning）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/13-ji-qi-xue-xi-ji-chu-ff08-basic-recipe-for-machine-learning.md)
- [1.4 正则化（Regularization）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/14-zheng-ze-hua-ff08-regularization.md)
- [1.5 为什么正则化有利于预防过拟合呢？（Why regularization reduces overfitting?）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/15-wei-shi-yao-zheng-ze-hua-you-li-yu-yu-fang-guo-ni-he-ni-ff1f-ff08-why-regularization-reduces-over.md)
- [1.6 dropout 正则化（Dropout Regularization）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/16-dropout-zheng-ze-hua-ff08-dropout-regularization.md)
- [1.7 理解 dropout（Understanding Dropout）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/17-li-jie-dropout-understanding-dropout.md)
- [1.8 其他正则化方法（Other regularization methods）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/18-qi-ta-zheng-ze-hua-fang-fa-ff08-other-regularization-methods.md)
- [1.9 归一化输入（Normalizing inputs）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/19-gui-yi-hua-shu-ru-ff08-normalizing-inputs.md)
- [1.10 梯度消失/梯度爆炸（Vanishing / Exploding gradients）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/110-ti-du-xiao-5931-ti-du-bao-zha-ff08-vanishing-exploding-gradients.md)
- [1.11 神经网络的权重初始化（Weight Initialization for Deep Networks）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/111-shen-jing-wang-luo-de-quan-zhong-chu-shi-hua-ff08-weight-initialization-for-deep-networks.md)
- [1.12 梯度的数值逼近（Numerical approximation of gradients）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/112-ti-du-de-shu-zhi-bi-jin-ff08-numerical-approximation-of-gradients.md)
- [1.13 梯度检验（Gradient checking）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/113-ti-du-jian-yan-ff08-gradient-checking.md)
- [1.14 梯度检验应用的注意事项（Gradient Checking Implementation Notes）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/114-ti-du-jian-yan-ying-yong-de-zhu-yi-shi-xiang-ff08-gradient-checking-implementation-notes.md)
- [Initialization](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/dai-ma.md)
- [Gradient Checking](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/gradient-checking.md)
- [Regularization](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/regularization.md)
- [reg\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/regutils-py.md)
- [testCases.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/practical-aspects-of-deep-learning/testcasespy.md)
- [第二周：优化算法 (Optimization algorithms)](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/optimization-algorithms.md)
- [2.1 Mini-batch 梯度下降（Mini-batch gradient descent）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/optimization-algorithms/21-mini-batch-ti-du-xia-jiang-ff08-mini-batch-gradient-descent.md)
- [2.2 理解 mini-batch 梯度下降法（Understanding mini-batch gradient descent）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/optimization-algorithms/22-li-jie-mini-batch-ti-du-xia-jiang-fa-ff08-understanding-mini-batch-gradient-descent.md)
- [2.3 指数加权平均数（Exponentially weighted averages）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/optimization-algorithms/23-zhi-shu-jia-quan-ping-jun-shu-ff08-exponentially-weighted-averages.md)
- [2.4 理解指数加权平均数（Understanding exponentially weighted averages ）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/optimization-algorithms/24-li-jie-zhi-shu-jiaquanping-jun-shu-ff08-understandingexponentially-weighted-averages.md)
- [2.5 指 数 加 权 平 均 的 偏 差 修 正 （ Bias correction in exponentially weighted averages ）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/optimization-algorithms/25-zhi-shu-jia-quan-ping-jun-de-pian-cha-xiu-zheng-bias-correction-in-exponentially-weighted-average.md)
- [2.6 动量梯度下降法（Gradient descent with Momentum ）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/optimization-algorithms/26-dong-liang-tidu-xia-jiang-fa-ff08-gradient-descent-with-momentum.md)
- [2.7 RMSprop( root mean square prop)](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/optimization-algorithms/27-rmsprop.md)
- [2.8 Adam 优化算法(Adam optimization algorithm)](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/optimization-algorithms/28-adam-you-hua-suan-6cd528-adam-optimization-algorithm.md)
- [2.9 学习率衰减(Learning rate decay)](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/optimization-algorithms/29-xue-xi-lv-shuai-51cf28-learning-rate-decay.md)
- [2.10 局部最优的问题(The problem of local optima)](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/optimization-algorithms/210-ju-bu-zui-you-de-wen-989828-the-problem-of-local-optima.md)
- [Optimization](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/optimization-algorithms/optimization-methods.md)
- [opt\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/optimization-algorithms/optutils-py.md)
- [testCases.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/optimization-algorithms/testcasespy.md)
- [第 三 周 超 参 数 调 试 、 Batch 正 则 化 和 程 序 框 架 （Hyperparameter tuning）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/hyperparameter-tuning.md)
- [3.1 调试处理（Tuning process）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/hyperparameter-tuning/31-diao-shi-chu-li-ff08-tuning-process.md)
- [3.2 为超参数选择合适的范围（Using an appropriate scale to pick hyperparameters）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/hyperparameter-tuning/32-wei-chao-can-shu-xuan-ze-he-shi-de-fan-wei-ff08-using-an-appropriate-scale-to-pick-hyperparameter.md)
- [3.3 超参数训练的实践： Pandas VS Caviar（Hyperparameters tuning in practice: Pandas vs. Caviar）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/hyperparameter-tuning/33-chao-can-shu-xun-lian-de-shi-jian-ff1a-pandas-vs-caviar-hyperparameterstuning-in-practice-pandas.md)
- [3.4 归一化网络的激活函数（ Normalizing activations in a network）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/hyperparameter-tuning/34-gui-yi-hua-wang-luo-de-ji-huo-han-shu-ff08-normalizing-activations-in-a-network.md)
- [3.5 将 Batch Norm 拟合进神经网络（Fitting Batch Norm into a neural network）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/hyperparameter-tuning/35-jiang-batch-norm-ni-he-jin-shen-jing-wang-luo-ff08-fitting-batch-norm-into-a-neural-network.md)
- [3.6 Batch Norm 为什么奏效？（Why does Batch Norm work?）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/hyperparameter-tuning/36-batch-norm-wei-shi-yao-zou-xiao-ff1f-ff08-why-does-batch-norm-work.md)
- [3.7 测试时的 Batch Norm（Batch Norm at test time）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/hyperparameter-tuning/37-ce-shi-shi-de-batch-norm-batch-norm-at-test-time.md)
- [3.8 Softmax 回归（Softmax regression）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/hyperparameter-tuning/38-softmax-hui-gui-ff08-softmax-regression.md)
- [3.9 训练一个 Softmax 分类器（Training a Softmax classifier）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/hyperparameter-tuning/39-xun-lian-yi-ge-softmax-fen-lei-qi-ff08-training-a-softmax-classifier.md)
- [tensorflow tutorial](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/hyperparameter-tuning/tensorflow-tutorial.md)
- [improv\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/hyperparameter-tuning/improvutils-py.md)
- [tf\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-er-men-ke-gai-shan-shen-ceng-shen-jing-wang-luo-chao-can-shu-tiao-shi-zheng-ze-hua-yi-ji-you-hua/improving-deep-neural-networks/hyperparameter-tuning/tfutils-py.md)
- [第三门课 结构化机器学习项目（Structuring Machine Learning Projects）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects.md)
- [第一周 机器学习（ML）策略（1）（ML strategy（1））](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/di-yi-zhou-ml-strategy.md)
- [1.1 为什么是 ML 策略？（Why ML Strategy?）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/di-yi-zhou-ml-strategy/11-wei-shi-yao-shi-ml-ce-lveff1f-ff08-why-ml-strategy.md)
- [1.2 正交化（Orthogonalization）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/di-yi-zhou-ml-strategy/12-zheng-jiao-hua-ff08-orthogonalization.md)
- [1.3 单一数字评估指标（Single number evaluation metric）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/di-yi-zhou-ml-strategy/13-dan-yi-shu-zi-ping-gu-zhi-biao-ff08-single-number-evaluation-metric.md)
- [1.4 满足和优化指标（Satisficing and optimizing metrics）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/di-yi-zhou-ml-strategy/14-man-zu-he-you-hua-zhi-biao-ff08-satisficing-and-optimizing-metrics.md)
- [1.5 训练/开发/测试集划分（Train/dev/test distributions）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/di-yi-zhou-ml-strategy/15-xun-7ec3-kai-53d1-ce-shi-jihua-fen-ff08-train-dev-test-distributions.md)
- [1.6 开发集和测试集的大小（Size of dev and test sets）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/di-yi-zhou-ml-strategy/16-kai-fa-ji-he-ce-shi-ji-de-da-xiao-ff08-size-of-dev-and-test-sets.md)
- [1.7 什么时候该改变开发/测试集和指标？（When to change dev/test sets and metrics）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/di-yi-zhou-ml-strategy/17-shi-yao-shi-hou-gai-gai-bian-kai-53d1-ce-shi-ji-he-zhi-biao-ff1f-ff08-when-to-change-dev-test-set.md)
- [1.8 为什么是人的表现？（ Why human-level performance?）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/di-yi-zhou-ml-strategy/18-wei-shi-yao-shi-ren-de-biao-xian-ff1f-ff08-why-human-level-performance.md)
- [1.9 可避免偏差（Avoidable bias）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/di-yi-zhou-ml-strategy/19-ke-bi-mian-pian-chaff08-avoidable-bias.md)
- [1.10 理解人的表现（Understanding human-level performance）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/di-yi-zhou-ml-strategy/110-li-jie-ren-de-biao-xian-ff08-understanding-human-level-performance.md)
- [1.11 超过人的表现（Surpassing human- level performance）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/di-yi-zhou-ml-strategy/111-chao-guo-ren-de-biao-xian-ff08-surpassing-human-level-performance.md)
- [1.12 改善你的模型的表现（Improving your model performance）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/di-yi-zhou-ml-strategy/112-gai-shan-nide-mo-xing-de-biao-xian-ff08-improving-your-model-performance.md)
- [第二周：机器学习策略（2）(ML Strategy (2))](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/ml-strategy.md)
- [2.1 进行误差分析（Carrying out error analysis）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/ml-strategy/21-jin-xing-wu-cha-fen-xi-ff08-carrying-out-error-analysis.md)
- [2.2 清楚标注错误的数据（Cleaning up Incorrectly labeled data）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/ml-strategy/22-qing-chu-biao-zhu-cuo-wu-de-shu-ju-ff08-cleaning-up-incorrectly-labeled-data.md)
- [2.3 快速搭建你的第一个系统，并进行迭代（Build your first system quickly, then iterate）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/ml-strategy/23-kuai-su-da-jian-ni-de-di-yi-ge-xi-tong-ff0c-bing-jin-xing-die-dai-ff08-build-your-first-system-qu.md)
- [2.4 在不同的划分上进行训练并测试（Training and testing on different distributions）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/ml-strategy/24-zai-bu-tong-de-huafen-shang-jin-xing-xun-lian-bing-ce-shi-ff08-training-and-testing-on-different.md)
- [2.5 不匹配数据划分的偏差和方差（Bias and Variance with mismatched data distributions）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/ml-strategy/25-bu-pi-pei-shu-ju-huafen-de-pian-cha-he-fang-cha-ff08-bias-and-variance-with-mismatched-data-distr.md)
- [2.6 定位数据不匹配（Addressing data mismatch）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/ml-strategy/26-ding-wei-shu-ju-bu-pi-pei-ff08-addressing-data-mismatch.md)
- [2.7 迁移学习（Transfer learning）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/ml-strategy/27-qian-yi-xue-xi-ff08-transfer-learning.md)
- [2.8 多任务学习（Multi-task learning）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/ml-strategy/28-duo-ren-wu-xue-xi-ff08-multi-task-learning.md)
- [2.9 什么是端到端的深度学习？（What is end-to-end deep learning?）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/ml-strategy/29-shi-yao-shi-duan-dao-duan-de-shen-du-xue-xiff1f-ff08-what-is-end-to-end-deep-learning.md)
- [2.10 是否要使用端到端的深度学习？（Whether to use end-to-end learning?）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-san-men-ke-jie-gou-hua-ji-qi-xue-xi-xiang-mu-structuring-machine-learning-projects/di-san-men-ke-structuring-machine-learning-projects/ml-strategy/210-shi-fou-yao-shi-yong-duan-dao-duan-de-shen-du-xue-xi-ff1f-ff08-whether-to-use-end-to-end-learnin.md)
- [第四门课 卷积神经网络（Convolutional Neural Networks）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks.md)
- [第一周 卷积神经网络（Foundations of Convolutional Neural Networks）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/foundations-of-convolutional-neural-networks.md)
- [1.1 计算机视觉（Computer vision）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/foundations-of-convolutional-neural-networks/11-ji-suan-ji-shi-jue-ff08-computer-vision.md)
- [1.2 边缘检测示例（Edge detection example）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/foundations-of-convolutional-neural-networks/12-bian-yuan-jian-ce-shi-li-ff08-edge-detection-example.md)
- [1.3 更多边缘检测内容（More edge detection）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/foundations-of-convolutional-neural-networks/13-geng-duo-bian-yuan-jian-ce-nei-rong-ff08-more-edge-detection.md)
- [1.4 Padding](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/foundations-of-convolutional-neural-networks/14-padding.md)
- [1.5 卷积步长（Strided convolutions）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/foundations-of-convolutional-neural-networks/15-juan-ji-bu-chang-ff08-strided-convolutions.md)
- [1.6 三维卷积（Convolutions over volumes）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/foundations-of-convolutional-neural-networks/16-san-wei-juan-ji-ff08-convolutions-over-volumes.md)
- [1.7 单层卷积网络（One layer of a convolutional network）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/foundations-of-convolutional-neural-networks/17-dan-ceng-juan-ji-wang-luo-ff08-one-layer-of-a-convolutional-network.md)
- [1.8 简单卷积网络示例（A simple convolution network example）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/foundations-of-convolutional-neural-networks/18-jian-dan-juan-ji-wang-luo-shi-li-ff08-a-simple-convolution-network-example.md)
- [1.9 池化层（Pooling layers）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/foundations-of-convolutional-neural-networks/19-chi-hua-ceng-ff08-pooling-layers.md)
- [1.10 卷积神经网络示例（Convolutional neural network example）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/foundations-of-convolutional-neural-networks/110-juan-ji-shenjing-wang-luo-shi-li-ff08-convolutional-neural-network-example.md)
- [1.11 为什么使用卷积？（Why convolutions?）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/foundations-of-convolutional-neural-networks/111-wei-shi-yao-shi-yong-juan-ji-ff1f-ff08-why-convolutions.md)
- [Convolution model Step by Step](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/foundations-of-convolutional-neural-networks/convolution-model-step-by-step.md)
- [Convolutional Neural Networks: Application](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/foundations-of-convolutional-neural-networks/convolutional-neural-networks-application.md)
- [cnn\_utils](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/foundations-of-convolutional-neural-networks/cnnutils.md)
- [第二周 深度卷积网络：实例探究（Deep convolutional models: case studies）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies.md)
- [2.1 经典网络（Classic networks）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/22-jing-dian-wang-luoff08-classic-networks.md)
- [2.2 残差网络（Residual Networks (ResNets)）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/23-can-cha-wang-luo-ff08-residual-networks-resnets.md)
- [2.3 残差网络为什么有用？（Why ResNets work?）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/24-can-cha-wang-luo-wei-shi-yao-you-yong-ff1f-ff08-why-resnets-work.md)
- [2.4 网络中的网络以及 1×1 卷积（Network in Network and 1×1 convolutions）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/25-wang-luo-zhong-de-wang-luo-yi-ji-1-1-juan-ji-ff08-network-in-network-and-1-1-convolutions.md)
- [2.5 谷歌 Inception 网络简介（Inception network motivation）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/26-gu-ge-inception-wang-luo-jianjie-ff08-inception-network-motivation.md)
- [2.6 Inception 网络（Inception network）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/27-inception-wang-luo-ff08-inception-network.md)
- [2.7 迁移学习（Transfer Learning）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/29-qian-yi-xue-xi-ff08-transfer-learning.md)
- [2.8 数据扩充（Data augmentation）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/210-shu-ju-kuo-chong-ff08-data-augmentation.md)
- [2.9 计算机视觉现状（The state of computer vision）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/211-ji-suan-ji-shi-jue-xian-zhuang-ff08-the-state-of-computer-vision.md)
- [Residual Networks](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/residual-networks.md)
- [Keras tutorial - the Happy House](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/keras-tutorial-happy-house-v2.md)
- [kt\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/deep-convolutional-models-case-studies/ktutils-py.md)
- [第三周 目标检测（Object detection）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/object-detection.md)
- [3.1 目标定位（Object localization）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/object-detection/31-mu-biao-ding-wei-ff08-object-localization.md)
- [3.2 特征点检测（Landmark detection）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/object-detection/32-te-zheng-dian-jian-ce-ff08-landmark-detection.md)
- [3.3 目标检测（Object detection）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/object-detection/33-mu-biao-jian-ce-ff08-object-detection.md)
- [3.4 卷积的滑动窗口实现（Convolutional implementation of sliding windows）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/object-detection/34-juan-ji-de-hua-dong-chuang-kou-shi-xian-ff08-convolutional-implementation-of-sliding-windows.md)
- [3.5 Bounding Box预测（Bounding box predictions）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/object-detection/35-bounding-boxyu-ce-ff08-bounding-box-predictions.md)
- [3.6 交并比（Intersection over union）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/object-detection/36-jiao-bing-bi-ff08-intersection-over-union.md)
- [3.7 非极大值抑制（Non-max suppression）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/object-detection/37-fei-ji-da-zhi-yi-zhi-ff08-non-max-suppression.md)
- [3.8 Anchor Boxes](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/object-detection/38-anchor-boxes.md)
- [3.9 YOLO 算法（Putting it together: YOLO algorithm）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/object-detection/39-yolo-suan-fa-ff08-putting-it-together-yolo-algorithm.md)
- [3.10 候选区域（选修）（Region proposals (Optional)）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/object-detection/310-hou-xuan-qu-yu-ff08-xuan-xiu-ff09-ff08-region-proposals-optional.md)
- [Autonomous driving application - Car detection](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/object-detection/autonomous-driving-application-car-detection-v3.md)
- [yolo\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/object-detection/yoloutils-py.md)
- [第四周 特殊应用：人脸识别和神经风格转换（Special applications: Face recognition \&Neural style transfer）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/special-applications.md)
- [4.1 什么是人脸识别？（What is face recognition?）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/special-applications/41-shi-yao-shi-ren-lian-shi-bie-ff1f-ff08-what-is-face-recognition.md)
- [4.2 One-Shot学习（One-shot learning）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/special-applications/42-one-shotxue-xi-ff08-one-shot-learning.md)
- [4.3 Siamese 网络（Siamese network）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/special-applications/43-siamese-wang-luo-ff08-siamese-network.md)
- [4.4 Triplet 损失（Triplet 损失）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/special-applications/44-triplet-sun-shi-ff08-triplet-sun-shi-ff09.md)
- [4.5 面部验证与二分类（Face verification and binary classification）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/special-applications/45-mian-bu-yan-zheng-yu-er-fen-lei-ff08-face-verification-and-binary-classification.md)
- [4.6 什么是深度卷积网络？（What are deep ConvNets learning?）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/special-applications/47-shi-yao-shi-shen-du-juan-ji-wang-luo-ff1f-ff08-what-are-deep-convnets-learning.md)
- [4.7 代价函数（Cost function）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/special-applications/48-dai-jia-han-shu-ff08-cost-function.md)
- [4.8 内容代价函数（Content cost function）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/special-applications/49-nei-rong-dai-jia-han-shu-ff08-content-cost-function.md)
- [4.9 风格代价函数（Style cost function）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/special-applications/410-feng-ge-dai-jia-han-shu-ff08-style-cost-function.md)
- [4.10 一维到三维推广（1D and 3D generalizations of models）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/special-applications/411-yi-wei-dao-san-wei-tui-guang-ff08-1d-and3d-generalizations-of-models.md)
- [Art Generation with Neural Style Transfer](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/special-applications/art-generation-with-neural-style-transfer-v2.md)
- [nst\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/special-applications/nstutils-py.md)
- [Face Recognition for the Happy House](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/special-applications/face+recognition+for+the+happy+house+-+v3.md)
- [fr\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/special-applications/frutils-py.md)
- [inception\_blocks.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-si-men-ke-juan-ji-shen-jing-wang-luo-convolutional-neural-networks/convolutional-neural-networks/special-applications/inceptionblocks-py.md)
- [第五门课 序列模型(Sequence Models)](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models.md)
- [第一周 循环序列模型（Recurrent Neural Networks）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks.md)
- [1.1 为什么选择序列模型？（Why Sequence Models?）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/11-wei-shi-yao-xuan-ze-xu-lie-mo-xing-ff1f-ff08-why-sequence-models.md)
- [1.2 数学符号（Notation）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/12-shu-xue-fu-hao-ff08-notation.md)
- [1.3 循环神经网络模型（Recurrent Neural Network Model）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/13-xun-huan-shen-jing-wang-luo-mo-xing-ff08-recurrent-neural-network-model.md)
- [1.4 通过时间的反向传播（Backpropagation through time）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/14-tong-guo-shi-jian-de-fan-xiang-chuan-bo-ff08-backpropagation-through-time.md)
- [1.5 不同类型的循环神经网络（Different types of RNNs）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/15-bu-tong-lei-xing-dexun-huan-shen-jing-wang-luo-ff08-different-types-of-rnns.md)
- [1.6 语言模型和序列生成（Language model and sequence generation）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/16-yu-yan-mo-xinghe-xu-liesheng-cheng-ff08-language-model-and-sequence-generation.md)
- [1.7 对新序列采样（Sampling novel sequences）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/17-dui-xin-xu-lie-cai-yang-ff08-sampling-novel-sequences.md)
- [1.8 循环神经网络的梯度消失（Vanishing gradients with RNNs）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/18-xun-huan-shen-jing-wangluo-de-ti-du-xiao-shi-ff08-vanishing-gradients-with-rnns.md)
- [1.9 GRU单元（Gated Recurrent Unit（GRU））](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/19-grudan-yuan-ff08-gated-recurrent-unit-gru.md)
- [1.10 长短期记忆（LSTM（long short term memory）unit）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/110-chang-duan-qi-ji-yi-ff08-lstm-long-short-term-memory-unit.md)
- [1.11 双向循环神经网络（Bidirectional RNN）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/111-shuang-xiang-xun-huan-shenjing-wang-luo-ff08-bidirectional-rnn.md)
- [1.12 深层循环神经网络（Deep RNNs）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/112-shen-ceng-xun-huan-shen-jing-wang-luo-ff08-deep-rnns.md)
- [Building your Recurrent Neural Network](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/building+a+recurrent+neural+network+-+step+by+step+-+v3.md)
- [rnn\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/rnnutils-py.md)
- [Dinosaurus Island -- Character level language model final](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/dinosaurus+island+-+character+level+language+model+final+-+v3.md)
- [utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/utilspy.md)
- [shakespeare\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/shakespeareutils-py.md)
- [Improvise a Jazz Solo with an LSTM Network](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/recurrent-neural-networks/improvise+a+jazz+solo+with+an+lstm+network+-+v3.md)
- [第二周 自然语言处理与词嵌入（Natural Language Processing and Word Embeddings）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings.md)
- [2.1 词汇表征（Word Representation）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/21-ci-hui-biao-zheng-ff08-word-representation.md)
- [2.2 使用词嵌入（Using Word Embeddings）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/22-shi-yong-ci-qian-ru-ff08-using-word-embeddings.md)
- [2.3 词嵌入的特性（Properties of Word Embeddings）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/23-ci-qian-ru-de-texing-ff08-propertiesof-word-embeddings.md)
- [2.4 嵌入矩阵（Embedding Matrix）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/24-qian-ru-ju-zhen-ff08-embedding-matrix.md)
- [2.5 学习词嵌入（Learning Word Embeddings）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/25-xue-xi-ci-qian-ru-ff08-learning-word-embeddings.md)
- [2.6 Word2Vec](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/26-word2vec.md)
- [2.7 负采样（Negative Sampling）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/27-fu-cai-yang-ff08-negative-sampling.md)
- [2.8 GloVe 词向量（GloVe Word Vectors）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/28-glove-ci-xiang-liang-ff08-glove-word-vectors.md)
- [2.9 情感分类（Sentiment Classification）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/29-qing-ganfen-lei-ff08-sentiment-classification.md)
- [2.10 词嵌入除偏（Debiasing Word Embeddings）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/210-ci-qian-ru-chu-pian-ff08-debiasing-word-embeddings.md)
- [Operations on word vectors](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/operations+on+word+vectors+-+v2.md)
- [w2v\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/w2vutils-py.md)
- [Emojify](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/emojify-v2.md)
- [emo\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/natural-language-processing-and-word-embeddings/emoutils-py.md)
- [第三周 序列模型和注意力机制（Sequence models & Attention mechanism）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism.md)
- [3.1 基础模型（Basic Models）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/31-ji-chu-mo-xing-ff08-basic-models.md)
- [3.2 选择最可能的句子（Picking the most likely sentence）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/32-xuan-ze-zuike-neng-de-ju-zi-ff08-picking-the-most-likely-sentence.md)
- [3.3 集束搜索（Beam Search）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/33-ji-shu-sou-suo-ff08-beam-search.md)
- [3.4 改进集束搜索（Refinements to Beam Search）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/34-gai-jin-ji-shu-sou-suo-ff08-refinements-to-beam-search.md)
- [3.5 集束搜索的误差分析（Error analysis in beam search）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/35-ji-shu-sou-suode-wu-cha-fen-xi-ff08-error-analysis-in-beam-search.md)
- [3.6 Bleu 得分（选修）（Bleu Score (optional)）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/36-bleu-de-fen-ff08-xuan-xiu-ff09-ff08-bleu-score-optional.md)
- [3.7 注意力模型直观理解（Attention Model Intuition）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/37-zhu-yi-li-mo-xing-zhi-guan-li-jie-ff08-attention-model-intuition.md)
- [3.8注意力模型（Attention Model）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/38zhu-yi-li-mo-xing-ff08-attention-model.md)
- [3.9语音识别（Speech recognition）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/39yu-yin-shi-bieff08-speech-recognition.md)
- [3.10触发字检测（Trigger Word Detection）](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/310hong-fa-zi-jian-ce-ff08-trigger-word-detection.md)
- [Neural machine translation with attention](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/neural-machine-translation-with-attention-v4.md)
- [nmt\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/nmtutils-py.md)
- [Trigger word detection](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/trigger-word-detection-v1.md)
- [td\_utils.py](https://baozoulin.gitbook.io/neural-networks-and-deep-learning/di-wu-men-ke-xu-lie-mo-xing-sequence-models/di-wu-men-kexulie-mo-578b28-sequence-models/di-san-zhou-xu-lie-mo-xing-he-zhu-yi-li-ji-zhi-ff08-sequence-models-and-attention-mechanism/tdutils-py.md)