# 第一门课 神经网络和深度学习(Neural-Networks-and-Deep-Learning)

- [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)


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