# 第一周：深度学习的实用层面(Practical aspects of Deep Learning)

- [1.1 训练，验证，测试集（Train / Dev / Test sets）](/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）](/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）](/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）](/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?）](/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）](/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）](/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）](/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）](/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）](/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）](/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）](/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）](/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）](/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](/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](/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](/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](/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](/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)