# 第 三 周 超 参 数 调 试 、 Batch 正 则 化 和 程 序 框 架 （Hyperparameter tuning）

- [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)


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