Coursera Deep Learning 2 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - week3, Hyperparameter tuning, Batch Normalization and Programming Frameworks

【Coursera Deep Learning 2 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - week3, Hyperparameter tuning, Batch Normalization and Programming Frameworks】的更多相关文章

Coursera Deep Learning 2 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - week3, Hyperparameter tuning, Batch Normalization and Programming Frameworks

Tuning process 下图中的需要tune的parameter的先后顺序, 红色>黄色>紫色,其他基本不会tune. 先讲到怎么选hyperparameter, 需要随机选取(sampling at random) 随机选取的过程中,可以采用从粗到细的方法逐步确定参数有些参数可以按照线性随机选取, 比如 n[l] 但是有些参数就不适合线性的sampling at radom, 比如 learning rate α,这时可以用 log Andrew 很幽默的讲到了两种选参数的实际场景…

[C2W3] Improving Deep Neural Networks : Hyperparameter tuning, Batch Normalization and Programming Frameworks

第三周:Hyperparameter tuning, Batch Normalization and Programming Frameworks 调试处理(Tuning process) 目前为止,你已经了解到,神经网络的改变会涉及到许多不同超参数的设置.现在,对于超参数而言,你要如何找到一套好的设定呢?在本节中,我想和你分享一些指导原则,一些关于如何系统地组织超参调试过程的技巧,希望这些能够让你更有效的聚焦到合适的超参设定中. 关于训练深度神经网络最难的事情之一是你要处理的参数的数量,下面粗…

Coursera Deep Learning 2 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - week1, Assignment(Initialization)

声明:所有内容来自coursera,作为个人学习笔记记录在这里. Initialization Welcome to the first assignment of "Improving Deep Neural Networks". Training your neural network requires specifying an initial value of the weights. A well chosen initialization method will help…

Coursera Deep Learning 2 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - week1, Assignment(Gradient Checking)

声明:所有内容来自coursera,作为个人学习笔记记录在这里. Gradient Checking Welcome to the final assignment for this week! In this assignment you will learn to implement and use gradient checking. You are part of a team working to make mobile payments available globally, and…

Coursera Deep Learning 2 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - week1, Assignment(Regularization)

声明:所有内容来自coursera,作为个人学习笔记记录在这里. Regularization Welcome to the second assignment of this week. Deep Learning models have so much flexibility and capacity that overfitting can be a serious problem, if the training dataset is not big enough. Sure it do…

Coursera Deep Learning 2 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - week2, Assignment(Optimization Methods)

声明:所有内容来自coursera,作为个人学习笔记记录在这里. 请不要ctrl+c/ctrl+v作业. Optimization Methods Until now, you've always used Gradient Descent to update the parameters and minimize the cost. In this notebook, you will learn more advanced optimization methods that can spee…

课程二(Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization)，第三周（Hyperparameter tuning, Batch Normalization and Programming Frameworks） —— 2.Programming assignments

Tensorflow Welcome to the Tensorflow Tutorial! In this notebook you will learn all the basics of Tensorflow. You will implement useful functions and draw the parallel with what you did using Numpy. You will understand what Tensors and operations are,…

吴恩达《深度学习》-课后测验-第一门课 (Neural Networks and Deep Learning)-Week 3 - Shallow Neural Networks（第三周测验 - 浅层神经网络）

Week 3 Quiz - Shallow Neural Networks(第三周测验 - 浅层神经网络) \1. Which of the following are true? (Check all that apply.) Notice that I only list correct options(以下哪一项是正确的?只列出了正确的答案) [ ]…

[CS231n-CNN] Training Neural Networks Part 1 : activation functions, weight initialization, gradient flow, batch normalization | babysitting the learning process, hyperparameter optimization

课程主页:http://cs231n.stanford.edu/ Introduction to neural networks -Training Neural Network ______________________________________________________________________________________________________________________________________________________________…

Coursera, Deep Learning 2, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - week1, Course

Train/Dev/Test set Bias/Variance Regularization 有下面一些regularization的方法. L2 regularation drop out data augmentation(翻转图片得到一个新的example), early stopping(画出J_train 和J_dev 对应于iteration的图像) L2 regularization: Forbenius Norm. 上面这张图提到了weight decay 的概念 Weigh…