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Improving Deep Neural Networks 笔记

1 Practical aspects of Deep Learning 1.1 Train/Dev/Test sets 在小样本的机器学习中,可以分为60/20/20. 在大数据训练中,不需要划分很多的开发集和测试集.假如共有一百万数据,可以只取其中1万条作为开发集,1万条作为测试集.剩下的作为训练集. 某些时候会没有开发集.但是这么叫不确切,应该成为没有测试机. 注意:这里的train/dev/test应该是同一个数据集里.例如图片什么的需要相同的分辨率. 1.2 bias/variance…

《Improving Deep Neural Networks:Hyperparameter tuning, Regularization and Optimization》课堂笔记

Lesson 2 Improving Deep Neural Networks:Hyperparameter tuning, Regularization and Optimization 这篇文章其实是 Coursera 上吴恩达老师的深度学习专业课程的第二门课程的课程笔记. 参考了其他人的笔记继续归纳的. 训练,验证,测试集 (Train / Dev / Test sets) 在机器学习发展的小数据量时代,常见做法是将所有数据三七分,就是人们常说的 70% 训练集,30% 测试集.如果明确设…

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…

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About this Course This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good res…

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…

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, 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…

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,…

课程二(Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization)，第二周（Optimization algorithms） —— 2.Programming assignments:Optimization

Optimization Welcome to the optimization's programming assignment of the hyper-parameters tuning specialization. There are many different optimization algorithms you could be using to get you to the minimal cost. Similarly, there are many different p…

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Gradient Checking Welcome to this week's third programming assignment! You will be implementing gradient checking to make sure that your backpropagation implementation is correct. By completing this assignment you will: - Implement gradient checking…