hdhuangzhihao-优快云博客

原创 Convolutional Neural Networks: Application 吴恩达第四课第一周application

Convolutional Neural Networks: ApplicationWelcome to Course 4's second assignment! In this notebook, you will:Implement helper functions that you will use when implementing a TensorFlow modelImp

2018-01-23 15:49:33 1449

原创 tensorflow+ tutorial 吴恩达第二课第三周作业

TensorFlow TutorialWelcome to this week's programming assignment. Until now, you've always used numpy to build neural networks. Now we will step you through a deep learning framework that will allow

2018-01-13 15:54:13 2621 1

原创吴恩达人工智能课程第二课第二周Optimization Methods

Optimization MethodsUntil 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 speed

2018-01-05 20:16:32 609

原创吴恩达第二课第一周 1 initialization

InitializationWelcome 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 met

2017-12-19 18:37:27 590

原创吴恩达第二课第一周 2 Regularization

RegularizationWelcome 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

2017-12-19 18:30:04 1371

原创吴恩达第二课第一周 3 Gradient Checking

Gradient CheckingWelcome 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 avai

2017-12-19 18:29:02 1341

原创 Deep Neural Network for Image Classification: Application吴恩达老师第一课第四周作业2

Deep Neural Network for Image Classification: ApplicationWhen you finish this, you will have finished the last programming assignment of Week 4, and also the last programming assignment of this cour

2017-12-07 16:40:15 641

原创 Building your Deep Neural Network: Step by Step 吴恩达老师第一课第四周作业

Building your Deep Neural Network: Step by StepWelcome to your week 4 assignment (part 1 of 2)! You have previously trained a 2-layer Neural Network (with a single hidden layer). This week, you will

2017-12-07 15:12:14 507