Optimization methodsOptimization MethodsUntil now, you've always used Gradient Descent to update the parameters and minimize the cost. In this notebook, you will learn more advanced optimi

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

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

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

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

先上一张summary map1. L层神经网络参数初始化: 返回各层参数W(1)…W(l-1)def initialize_parameters_deep(layer_dims):   forl inrange(1,L):        parameters['W'+str(l)]=np.random.randn(layer_dims[l],layer_dims[

该实验主要是实现一些“Helper function”,为下一步实现两层神经网络和L层神经网络做准备，实现一个两层网络或深层网络的步骤如下：Step 1.分别初始化一个两层神经网络和L层神经网络的参数Step 2: 前向传播的实现：1.完成一个网络的前向传播的线性部分（linear part）,即计算出 Z [l]     2.实现relu和 sigmoid激活函数

Convolutional Neural Networks: Step by StepWelcome to Course 4's first assignment! In this assignment, you will implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forw