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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 speed up learning and perhaps even get you to a better final value for the cost function. Having a good optimization algorithm can be the difference between waiting days vs. just a few hours to get a good result. Gradient descent goes “downhill” on a cost function JJJ. Think of it as trying to do this: ...

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 are asked to build a deep learning model to detect fraud–whenever someone makes a payment, you want to see if the payment might be fraudulent, such as if the user’s account has been taken over by a hacker. But backpropagation is quite challenging to implement, and sometimes has bug ...

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 does well on the training set, but the learned network doesn’t generalize to new examples that it has never seen! You will learn to: Use regularization in your deep learning models. Let’s first import the packages you are going to use. 1234567891011121314# import packagesimport numpy ...

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 learning. If you completed the previous course of this specialization, you probably followed our instructions for weight initialization, and it has worked out so far. But how do you choose the initialization for a new neural network? In this notebook, you will see how different initializ ...

Deep Neural Network for Image Classification: Application When you finish this, you will have finished the last programming assignment of Week 4, and also the last programming assignment of this course! You will use use the functions you’d implemented in the previous assignment to build a deep network, and apply it to cat vs non-cat classification. Hopefully, you will see an improvement in accuracy relative to your previous logistic regression implementation. After this assignment you will be ab ...

Building your Deep Neural Network: Step by Step Welcome 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 build a deep neural network, with as many layers as you want! In this notebook, you will implement all the functions required to build a deep neural network. In the next assignment, you will use these functions to build a deep neural network for image classification. After this assignment you will ...

Planar data classification with one hidden layer Welcome to your week 3 programming assignment. It’s time to build your first neural network, which will have a hidden layer. You will see a big difference between this model and the one you implemented using logistic regression. You will learn how to: Implement a 2-class classification neural network with a single hidden layer Use units with a non-linear activation function, such as tanh Compute the cross entropy loss Implement forward and backwa ...

Logistic Regression with a Neural Network mindset Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize cats. This assignment will step you through how to do this with a Neural Network mindset, and so will also hone your intuitions about deep learning. Instructions: Do not use loops (for/while) in your code, unless the instructions explicitly ask you to do so. You will learn to: Build the general architecture of a learning algor ...