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Applied Deep Learning with Keras

You're reading from   Applied Deep Learning with Keras Solve complex real-life problems with the simplicity of Keras

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Product type Paperback
Published in Apr 2019
Publisher
ISBN-13 9781838555078
Length 412 pages
Edition 1st Edition
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Authors (3):
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Matthew Moocarme Matthew Moocarme
Author Profile Icon Matthew Moocarme
Matthew Moocarme
Mahla Abdolahnejad Mahla Abdolahnejad
Author Profile Icon Mahla Abdolahnejad
Mahla Abdolahnejad
Ritesh Bhagwat Ritesh Bhagwat
Author Profile Icon Ritesh Bhagwat
Ritesh Bhagwat
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Building Your First Neural Network


In this section, you will first learn about the representations and concepts of deep learning such as forward propagation, backpropagation, and gradient descent. We will not delve deeply into these concepts, as it isn't required for this book. However, the coverage will essentially help anyone who wants to apply deep learning to a problem.

We then will move on to implementing neural networks using Keras. Also, we will stick to the simplest case, which is a neural network with a single hidden layer. You will learn how to define a model in Keras, choose the hyperparameters, and then train your model. At the end of this section, you will have the opportunity to practice what you have learned by implementing a neural network in Keras to perform classification on a dataset and observe how neural networks outperform simpler models such as logistic regression.

Logistic Regression to a Deep Neural Network

You learned in the previous chapter about the logistic regression...

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