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The Deep Learning with Keras Workshop

You're reading from   The Deep Learning with Keras Workshop Learn how to define and train neural network models with just a few lines of code

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781800562967
Length 496 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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Table of Contents (11) Chapters Close

Preface
1. Introduction to Machine Learning with Keras 2. Machine Learning versus Deep Learning FREE CHAPTER 3. Deep Learning with Keras 4. Evaluating Your Model with Cross-Validation Using Keras Wrappers 5. Improving Model Accuracy 6. Model Evaluation 7. Computer Vision with Convolutional Neural Networks 8. Transfer Learning and Pre-Trained Models 9. Sequential Modeling with Recurrent Neural Networks Appendix

Introduction

In the previous chapter, you learned about the mathematics of neural networks, including linear transformations with scalars, vectors, matrices, and tensors. Then, you implemented your first neural network using Keras by building a logistic regression model to classify users of a website into those who will purchase from the website and those who will not.

In this chapter, you will extend your knowledge of building neural networks using Keras. This chapter covers the basics of deep learning and will provide you with the necessary foundations so that you can build highly complex neural network architectures. We will start by extending the logistic regression model to a simple single-layer neural network and then proceed to more complicated neural networks with multiple hidden layers.

In this process, you will learn about the underlying basic concepts of neural networks, including forward propagation for making predictions, computing loss, backpropagation for computing...

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