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Deep Learning for Beginners

You're reading from  Deep Learning for Beginners

Product type Book
Published in Sep 2020
Publisher Packt
ISBN-13 9781838640859
Pages 432 pages
Edition 1st Edition
Languages
Author (1):
Dr. Pablo Rivas Dr. Pablo Rivas
Profile icon Dr. Pablo Rivas

Table of Contents (20) Chapters

Preface Section 1: Getting Up to Speed
Introduction to Machine Learning Setup and Introduction to Deep Learning Frameworks Preparing Data Learning from Data Training a Single Neuron Training Multiple Layers of Neurons Section 2: Unsupervised Deep Learning
Autoencoders Deep Autoencoders Variational Autoencoders Restricted Boltzmann Machines Section 3: Supervised Deep Learning
Deep and Wide Neural Networks Convolutional Neural Networks Recurrent Neural Networks Generative Adversarial Networks Final Remarks on the Future of Deep Learning Other Books You May Enjoy

Summary

In this chapter, we discussed many data manipulation techniques that we will come back to use all the time. It is good for you to spend time doing this now rather than later. It will make our modeling of deep learning architectures easier.

After reading this chapter, you are now able to manipulate and produce binary data for classification or for feature representation. You also know how to deal with categorical data and labels and prepare it for classification or regression. When you have real-valued data, you now know how to identify statistical properties and how to normalize such data. If you ever have the problem of data that has non-normal or non-uniform distributions, now you know how to fix that. And if you ever encounter problems of not having enough data, you learned a few data augmentation techniques. Toward the end of this chapter, you learned some of the most popular dimensionality reduction techniques. You will learn more of these along the road, for example, when...

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