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Deep Learning for Beginners

You're reading from   Deep Learning for Beginners A beginner's guide to getting up and running with deep learning from scratch using Python

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Product type Paperback
Published in Sep 2020
Publisher Packt
ISBN-13 9781838640859
Length 432 pages
Edition 1st Edition
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Authors (2):
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Pablo Rivas Pablo Rivas
Author Profile Icon Pablo Rivas
Pablo Rivas
Dr. Pablo Rivas Dr. Pablo Rivas
Author Profile Icon Dr. Pablo Rivas
Dr. Pablo Rivas
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Getting Up to Speed
2. Introduction to Machine Learning FREE CHAPTER 3. Setup and Introduction to Deep Learning Frameworks 4. Preparing Data 5. Learning from Data 6. Training a Single Neuron 7. Training Multiple Layers of Neurons 8. Section 2: Unsupervised Deep Learning
9. Autoencoders 10. Deep Autoencoders 11. Variational Autoencoders 12. Restricted Boltzmann Machines 13. Section 3: Supervised Deep Learning
14. Deep and Wide Neural Networks 15. Convolutional Neural Networks 16. Recurrent Neural Networks 17. Generative Adversarial Networks 18. Final Remarks on the Future of Deep Learning 19. Other Books You May Enjoy

Applications in dimensionality reduction and visualization

Among some of the most interesting applications of autoencoders is dimensionality reduction [Wang, Y., et al. (2016)]. Given that we live in a time where data storage is easily accessible and affordable, large amounts of data are currently stored everywhere. However, not everything is relevant information. Consider, for example, a database of video recordings of a home security camera that always faces one direction. Chances are that there is a lot of repeated data in every video frame or image and very little of the data gathered will be useful. We would need a strategy to look at what is really important in those images. Images, by their nature, have a lot of redundant information, and there is usually correlation among image regions, which makes autoencoders very useful in compressing the information in images (Petscharnig, S., et al. (2017)).

To demonstrate the applicability of autoencoders in dimensionality reduction for...

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