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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
Learning from Data

Data preparation takes a great deal of time for complex datasets, as we saw in the previous chapter. However, time spent on data preparation is time well invested... this I can guarantee! In the same way, investing time in understanding the basic theory of learning from data is super important for any person that wants to join the field of deep learning. Understanding the fundamentals of learning theory will pay off whenever you read new algorithms or evaluate your own models. It will also make your life much easier when you get to the later chapters in this book.

More specifically, this chapter introduces the most elementary concepts around the theory of deep learning, including measuring performance on regression and classification as well as the identification of overfitting. It also offers some warnings about the sensibility of—and the need to optimize...

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