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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
Preparing Data

Now that you have successfully prepared your system to learn about deep learning, see Chapter 2, Setup and Introduction to Deep Learning Frameworks, we will proceed to give you important guidelines about data that you may encounter frequently when practicing deep learning. When it comes to learning about deep learning, having well-prepared datasets will help you to focus more on designing your models rather than preparing your data. However, everyone knows that this is not a realistic expectation and if you ask any data scientist or machine learning professional about this, they will tell you that an important aspect of modeling is knowing how to prepare your data. Knowing how to deal with your data and how to prepare it will save you many hours of work that you can spend fine-tuning your models. Any time spent preparing your data is time well invested indeed.

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