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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
Setup and Introduction to Deep Learning Frameworks

At this point, you are now familiar with machine learning (ML) and deep learning (DL) - this is great! You should feel ready to begin making the preparations for writing and running your own programs. This chapter helps you in the process of setting up TensorFlow and Keras, and introduces their usefulness and purpose in deep learning. Dopamine is presented as the new reinforcement learning framework that we will use later on. This chapter also briefly introduces other deep learning libraries that are important to know.

The topics that will be covered in this chapter are as follows:

  • Introduction to Colaboratory
  • Introduction and setup of TensorFlow
  • Introduction and setup of Keras
  • Introduction to PyTorch
  • Introduction to Dopamine
  • Other deep learning libraries
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