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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

Introduction and setup of Keras

If you search on the internet for sample TensorFlow code, you will find that it may not be super easy to understand or follow. You can find tutorials for beginners but, in reality, things can get complicated very easily and editing someone else's code can be very difficult. Keras comes as an API solution to develop deep learning Tensorflow model prototypes with relative ease. In fact, Keras supports running not only on top of TensorFlow, but also over CNTK and Theano.

We can think of Keras as an abstraction to actual TensorFlow models and methods. This symbiotic relationship has become so popular that TensorFlow now unofficially encourages its use for those who are beginning to use TensorFlow. Keras is very user friendly, it is easy to follow in Python, and it is easy to learn in a general sense.

Setup

To set up Keras on your Colab, do the following:

  1. Run the following command:
!pip install keras
  1. The system will proceed to install the necessary libraries...
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