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Hands-On Artificial Intelligence for Beginners

You're reading from   Hands-On Artificial Intelligence for Beginners An introduction to AI concepts, algorithms, and their implementation

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781788991063
Length 362 pages
Edition 1st Edition
Languages
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Authors (2):
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David Dindi David Dindi
Author Profile Icon David Dindi
David Dindi
Patrick D. Smith Patrick D. Smith
Author Profile Icon Patrick D. Smith
Patrick D. Smith
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Table of Contents (15) Chapters Close

Preface 1. The History of AI 2. Machine Learning Basics FREE CHAPTER 3. Platforms and Other Essentials 4. Your First Artificial Neural Networks 5. Convolutional Neural Networks 6. Recurrent Neural Networks 7. Generative Models 8. Reinforcement Learning 9. Deep Learning for Intelligent Agents 10. Deep Learning for Game Playing 11. Deep Learning for Finance 12. Deep Learning for Robotics 13. Deploying and Maintaining AI Applications 14. Other Books You May Enjoy

The training process

Training is the process by which we teach a neural network to learn, and it's controlled programmatically in our code. Recall, from Chapter 2, Machine Learning Basics, that there are two forms of learning in the AI world: supervised learning and unsupervised learning. In general, most ANNs are supervised learners, and they learn by example from a training set. A singular unit in a training cycle in a neural network is called an epoch. By the end of one epoch, your network has been exposed to every data point in the dataset once. At the end of one epoch, epochs are defined as a hyperparameter when first setting up your network. Each of these epochs contain two processes: forward propagation and backpropagation.

Within an epoch we have iterations, which tell us what proportion of the data has gone through both forward propagation and backpropagation. For...

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