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

Product type Paperback

Published in Oct 2018

Publisher Packt

ISBN-13 9781788991063

Length 362 pages

Edition 1st Edition

Languages

Python

Tools

Assemble

Concepts

Artificial Intelligence

Authors (2):

David Dindi

Patrick D. Smith

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Table of Contents (15) Chapters

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

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Technical requirements

While this chapter will contain some materials that are typically part of the job of a DevOps engineer, we'll touch on these tools and topics on a need-to-know basis, and refer to other resources and tools that can help you learn about the topics in more depth. In this chapter, we'll be utilizing the following:

TensorFlow
PyTorch
Docker, a containerization service for deploying our models
Amazon Web Services or Google Cloud Platform as a cloud provider
Introductory knowledge of Kubernetes

The rest of the chapter is locked

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Authors (2)

Patrick D. Smith

Patrick D. Smith is the Data Science Lead for Excella in Arlington, Virginia, where he founded the data science and machine learning team. Prior to Excella, Patrick was the lead instructor for the data science program at General Assembly in Washington, DC, as well as a data scientist with Booz Allen Hamilton's Strategic Innovations Group. He holds a bachelor's degree from The George Washington University in International Economics, and is currently a part-time masters student in software engineering at Harvard University.

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Dindi

David Dindi received a M.Sc. and a B.Sc. in chemical engineering with a focus on artificial intelligence from Stanford University. While at Stanford, David developed deep learning frameworks for predicting patient-specific adverse reactions to drugs at the Stanford Center for Biomedical Informatics. He currently advises a number of early stage start-ups in Silicon Valley and in New York.

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