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Artificial Intelligence By Example

You're reading from   Artificial Intelligence By Example Develop machine intelligence from scratch using real artificial intelligence use cases

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788990547
Length 490 pages
Edition 1st Edition
Languages
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Table of Contents (19) Chapters Close

Preface 1. Become an Adaptive Thinker 2. Think like a Machine FREE CHAPTER 3. Apply Machine Thinking to a Human Problem 4. Become an Unconventional Innovator 5. Manage the Power of Machine Learning and Deep Learning 6. Don't Get Lost in Techniques – Focus on Optimizing Your Solutions 7. When and How to Use Artificial Intelligence 8. Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies 9. Getting Your Neurons to Work 10. Applying Biomimicking to Artificial Intelligence 11. Conceptual Representation Learning 12. Automated Planning and Scheduling 13. AI and the Internet of Things (IoT) 14. Optimizing Blockchains with AI 15. Cognitive NLP Chatbots 16. Improve the Emotional Intelligence Deficiencies of Chatbots 17. Quantum Computers That Think 18. Answers to the Questions

Chapter 10 – Applying Biomimicking to AI

1. Deep learning and machine learning mean the same thing. (Yes | No)

No. When an AI program contains a network, especially a deep one (with several layers), that is deep learning. Deep learning is a subset of machine learning.

When programs such as an Markov Decision Process (MDP) are used, that is machine learning.

To sum it up, not all artificial intelligence programs have to learn. Machine learning is a subset of artificial intelligence programs that learn but do not require networks. Deep learning is a subset of machine learning that uses networks.

2. Deep learning networks mostly reproduce human brain functions. (Yes | No)

Yes in neuroscience research on the human brain. Computer models of the brain using deep learning can provide interesting models.

Sometimes yes, when deep learning networks try to reproduce human vision...

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