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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 FREE CHAPTER 2. Think like a Machine 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 6 – Don't Get Lost in Techniques, Focus on Optimizing Your Solutions

1. Can a prototype be built with random data in corporate environments? (Yes | No)

The answer is yes and no. To start developing a prototype, using random data can help make sure that the basic algorithm works as planned.

However, once the prototype is advanced, it will be more reliable to use a well-designed dataset. Then, once the training has been successfully accomplished, random data can help again to see how your system behaves in all situations.

2. Do design matrices contain one example per matrix? (Yes | No)

The answer is no. A good design matrix contains one example in each row or each column depending on the shape you want it to have. But be careful; a design matrix that contains data that is too efficient might overfit. That means the learning algorithm will be efficient with that...

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