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

Product type Paperback

Published in May 2018

Publisher Packt

ISBN-13 9781788990547

Length 490 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Artificial Intelligence

Author (1):

Denis Rothman

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Table of Contents (19) Chapters

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

Questions

The curse of dimensionality leads to reducing dimensions and features in machine learning algorithms. (Yes | No)
Transfer learning determines the profitability of a project. (Yes | No)
Reading model.h5 does not provide much information. (Yes | No)
Numbers without meaning are enough to replace humans. (Yes | No)
Chatbots prove that body language doesn't mean that much. (Yes | No)
Present-day ANNs provide enough theory to solve all AI requests. (Yes | No)
Chatbots can now replace humans in all situations. (Yes | No)
Self-driving cars have been approved and do not need conceptual training. (Yes | No)
Industries can implement AI algorithms for all of their needs. (Yes | No)

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Authors (1)

Denis Rothman

Denis Rothman graduated from Sorbonne University and Paris-Diderot University, and as a student, he wrote and registered a patent for one of the earliest word2vector embeddings and word piece tokenization solutions. He started a company focused on deploying AI and went on to author one of the first AI cognitive NLP chatbots, applied as a language teaching tool for Moët et Chandon (part of LVMH) and more. Denis rapidly became an expert in explainable AI, incorporating interpretable, acceptance-based explanation data and interfaces into solutions implemented for major corporate projects in the aerospace, apparel, and supply chain sectors. His core belief is that you only really know something once you have taught somebody how to do it.

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