You're reading from Artificial Intelligence By Example Acquire advanced AI, machine learning, and deep learning design skills

Product type Paperback

Published in Feb 2020

Publisher Packt

ISBN-13 9781839211539

Length 578 pages

Edition 2nd Edition

Languages

Python

Tools

TensorFlow

Concepts

Artificial Intelligence

Author (1):

Denis Rothman

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Table of Contents (23) Chapters

Preface

1. Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning

2. Building a Reward Matrix – Designing Your Datasets FREE CHAPTER

3. Machine Intelligence – Evaluation Functions and Numerical Convergence

4. Optimizing Your Solutions with K-Means Clustering

5. How to Use Decision Trees to Enhance K-Means Clustering

6. Innovating AI with Google Translate

7. Optimizing Blockchains with Naive Bayes

8. Solving the XOR Problem with a Feedforward Neural Network

9. Abstract Image Classification with Convolutional Neural Networks (CNNs)

10. Conceptual Representation Learning

11. Combining Reinforcement Learning and Deep Learning

12. AI and the Internet of Things (IoT)

13. Visualizing Networks with TensorFlow 2.x and TensorBoard

14. Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA)

15. Setting Up a Cognitive NLP UI/CUI Chatbot

16. Improving the Emotional Intelligence Deficiencies of Chatbots

17. Genetic Algorithms in Hybrid Neural Networks

18. Neuromorphic Computing

19. Quantum Computing

20. Answers to the Questions

21. Other Books You May Enjoy

22. Index

Summary

This chapter drew a distinction between machine intelligence and human intelligence. Solving a problem like a machine means using a chain of mathematical functions and properties. Machine intelligence surpasses humans in many fields.

The further you get in machine learning and deep learning, the more you will find mathematical functions that solve the core problems. Contrary to the astounding amount of hype, mathematics relying on CPUs is replacing humans, not some form of mysterious conscious intelligence.

The power of machine learning reaches beyond human mathematical reasoning. It makes ML generalization to other fields easier. A mathematical model, without the complexity of humans entangled in emotions, makes it easier to deploy the same model in many fields. The models of the first three chapters of this book can be used for self-driving vehicles, drones, robots in a warehouse, scheduling priorities, and much more. Try to imagine as many fields you can apply these...