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

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

Chapter 3 – Machine Intelligence – Evaluation Functions and Numerical Convergence

Can a human beat a chess engine? (Yes | No)
The answer is no. Today, the highest-level chess tournaments are not between humans but between chess engines. Each chess engine software editor prepares for these competitions by making their algorithms faster and requiring less CPU. Today, a top chess engine running on a smartphone can beat humans. In human-to-human chess competitions, the level of chess has reached very high limits of complexity. Humans now mostly train against machines.
Humans can estimate decisions better than machines with intuition when it comes to large volumes of data. (Yes | No)
The answer is no. The sheer CPU power of an average machine or even a smartphone can generate better results than humans with the proper algorithms.
Building a reinforcement learning program with a Q function is a feat in itself...