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

Genetic Algorithms in Hybrid Neural Networks

In this chapter and the following two chapters, we will explore the world inside us. First, in this chapter, we will use the model of our genes as an optimizing tool. In Chapter 18, Neuromorphic Computing, we will enter our biological brain activity and create neuromorphic networks. Finally, in Chapter 19, Quantum Computing, we will go even deeper and use the quantum material in us to build quantum mechanic models for quantum computing.

A slight change in any of these tiny entities (genes, neurons, qubits) within us can modify our whole existence.

In this chapter, we will discover how to enter our chromosome, find our genes, and understand how our reproduction process works. From there, we will begin to implement an evolutionary algorithm in Python, a genetic algorithm (GA).

Charles Darwin offered "survival of the fittest" as a model to represent evolution. In some ways, the model is controversial. In 21st century...