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

View More author details

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, like the previous chapter, described a connected IoT process with no humans involved. This trend will expand into every field in the years to come.

This also shows that knowing how to use a tool requires hard work, especially when learning artificial intelligence. Imagining a solution for a given market requires more than hard work. Creativity does not come with work. It develops by freeing your mind from any form of constraint.

Once the solution has been imagined, then comes the fine line between developing too much for a presentation and not showing enough. A CRLMM provides the kind of framework that helps build a technical solution (CNN, MDP, SVM, and optimizers) while keeping everyday concepts that others understand in mind.

The chapter also shows that an artificial intelligence model can contain an ensemble of algorithms, RL, DL, SVM, and CRL cognitive approaches, and more.

The next chapter will take us deeper under the...