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

Conceptual Representation Learning

Understanding cutting-edge machine learning and deep learning theory only marks the beginning of your adventure. The knowledge you have acquired should help you become an AI visionary. Take everything you see as opportunities and see how AI can fit into your projects. Reach the limits and skydive beyond them.

This chapter focuses on decision-making through visual representations and explains the motivation that led to conceptual representation learning (CRL) and metamodels (MM), which form CRLMMs.

Concept learning is our human ability to partition the world from chaos to categories, classes, sets, and subsets. As a child and young adult, we acquire many classes of things and concepts. For example, once we understand what a "hole" is, we can apply it to anything we see that is somewhat empty: a black hole, a hole in the wall, a hole in a bank account if money is missing or overspent, and hundreds of other cases.

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