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

Domain learning

This section on domain learning builds a bridge between classic transfer learning, as described previously, and another use of domain learning I have found profitable on corporate projects: teaching a machine a concept (CRLMM). In this chapter, we are focusing on teaching a machine to learn how to recognize a gap in situations other than at the food processing company.

How to use the programs

You can read this whole chapter first to grasp the concepts or play with the programs first. Do as you feel is best for you. In any case, CNN_TDC_STRATEGY.py loads trained models (you do not have to train them again for this chapter) and CNN_CONCEPT_STRATEGY.py trains the models.

The trained models used in this section

This section uses CNN_TDC_STRATEGY.py to apply the trained models to the target concept images. READ_MODEL.py (as shown previously) was converted into CNN_TDC_STRATEGY.py by adding variable directory paths (for the model3.h5 files and images...