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

Artificial hybrid neural networks

In the previous section, we used a GA to optimize a physical neural network.

In this section, we will extend the concept of hybrid we have just explored to ANNs. The principle is the same, so it will be relatively easy, with the concepts you now have in mind, to intuitively grasp the RNN we will optimize in this section.

The future of AI in society lies in the collective intelligence of humans (diversity), machines (AI and IoT), and nature (sustainable projects for our survival).

In AI, this diversity lies in ensemble algorithms, meta-algorithms and hybrid systems. Deep learning has proven its point. We can create a neural network with TensorFlow 2.x in a few lines. However, more often than not, it takes days, weeks, and often months to fine-tune ANN models that rely on large amounts of data to provide a reliable model. And that's where hybrid neural networks are necessary.

A deep learning network can use any form...