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

Applying Nengo's unique approach to critical AI research areas

It is useless to apply the power of brain neuromorphic models to simple arithmetic or classical neural networks that do not require any more than TensorFlow 2.x, for example.

But it is also a waste of time to try to solve problems with classical networks that neuromorphic computing can solve better with organic brain models. For example:

Deep learning, TensorFlow 2. Convolutional models use a unique activation function such as ReLU (see Chapter 9, Abstract Image Classification with Convolutional Neural Networks (CNNs)). Neuromorphic neurons have a variety of reactions when stimulated.
Neuromorphic models integrate time versus more static DL algorithms. When we run neuromorphic models, we are closer to the reality of our time-driven biological models.
The Human Brain Project, https://www.humanbrainproject.eu/en/, provides wide research and examples of how neuromorphic computing provides...