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

Summary

In this chapter, we built neuromorphic Python programs from scratch. Populations of neurons, in Nengo ensembles, are made up of neurons. The system then has stimulation functions, connections, and probing objects. Nengo offers many other examples you can explore.

The NEF was designed to implement neuromorphic computing models. The novel concept of SPA shows that our brains have enhanced pointers that have a meaning and are linked to our physical data.

Neuromorphic computing opens tremendous horizons for a complex program that classical machine learning and deep learning cannot solve. Weather forecasting, with the power of the neuromorphic chips that are reaching the market, can tap into the complexity and variety of a machine brain. A machine brain can produce unique calculations by firing hundreds of thousands of neurons with both individual and collective behavior.

We have covered many algorithms and frameworks in this book. We have access...