Book Image

Artificial Intelligence By Example - Second Edition

By : Denis Rothman
Book Image

Artificial Intelligence By Example - Second Edition

By: Denis Rothman

Overview of this book

AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing. By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions.
Table of Contents (23 chapters)
21
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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...