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

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...