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Artificial Intelligence By Example

Artificial Intelligence By Example - Second Edition

By : Denis Rothman
4.6 (17)
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Artificial Intelligence By Example

Artificial Intelligence By Example

4.6 (17)
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)
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21
Other Books You May Enjoy
22
Index

Conceptual Representation Learning

Understanding cutting-edge machine learning and deep learning theory only marks the beginning of your adventure. The knowledge you have acquired should help you become an AI visionary. Take everything you see as opportunities and see how AI can fit into your projects. Reach the limits and skydive beyond them.

This chapter focuses on decision-making through visual representations and explains the motivation that led to conceptual representation learning (CRL) and metamodels (MM), which form CRLMMs.

Concept learning is our human ability to partition the world from chaos to categories, classes, sets, and subsets. As a child and young adult, we acquire many classes of things and concepts. For example, once we understand what a "hole" is, we can apply it to anything we see that is somewhat empty: a black hole, a hole in the wall, a hole in a bank account if money is missing or overspent, and hundreds of other cases.

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Artificial Intelligence By Example
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