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
Other Books You May Enjoy
22
Index

Domain learning

This section on domain learning builds a bridge between classic transfer learning, as described previously, and another use of domain learning I have found profitable on corporate projects: teaching a machine a concept (CRLMM). In this chapter, we are focusing on teaching a machine to learn how to recognize a gap in situations other than at the food processing company.

How to use the programs

You can read this whole chapter first to grasp the concepts or play with the programs first. Do as you feel is best for you. In any case, CNN_TDC_STRATEGY.py loads trained models (you do not have to train them again for this chapter) and CNN_CONCEPT_STRATEGY.py trains the models.

The trained models used in this section

This section uses CNN_TDC_STRATEGY.py to apply the trained models to the target concept images. READ_MODEL.py (as shown previously) was converted into CNN_TDC_STRATEGY.py by adding variable directory paths (for the model3.h5 files and images...