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

Training a CNN model

Training a CNN model involves four phases: compiling the model, loading the training data, loading the test data, and running the model through epochs of loss evaluation and parameter-updating cycles.

In this section, the choice of theme for the training dataset will be an example from the food-processing industry. The idea here is not only to recognize an object but to form a concept. We will explore concept learning neural networks further in Chapter 10, Conceptual Representation Learning. For the moment, let's train our model.

The goal

The primary goal of this model consists of detecting production efficiency flaws on a food-processing conveyor belt. The use of CIFAR-10 (images) and MNIST (a handwritten digit database) proves useful to understand and train some models. However, in this example, the goal is not to recognize objects but a concept.

The following image shows a section of the conveyor belt that contains an acceptable ...