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.

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Table of Contents (23 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning
Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning
Reinforcement learning concepts
How to adapt to machine thinking and become an adaptive thinker
Overcoming real-life issues using the three-step approach
The lessons of reinforcement learning
Summary
Questions
Further reading
Free Chapter
2
Building a Reward Matrix – Designing Your Datasets
Building a Reward Matrix – Designing Your Datasets
Designing datasets – where the dream stops and the hard work begins
Logistic activation functions and classifiers
Summary
Questions
Further reading
3
Machine Intelligence – Evaluation Functions and Numerical Convergence
Machine Intelligence – Evaluation Functions and Numerical Convergence
Tracking down what to measure and deciding how to measure it
Evaluating beyond human analytic capacity
Using supervised learning to evaluate a result that surpasses human analytic capacity
Summary
Questions
Further reading
4
Optimizing Your Solutions with K-Means Clustering
Optimizing Your Solutions with K-Means Clustering
Dataset optimization and control
Implementing a k-means clustering solution
Summary
Questions
Further reading
5
How to Use Decision Trees to Enhance K-Means Clustering
How to Use Decision Trees to Enhance K-Means Clustering
Unsupervised learning with KMC with large datasets
Summary
Questions
Further reading
6
Innovating AI with Google Translate
Innovating AI with Google Translate
Understanding innovation and disruption in AI
Discover a world of opportunities with Google Translate
AI as a new frontier
Summary
Questions
Further reading
7
Optimizing Blockchains with Naive Bayes
Optimizing Blockchains with Naive Bayes
Part I – the background to blockchain technology
PART II – using blockchains to share information in a supply chain
Part III – optimizing a supply chain with naive Bayes in a blockchain process
Summary
Questions
Further reading
8
Solving the XOR Problem with a Feedforward Neural Network
Solving the XOR Problem with a Feedforward Neural Network
The original perceptron could not solve the XOR function
Building an FNN from scratch
Applying the FNN XOR function to optimizing subsets of data
Summary
Questions
Further reading
9
Abstract Image Classification with Convolutional Neural Networks (CNNs)
Abstract Image Classification with Convolutional Neural Networks (CNNs)
Introducing CNNs
Training a CNN model
Summary
Questions
Further reading and references
10
Conceptual Representation Learning
Conceptual Representation Learning
Generating profit with transfer learning
Domain learning
Summary
Questions
Further reading
11
Combining Reinforcement Learning and Deep Learning
Combining Reinforcement Learning and Deep Learning
Planning and scheduling today and tomorrow
CRLMM applied to an automated apparel manufacturing process
Building the RL-DL-CRLMM
Summary
Questions
Further reading
12
AI and the Internet of Things (IoT)
AI and the Internet of Things (IoT)
The public service project
Setting up the RL-DL-CRLMM model
Adding an SVM function
Running the CRLMM
Summary
Questions
Further reading
13
Visualizing Networks with TensorFlow 2.x and TensorBoard
Visualizing Networks with TensorFlow 2.x and TensorBoard
Exploring the output of the layers of a CNN in two steps with TensorFlow
Analyzing the accuracy of a CNN using TensorBoard
Summary
Questions
Further reading
14
Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA)
Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA)
Defining basic terms and goals
Introducing and building an RBM
Using the weights of an RBM as feature vectors for PCA
Summary
Questions
Further reading
15
Setting Up a Cognitive NLP UI/CUI Chatbot
Setting Up a Cognitive NLP UI/CUI Chatbot
Basic concepts
Adding fulfillment functionality to an agent
Machine learning agents
Summary
Questions
Further reading
16
Improving the Emotional Intelligence Deficiencies of Chatbots
Improving the Emotional Intelligence Deficiencies of Chatbots
From reacting to emotions, to creating emotions
Data logging
Creating emotions
RNN research for future automatic dialog generation
Summary
Questions
Further reading
17
Genetic Algorithms in Hybrid Neural Networks
Genetic Algorithms in Hybrid Neural Networks
Understanding evolutionary algorithms
Artificial hybrid neural networks
Summary
Questions
Further reading
18
Neuromorphic Computing
Neuromorphic Computing
Neuromorphic computing
Getting started with Nengo
Applying Nengo's unique approach to critical AI research areas
Summary
Questions
References
Further reading
19
Quantum Computing
Quantum Computing
The rising power of quantum computers
A thinking quantum computer
Summary
Questions
Further reading
20
Answers to the Questions
Chapter 1 – Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning
Chapter 2 – Building a Reward Matrix – Designing Your Datasets
Chapter 3 – Machine Intelligence – Evaluation Functions and Numerical Convergence
Chapter 4 – Optimizing Your Solutions with K-Means Clustering
Chapter 5 – How to Use Decision Trees to Enhance K-Means Clustering
Chapter 6 – Innovating AI with Google Translate
Chapter 7 – Optimizing Blockchains with Naive Bayes
Chapter 8 – Solving the XOR Problem with a Feedforward Neural Network
Chapter 9 – Abstract Image Classification with Convolutional Neural Networks (CNNs)
Chapter 10 – Conceptual Representation Learning
Chapter 11 – Combining Reinforcement Learning and Deep Learning
Chapter 12 – AI and the Internet of Things
Chapter 13 – Visualizing Networks with TensorFlow 2.x and TensorBoard
Chapter 14 – Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA)
Chapter 15 – Setting Up a Cognitive NLP UI/CUI Chatbot
Chapter 16 – Improving the Emotional Intelligence Deficiencies of Chatbots
Chapter 17 – Genetic Algorithms in Hybrid Neural Networks
Chapter 18 – Neuromorphic Computing
Chapter 19 – Quantum Computing
21
Other Books You May Enjoy
Other Books You May Enjoy
22
Index
Index

Unsupervised learning with KMC with large datasets

KMC takes unlabeled data and forms clusters of data points. The names (integers) of these clusters provide a basis to then run a supervised learning algorithm such as a decision tree.

In this section, we will see how to use KMC with large datasets.

When facing a project with large unlabeled datasets, the first step consists of evaluating if machine learning will be feasible or not. Trying to avoid AI in a book on AI may seem paradoxical. However, in AI, as in real life, you should use the right tools at the right time. If AI is not necessary to solve a problem, do not use it.

Use a proof of concept (POC) approach to see if a given AI project is possible or not. A POC costs much less than the project itself and helps to build a team that believes in the outcome. Or, the POC might show that it is too risky to go forward with an ML solution. Intractable problems exist. It's best to avoid spending months on something...

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