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  • Book Overview & Buying Artificial Intelligence By Example
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Artificial Intelligence By Example

Artificial Intelligence By Example - Second Edition

By : Denis Rothman
4.6 (17)
Buy this Book
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Artificial Intelligence By Example

Artificial Intelligence By Example

4.6 (17)
By: Denis Rothman
Buy this Book

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

Preface

This second edition of Artificial Intelligence By Example will take you through the main aspects of present-day artificial intelligence (AI) and beyond!

This book contains many revisions and additions to the key aspects of AI described in the first edition:

  • The theory of machine learning and deep learning including hybrid and ensemble algorithms.
  • Mathematical representations of the main AI algorithms including natural language explanations making them easier to understand.
  • Real-life case studies taking the reader inside the heart of e-commerce: manufacturing, services, warehouses, and delivery.
  • Introducing AI solutions that combine IoT, convolutional neural networks (CNN), and Markov decision process (MDP).
  • Many open source Python programs with a special focus on the new features of TensorFlow 2.x, TensorBoard, and Keras. Many modules are used, such as scikit-learn, pandas, and more.
  • Cloud platforms: Google Colaboratory with its free VM, Google Translate, Google Dialogflow, IBM Q for quantum computing, and more.
  • Use of the power of restricted Boltzmann machines (RBM) and principal component analysis (PCA) to generate data to create a meaningful chatbot.
  • Solutions to compensate for the emotional deficiencies of chatbots.
  • Genetic algorithms, which run faster than classical algorithms in specific cases, and genetic algorithms used in a hybrid deep learning neural network.
  • Neuromorphic computing, which reproduces our brain activity with models of selective spiking ensembles of neurons in models that reproduce our biological reactions.
  • Quantum computing, which will take you deep into the tremendous calculation power of qubits and cognitive representation experiments.

This second edition of Artificial Intelligence By Example will take you to the cutting edge of AI and beyond with innovations that improve existing solutions. This book will make you a key asset not only as an AI specialist but a visionary. You will discover how to improve your AI skills as a consultant, developer, professor, a curious mind, or any person involved in artificial intelligence.

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