Artificial Intelligence By Example : Acquire advanced AI, machine learning, and deep learning design skills / Denis Rothman

It includes Index Pages.

Table of Contents
22 Chapters
Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning Chevron down icon
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
Building a Reward Matrix – Designing Your Datasets Chevron down icon
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
Machine Intelligence – Evaluation Functions and Numerical Convergence Chevron down icon
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
Optimizing Your Solutions with K-Means Clustering Chevron down icon
Optimizing Your Solutions with K-Means Clustering
Dataset optimization and control
Implementing a k-means clustering solution
Summary
Questions
Further reading
How to Use Decision Trees to Enhance K-Means Clustering Chevron down icon
How to Use Decision Trees to Enhance K-Means Clustering
Unsupervised learning with KMC with large datasets
Summary
Questions
Further reading
Innovating AI with Google Translate Chevron down icon
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
Optimizing Blockchains with Naive Bayes Chevron down icon
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
Solving the XOR Problem with a Feedforward Neural Network Chevron down icon
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
Abstract Image Classification with Convolutional Neural Networks (CNNs) Chevron down icon
Abstract Image Classification with Convolutional Neural Networks (CNNs)
Introducing CNNs
Training a CNN model
Summary
Questions
Further reading and references
Conceptual Representation Learning Chevron down icon
Conceptual Representation Learning
Generating profit with transfer learning
Domain learning
Summary
Questions
Further reading
Combining Reinforcement Learning and Deep Learning Chevron down icon
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
AI and the Internet of Things (IoT) Chevron down icon
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
Visualizing Networks with TensorFlow 2.x and TensorBoard Chevron down icon
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
Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA) Chevron down icon
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
Setting Up a Cognitive NLP UI/CUI Chatbot Chevron down icon
Setting Up a Cognitive NLP UI/CUI Chatbot
Basic concepts
Adding fulfillment functionality to an agent
Machine learning agents
Summary
Questions
Further reading
Improving the Emotional Intelligence Deficiencies of Chatbots Chevron down icon
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
Genetic Algorithms in Hybrid Neural Networks Chevron down icon
Genetic Algorithms in Hybrid Neural Networks
Understanding evolutionary algorithms
Artificial hybrid neural networks
Summary
Questions
Further reading
Neuromorphic Computing Chevron down icon
Neuromorphic Computing
Neuromorphic computing
Getting started with Nengo
Applying Nengo's unique approach to critical AI research areas
Summary
Questions
References
Further reading
Quantum Computing Chevron down icon
Quantum Computing
The rising power of quantum computers
A thinking quantum computer
Summary
Questions
Further reading
Answers to the Questions Chevron down icon
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
Other Books You May Enjoy Chevron down icon
Other Books You May Enjoy
Index Chevron down icon
Index