Artificial Intelligence By Example : Acquire advanced AI, machine learning, and deep learning design skills
/ Denis Rothman
- 2nd Ed.
- Mumbai Packt Publishing Ltd. 2020
- xxi, 549p. : ill. ; 24cm
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