Artificial intelligence (AI) Books

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Book SynopsisArshin Adib-Moghaddam is Professor in Global Thought and Comparative Philosophies, Fellow of Hughes Hall, University of Cambridge, UK and Inaugural Co-Director of the SOAS Centre for AI Futures, University of London, UK.Trade ReviewWritten with intellectual flair, this is a stimulating if sobering assessment of what we can expect in a world increasingly dominated by biased AI. A must-read to understand the paradigm shift we are already experiencing, and better anticipate the all too human flaws in the embedded tech so rapidly accumulating in our techno-societies. * Roxane Farmanfarmaian, University of Cambridge, UK *A fascinating work on the age of artificial intelligence, surveillance, and algorithmic regimes. Arshin Adib-Moghaddam asks compelling questions regarding our dice-throw with the virtual, the digital, and the simulated, taking us into those timescapes of the near-beyond where we will have to confront dire questions of our own post-humanism. This work unveils with exceptional precision both the potentiality for catastrophic violence beneath the surface of such epochal technologies yet also an escape-route into its more boundless figurations. * Jason Mohaghegh, Babson College, USA *A cutting-edge piece of work illustrating how we can transform our psychology and change values within an AI-controlled system in the age of post-human society. * Hisae Nakanishi, Doshisha University, Japan *Table of ContentsIntroduction Chapter 1: Beyond Human Robots Chapter 2: The Matrix Decoded Chapter 3: Capital Punishment Chapter 4: Techno-Imperialism Chapter 5: Death-Techniques Conclusion: Decolonial AI - A Manifesto

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Book SynopsisThis text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book's dual approach includes a mixture of methodology and theory.Trade ReviewFrom the reviews: "...The book is excellent." (Short Book Reviews of the ISI, June 2006) "Now we have All of Nonparametric Statistics … the writing is excellent and the author is to be congratulated on the clarity achieved. … the book is excellent." (N.R. Draper, Short Book Reviews, 26:1, 2006) "Overall, I enjoyed reading this book very much. I like Wasserman's intuitive explanations and careful insights into why one path or approach is taken over another. Most of all, I am impressed with the wealth of information on the subject of asymptotic nonparametric inferences." (Stergios B. Fotopoulos for Technometrics, 49:1, February 2007) "The intention of this book is to give a single source with brief accounts of modern topics in nonparametric inference. … The text is a mixture of theory and applications, and there are lots of examples … . The text is also illustrated with many informative figures. … this book covers many topics of modern nonparametric methods, with focus on estimation and on the construction of confidence sets. It should be a useful reference for anyone interested in the theories and methods of this area." (Andreas Karlsson, Statistical Papers, 48, 2006) "...ANPS provides an excellent complement or a complete course textbook with a mixture of theoretical and computational exercises. ...For a book in a rapidly evolving field, the content and references are quit eup to date. ...As advertised, it offers a well-written, albeit brief account of numerous topics in modern nonparametric inference." (Greg Ridgeway, Journal of the American Statistical Association, Vol. 102, No. 477, 2007) "This is a nicely written textbook oriented mainly to master level statistics and computer science students. The author provides wide a coverage of modern nonparametric methods … . the key ideas and basic proofs are carefully explained. Bibliographic remarks point the reader to references that contain further details. Each chapter is finished with useful exercises … . The book is also suitable for researchers in statistics, machine learning, and data mining." (Oleksandr Kukush, Zentralblatt MATH, Vol. 1099 (1), 2007)Table of ContentsEstimating the CDF and Statistical Functionals.- The Bootstrap and the Jackknife.- Smoothing: General Concepts.- Nonparametric Regression.- Density Estimation.- Normal Means and Minimax Theory.- Nonparametric Inference Using Orthogonal Functions.- Wavelets and Other Adaptive Methods.- Other Topics.

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Book SynopsisThis book offers an introduction to the key ideas, basic analysis, and efficient implementation of discontinuous Galerkin finite element methods (DG-FEM) for the solution of partial differential equations.Trade ReviewFrom the reviews: "This book provides comprehensive coverage of the major aspects of the DG-FEM, from derivation, analysis and implementation of the method to simulation of application problems. It is a highly valuable volume in the literature on the DG-FEM. It is also suitable as a textbook for a graduate-level course for students in computational and applied mathematics, physics and engineering." -Mathematical Reviews "The book under review presents basic ideas, theoretical analysis, MATLAB implementation and applications of the DG-FEM. … The representative references quoted are useful for any reader interested in applying the method in a particular area. … This book provides comprehensive coverage of the major aspects of the DG-FEM … . It is a highly valuable volume in the literature on the DG-FEM. It is also suitable as a textbook for a graduate-level course for students in computational and applied mathematics, physics, and engineering." (Weimin Han, Mathematical Reviews, Issue 2008 k) "This book is intended to offer a comprehensive introduction to, and an efficient implementation of discontinuous Galerkin finite element methods … . Each chapter of the book is largely self-contained and is complemented by adequate exercises. … The style of writing is clear and concise … . is an exceptionally complete and accessible reference for graduate students, researchers, and professionals in applied mathematics, physics, and engineering. It may be used in graduate-level courses, as a self-study resource, or as a research reference." (Marius Ghergu, Zentralblatt MATH, Vol. 1134 (12), 2008)Table of ContentsThe key ideas.- Making it work in one dimension.- Insight through theory.- Nonlinear problems.- Beyond one dimension.- Higher-order equations.- Spectral properties of discontinuous Galerkin operators.- Curvilinear elements and nonconforming discretizations.- Into the third dimension.

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Book SynopsisIntegrating all aspects of the fields of metacognition and learning technologies, this book describes features of the learning technologies and how they have been designed to study and support metacognitive processing and self-regulated learning.Table of Contents Planning, sub-goaling, and metareasoning Metacognitive monitoring and control Strategy instruction to support metacognition and learning Control of behavior (e.g., help-seeking behavior) Development of metacognition (knowledge and strategy) Interface between affective and/or motivation processes with metacognition Scaffolding of metacognition External regulating agents (human and artificial) and metacognition Methodological issues in using computer environments as data collection tools to study metacognition.

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Book SynopsisGuide to Intelligent Data Analysis provides a hands-on instructional approach to many basic data analysis techniques, and explains how these are used to solve data analysis problems.Trade ReviewFrom the reviews: “The authors, leading scholars in the field based in Germany and Spain, seek to offer a hands-on instructional approach to basic data analysis techniques and consider their use in solving problems. The reader is taken through the process, following the interlinked steps of project understanding, data understanding, data preparation, modelling, and deployment and monitoring. The text reviews the basics of classical statistics that support and justify many data analysis methods, and includes a glossary of statistical terms.” (Times Higher Education, 26 May 2011)“The clear and complete exposition of arguments, along with the attention to formalization and the balanced number of bibliographic references, make this book a bright introduction to intelligent data analysis. It is an excellent choice for graduate or advanced undergraduate courses, as well as for researchers and professionals who want get acquainted with this field of study. … Overall, the authors hit their target producing a textbook that aids in understanding the basic processes, methods, and issues for intelligent data analysis.” (Corrado Mencar, ACM Computing Reviews, April, 2011)“The book provides a thorough introduction to data mining that covers theoretical background as well as the use of tools (KNIME and R). The book is intended as a textbook for a broad audience from graduate and advanced undergraduate students to professional data analysts. … each chapter ends with a list of references to identify relevant research. Hence, I recommend this book as an introductory text on data analysis for the intended target audience.” (Gottfried Vossen, Zentralblatt MATH, Vol. 1210, 2011)Table of ContentsIntroduction Practical Data Analysis: An Example Project Understanding Data Understanding Principles of Modeling Data Preparation Finding Patterns Finding Explanations Finding Predictors Evaluation and Deployment Appendix A: Statistics Appendix B: The R Project Appendix C: KNIME

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Book SynopsisOne of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. The majority of these papers were collected from the participants at the Third International Machine Learning Workshop.Table of ContentsJudge: A Case-Based Reasoning System.- Changing Language While Learning Recursive Descriptions from Examples.- Learning by Disjunctive Spanning.- Transfer of Knowledge between Teaching and Learning Systems.- Some Approaches to Knowledge Acquisition.- Analogical Learning with Multiple Models.- The World Modelers Project: Objectives and Simulator Architecture.- The Acquisition of Procedural Knowledge through Inductive Learning.- Learning Static Evaluation Functions by Linear Regression.- Plan Invention and Plan Transformation.- A Brief Overview of Explanatory Schema Acquisition.- The EG Project: Recent Progress.- Learning Causal Relations.- Functional Properties and Concept Formation.- Explanation-Based Learning in Logic Circuit Design.- A Proposed Method of Conceptual Clustering for Structured and Decomposable Objects.- Exploiting Functional Vocabularies to Learn Structural Descriptions.- Combining Numeric and Symbolic Learning Techniques.- Learning by Understanding Analogies.- Analogical Reasoning in the Context of Acquiring Problem Solving Expertise.- Planning and Learning in a Design Domain: The Problems Plan Interactions.- Inference of Incorrect Operators.- A Conceptual Framework for Concept Identification.- Neural Modeling as One Approach to Machine Learning.- Steps Toward Building a Dynamic Memory.- Learning by Composition.- Knowledge Acquisition: Investigations and General Principles.- Purpose-Directed Analogy: A Summary of Current Research.- Development of a Framework for Contextual Concept Learning.- On Safely Ignoring Hypotheses.- A Model of Acquiring Problem Solving Expertise.- Another Learning Problem: Symbolic Process Prediction.- Learning at LRI Orsay.- Coper: A Methodology for Learning Invariant Functional Descriptions.- Using Experience as a Guide for Problem Solving.- Heuristics as Invariants and its Application to Learning.- Components of Learning in a Reactive Environment.- The Development of Structures through Interaction.- Complex Learning Environments: Hierarchies and the use of Explanation.- Prediction and Control in an Active Environment.- Better Information Retrieval through Linguistic Sophistication.- Machine Learning Research in the Artificial Intelligence Laboratory at Illinois.- Overview of the Prodigy Learning Apprentice.- A Learning Apprentice System for VLSI Design.- Generalizing Explanations of Narratives into Schemata.- Why Are Design Derivations Hard to Replay?.- An Architecture for Experiential Learning.- Knowledge Extraction through Learning from Examples.- Learning Concepts with a Prototype-Based Model for Concept Representation.- Recent Progress on the Mathematician’s Apprentice Project.- Acquiring Domain Knowledge from Fragments of Advice.- Calm: Contestation for Argumentative Learning Machine.- Directed Experimentation for Theory Revision and Conceptual Knowledge Acquisition.- Goal-Free Learning by Analogy.- A Scientific Approach to Practical Induction.- Exploring Shifts of Representation.- Current Research on Learning in Soar.- Learning Concepts in a Complex Robot World.- Learning Evaluation Functions.- Learning from Data with Errors.- Explanation-Based Manipulator Learning.- Learning Classical Physics.- Views and Causality in Discovery: Modelling Human Induction.- Learning Control Information.- An Investigation of the Nature of Mathematical Discovery.- Learning How to Reach a Goal: A Strategy for the Multiple Classes Classification Problem.- Conceptual Clustering Of Structured Objects.- Learning in Intractable Domains.- On Compiling Explainable Models of a Design Domain.- What Can Be Learned?.- Learning Heuristic Rules from Deep Reasoning.- Learning a Domain Theory by Completing Explanations.- Learning Implementation Rules with Operating-Conditions Depending on Internal Structures in VLSI Design.- Overview of the Odysseus Learning Apprentice.- Learning from Exceptions in Databases.- Learning Apprentice Systems Research at Schlumberger.- Language Acquisition: Learning Phrases in Context.- References.

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Book SynopsisThis book provides a deep dive into the world of generative AI, covering everything from the basics of neural networks to the intricacies of large language models like ChatGPT and Google Bard. It serves as a one-stop resource for anyone interested in understanding and applying this transformative technology and is particularly aimed at those just getting started with generative AI. Applied Generative AI for Beginners is structured around detailed chapters that will guide you from foundational knowledge to practical implementation. It starts with an introduction to generative AI and its current landscape, followed by an exploration of how the evolution of neural networks led to the development of large language models. The book then delves into specific architectures like ChatGPT and Google Bard, offering hands-on demonstrations for implementation using tools like Sklearn. You'll also gain insight into the strategic aspects of implementing generative AI in an enterprise setting, with Table of Contents

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Book SynopsisThe rise of technology has led to rapid developments in robotic intelligence and its various applications. The success or failure of these systems is linked closely with effective perception and cognition models.Aligning Perceptual and Conceptual Information for Cognitive Contextual System Development: Emerging Research and Opportunities is an innovative source of academic content on approaches to cognitive and perceptual systems development in artificial intelligence. Including a range of relevant topics such as object processing, implicit symbols, and knowledge representation, this book is ideally designed for engineers, academics, practitioners, and students interested in perceptual and conceptual interpretation in artificial intelligence.

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Book SynopsisDesign, simulate, and program interactive robots Key Features Design, simulate, build, and program an interactive autonomous mobile robot Leverage the power of ROS, Gazebo, and Python to enhance your robotic skills A hands-on guide to creating an autonomous mobile robot with the help of ROS and Python Book DescriptionRobot Operating System (ROS) is one of the most popular robotics software frameworks in research and industry. It has various features for implement different capabilities in a robot without implementing them from scratch.This book starts by showing you the fundamentals of ROS so you understand the basics of differential robots. Then, you'll learn about robot modeling and how to design and simulate it using ROS. Moving on, we'll design robot hardware and interfacing actuators. Then, you'll learn to configure and program depth sensors and LIDARs using ROS. Finally, you'll create a GUI for your robot using the Qt framework. By the end of this tutorial, you'll have a clear idea of how to integrate and assemble everything into a robot and how to bundle the software package.What you will learn Design a differential robot from scratch Model a differential robot using ROS and URDF Simulate a differential robot using ROS and Gazebo Design robot hardware electronics Interface robot actuators with embedded boards Explore the interfacing of different 3D depth cameras in ROS Implement autonomous navigation in ChefBot Create a GUI for robot control Who this book is forThis book is for those who are conducting research in mobile robotics and autonomous navigation. As well as the robotics research domain, this book is also for the robot hobbyist community. You’re expected to have a basic understanding of Linux commands and Python.Table of ContentsTable of Contents Getting started with ROS Understanding basics of differential robots Modeling the Differential Drive Robot Simulating a Differential Drive Robot Using ROS Designing ChefBot Hardware and Circuits Interfacing Actuators and Sensors to the Robot Controller Interfacing Vision Sensors with ROS Building ChefBot Hardware and the Integration of Software Designing a GUI for a Robot Using Qt and Python Assessments

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Book SynopsisA practical guide to getting the most out of Excel, using it for data preparation, applying machine learning models (including cloud services) and understanding the outcome of the data analysis.Key Features Use Microsoft's product Excel to build advanced forecasting models using varied examples Cover range of machine learning tasks such as data mining, data analytics, smart visualization, and more Derive data-driven techniques using Excel plugins and APIs without much code required Book DescriptionWe have made huge progress in teaching computers to perform difficult tasks, especially those that are repetitive and time-consuming for humans. Excel users, of all levels, can feel left behind by this innovation wave. The truth is that a large amount of the work needed to develop and use a machine learning model can be done in Excel.The book starts by giving a general introduction to machine learning, making every concept clear and understandable. Then, it shows every step of a machine learning project, from data collection, reading from different data sources, developing models, and visualizing the results using Excel features and offerings. In every chapter, there are several examples and hands-on exercises that will show the reader how to combine Excel functions, add-ins, and connections to databases and to cloud services to reach the desired goal: building a full data analysis flow. Different machine learning models are shown, tailored to the type of data to be analyzed.At the end of the book, the reader is presented with some advanced use cases using Automated Machine Learning, and artificial neural network, which simplifies the analysis task and represents the future of machine learning.What you will learn Use Excel to preview and cleanse datasets Understand correlations between variables and optimize the input to machine learning models Use and evaluate different machine learning models from Excel Understand the use of different visualizations Learn the basic concepts and calculations to understand how artificial neural networks work Learn how to connect Excel to the Microsoft Azure cloud Get beyond proof of concepts and build fully functional data analysis flows Who this book is forThis book is for data analysis, machine learning enthusiasts, project managers, and someone who doesn't want to code much for performing core tasks of machine learning. Each example will help you perform end-to-end smart analytics. Working knowledge of Excel is required.Table of ContentsTable of Contents Implementing Machine Learning Algorithms Hands-on examples of machine learning models Importing Data into Excel from Different Data Sources Data cleansing and preliminary data analysis Correlations and the Importance of Variables Data Mining Models in Excel Hands-On Examples Implementing Time Series Visualizing data in diagrams, histograms, and maps Artificial Neural Networks Azure and Excel - Machine Learning in the Cloud The future of Machine Learning

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Book SynopsisBuild smart applications by implementing real-world artificial intelligence projectsKey Features Explore a variety of AI projects with Python Get well-versed with different types of neural networks and popular deep learning algorithms Leverage popular Python deep learning libraries for your AI projects Book DescriptionArtificial Intelligence (AI) is the newest technology that’s being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence.This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library.By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progressWhat you will learn Build a prediction model using decision trees and random forest Use neural networks, decision trees, and random forests for classification Detect YouTube comment spam with a bag-of-words and random forests Identify handwritten mathematical symbols with convolutional neural networks Revise the bird species identifier to use images Learn to detect positive and negative sentiment in user reviews Who this book is forPython Artificial Intelligence Projects for Beginners is for Python developers who want to take their first step into the world of Artificial Intelligence using easy-to-follow projects. Basic working knowledge of Python programming is expected so that you’re able to play around with codeTable of ContentsTable of Contents Building Your Own Prediction Models Prediction with Random Forests Application for comment classification Neural Networks Deep Learning

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Book SynopsisIn today's modernized market, various disciplines continue to search for universally functional technologies that improve upon traditional processes. Artificial neural networks are a set of statistical modeling tools that are capable of processing nonlinear data with strong accuracy. Due to their complexity, utilizing their potential was previously seen as a challenge. However, with the development of artificial intelligence, this technology has proven to be an effective and efficient problem-solving method. Artificial Neural Network Applications in Business and Engineering is an essential reference source that illustrates recent advancements of artificial neural networks in various professional fields, accompanied by specific case studies and practical examples. Featuring research on topics such as training algorithms, transportation, and computer security, this book is ideally designed for researchers, students, developers, managers, engineers, academicians, industrialists, policymakers, and educators seeking coverage on modern trends in artificial neural networks and their real-world implementations.

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Book SynopsisFun and exciting projects to learn what artificial minds can createKey Features Code examples are in TensorFlow 2, which make it easy for PyTorch users to follow along Look inside the most famous deep generative models, from GPT to MuseGAN Learn to build and adapt your own models in TensorFlow 2.x Explore exciting, cutting-edge use cases for deep generative AI Book DescriptionMachines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI?In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks.There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation.What you will learn Export the code from GitHub into Google Colab to see how everything works for yourself Compose music using LSTM models, simple GANs, and MuseGAN Create deepfakes using facial landmarks, autoencoders, and pix2pix GAN Learn how attention and transformers have changed NLP Build several text generation pipelines based on LSTMs, BERT, and GPT-2 Implement paired and unpaired style transfer with networks like StyleGAN Discover emerging applications of generative AI like folding proteins and creating videos from images Who this book is forThis is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.Table of ContentsTable of Contents An Introduction to Generative AI: "Drawing" Data from Models Setting Up a TensorFlow Lab Building Blocks of Deep Neural Networks Teaching Networks to Generate Digits Painting Pictures with Neural Networks Using VAEs Image Generation with GANs Style Transfer with GANs Deepfakes with GANs The Rise of Methods for Text Generation NLP 2.0: Using Transformers to Generate Text Composing Music with Generative Models Play Video Games with Generative AI: GAIL Emerging Applications in Generative AI

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