Artificial intelligence (AI) Books

Book SynopsisThis book presents a theory of consciousness which is unique and sustainable in nature, based on physiological and cognitive-linguistic principles controlled by a number of socio-psycho-economic factors. In order to anchor this theory, which draws upon various disciplines, the author presents a number of different theories, all of which have been abundantly studied by scientists from both a theoretical and experimental standpoint, including models of social organization, ego theories, theories of the motivational system in psychology, theories of the motivational system in neurosciences, language modeling and computational modeling of motivation. The theory presented in this book is based on the hypothesis that an individual’s main activities are developed by self-motivation, managed as an informational need. This is described in chapters covering self-motivation on a day-to-day basis, the notion of need, the hypothesis and control of cognitive self-motivation and a model of self-motivation which associates language and physiology. The subject of knowledge extraction is also covered, including the impact of self-motivation on written information, non-transversal and transversal text-mining techniques and the fields of interest of text mining. Contents: 1. Consciousness: an Ancient and Current Topic of Study. 2. Self-motivation on a Daily Basis. 3. The Notion of Need. 4. The Models of Social Organization. 5. Self Theories. 6. Theories of Motivation in Psychology. 7. Theories of Motivation in Neurosciences. 8. Language Modeling. 9. Computational Modeling of Motivation. 10. Hypothesis and Control of Cognitive Self-Motivation. 11. A Model of Self-Motivation which Associates Language and Physiology. 12. Impact of Self-Motivation on Written Information. 13. Non-Transversal Text Mining Techniques. 14. Transversal Text Mining Techniques. 15. Fields of Interest for Text Mining. About the Authors Nicolas Turenne is a researcher at INRA in the Science and Society team at the University of Paris-Est Marne la Vallée in France. He specializes in knowledge extraction from texts with theoretical research into relational and stochastic models. His research topics also concern the sociology of uses, food and environmental sciences, and bioinformatics.Table of ContentsIntroduction xi Acknowledgements xiii Chapter 1. Consciousness: an Ancient and Current Topic of Study 1 1.1. Multidisciplinarity of the subject 1 1.2. Terminological outlook 2 1.3. Theological point of view 4 1.4. Notion of belief and autonomy 5 1.5. Scientific schools of thought 6 1.6. The question of experience 7 Chapter 2. Self-motivation on a Daily Basis 9 2.1. In news blogs 9 2.2. Marketing 9 2.3. Appearance 10 2.4. Mystical experiences 11 2.5. Infantheism 11 2.6. Addiction 11 Chapter 3. The Notion of Need 15 3.1. Hierarchy of needs 15 3.1.1. Level-1 needs 16 3.1.2. Level-3 needs 17 3.2. The satiation cycle 18 Chapter 4. The Models of Social Organization 21 4.1. The entrepreneurial model 21 4.2. Motivational and ethical states 23 Chapter 5. Self Theories 29 Chapter 6. Theories of Motivation in Psychology 33 6.1. Behavior and cognition 33 6.2. Theory of self-efficacy 34 6.3. Theory of self-determination 38 6.4. Theory of control 39 6.5. Attribution theory 39 6.6. Standards and self-regulation 42 6.7. Deviance and pathology 47 6.8. Temporal Motivation Theory 48 6.9. Effect of objectives 49 6.10. Context of distance learning 49 6.11. Maintenance model 49 6.12. Effect of narrative 49 6.13. Effect of eviction 50 6.14. Effect of the teacher–student relationship 50 6.15. Model of persistence and change 50 6.16. Effect of the man–machine relationship 51 Chapter 7. Theories of Motivation in Neurosciences 53 7.1. Academic literature on the subject 53 7.2. Psychology and Neurosciences 53 7.3. Neurophysiological theory 54 7.4. Relationship between the motivational system and the emotions 56 7.5. Relationship between the motivational system and language 58 7.6. Relationship between the motivational system and need 59 Chapter 8. Language Modeling 61 8.1. Issues surrounding language 61 8.2. Interaction and language 61 8.3. Development and language 62 8.4. Schools of thought in linguistic sciences 62 8.5. Semantics and combination 68 8.6. Functional grammar 68 8.7. Meaning-Text Theory 69 8.8. Generative lexicon 70 8.9. Theory of synergetic linguistics 70 8.10. Integrative approach to language processing 71 8.11. New spaces for date production 73 8.12. Notion of ontology 75 8.13. Knowledge representation 76 Chapter 9. Computational Modeling of Motivation 81 9.1. Notion of a computational model 81 9.2. Multi-agent systems 81 9.3. Artificial self-organization 85 9.4. Artificial neural networks 87 9.5. Free will theorem 88 9.6. The probabilistic utility model 89 9.7. The autoepistemic model 91 Chapter 10. Hypothesis and Control of Cognitive Self-Motivation 93 10.1. Social groups 93 10.2. Innate self-motivation 95 10.3. Mass communication 96 10.4. The Cost–Benefit ratio 97 10.5. Social representation 98 10.6. The relational environment 99 10.7. Perception 100 10.8. Identity 100 10.9. Social environment 101 10.10. Historical antecedence 102 10.11. Ethics 102 Chapter 11. A Model of Self-Motivation which Associates Language and Physiology 105 11.1. A new model 105 11.2. Architecture of a self-motivation subsystem 106 11.3. Level of certainty 108 11.4. Need for self-motivation 108 11.5. Notion of motive 109 11.6. Age and location 113 11.7. Uniqueness 113 11.8. Effect of spontaneity 114 11.9. Effect of dependence 114 11.10. Effect of emulation 115 11.11. Transition of belief 115 11.12. Effect of individualism 117 11.13. Modeling of the groups of beliefs 117 Chapter 12. Impact of Self-Motivation on Written Information 123 12.1. Platform for production and consultation of texts 123 12.2. Informational measure of the motives of self-motivation 124 12.2.1. Intra-phrastic extraction 125 12.2.2. Inter-phrastic extraction 126 12.2.3. Meta-phrastic extraction 128 12.3. The information market 129 12.4. Types of data 130 12.5. The outlines of text mining 133 12.6. Software economy 139 12.7. Standards and metadata 139 12.8. Open-ended questions and challenges for text-mining methods 140 12.9. Notion of lexical noise 141 12.10. Web mining 143 12.11. Mining approach 145 Chapter 13. Non-Transversal Text Mining Techniques 147 13.1. Constructivist activity 147 13.2. Typicality associated with the data 148 13.3. Specific character of text mining 148 13.4. Supervised, unsupervised and semi-supervised techniques 149 13.5. Quality of a model 149 13.6. The scenario 149 13.7. Representation of a datum 150 13.8. Standardization 151 13.9. Morphological preprocessing 152 13.10. Selection and weighting of terminological units 153 13.11. Statistical properties of textual units: lexical laws 154 13.12. Sub-lexical units 155 13.14. Shallow parsing or superficial syntactic analysis 157 13.15. Argumentation models 158 Chapter 14. Transversal Text Mining Techniques 159 14.1. Mixed and interdisciplinary text mining techniques 159 14.1.1. Supervised, unsupervised and semi-supervised techniques 159 14.2. Techniques for extraction of named entities 160 14.3. Inverse methods 163 14.4. Latent Semantic Analysis 164 14.5. Iterative construction of sub-corpora 165 14.6. Ordering approaches or ranking method 165 14.7. Use of ontology 166 14.8. Interdisciplinary techniques 167 14.9. Information visualization techniques 167 14.10. The k-means technique 168 14.11. Naive Bayes classifier technique 169 14.12. The k-nearest neighbors (KNN) technique 170 14.13. Hierarchical clustering technique 171 14.14. Density-based clustering techniques 172 14.15. Conditional fields 175 14.16. Nonlinear regression and artificial neural networks 176 14.17. Models of multi-agent systems (MASs) 177 14.18. Co-clustering models 178 14.19. Dependency models 179 14.20. Decision tree technique 179 14.21. The Support Vector Machine (SVM) technique 180 14.22. Set of frequent items 182 14.23. Genetic algorithms 184 14.24. Link analysis with a theoretical graph model 184 14.25. Link analysis without a graph model 185 14.26. Quality of a model 186 14.27. Model selection 189 Chapter 15. Fields of Interest for Text Mining 191 15.1. The avenues in text mining 191 15.1.1. Organization 191 15.1.2. Discovery 193 15.2. About decision support 194 15.3. Competitive intelligence (vigilance) 195 15.4. About strategy 197 15.5. About archive management 200 15.6. About sociology and the legal field 203 15.7. About biology 215 15.8. About other domains 219 Conclusion 221 Bibliography 225 Index 267

Read more less

Book Synopsis'Artificial intelligence is going to have a massive impact on everyone’s lives... an accessible and sensible read that helps demystify AI' Deborah Meaden, entrepreneur and star of Dragon's Den'Nigel Toon is a visionary leader in the field of artificial intelligence... a must-read' Marc Tremblay, Distinguished Engineer, MicrosoftThose who understand how AI thinks are about to win big.We are used to thinking of computers as being a step up from calculators - very good at storing information, and maybe even at playing a logical game like chess. But up to now they haven't been able to think in ways that are intuitive, or respond to questions as a human might. All that has changed, dramatically, in the past few years.Our search engines are becoming answer engines. Artificial intelligence is already revolutionising sectors from education to healthcare to the creative arts. But how does an AI understand sentiment or context? How does it play and win games that have an almost infinite number of moves? And how can we work with AI to produce insights and innovations that are beyond human capacity, from writing code in an instant to unfolding the elaborate 3D puzzles of proteins?We stand at the brink of a historic change that will disrupt society and at the same time create enormous opportunities for those who understand how AI thinks. Nigel Toon shows how we train AI to train itself, so that it can paint images that have never existed before or converse in any language. In doing so he reveals the strange and fascinating ways that humans think, too, as we learn how to live in a world shared by machine intelligences of our own creation.Trade ReviewFew books are more timely than How AI Thinks, an accessible guide that walks the reader through the technology’s developmental history right back to the days before the computer... This is a fascinating read. -- Simon Hunt * Evening Standard *I believe that AI is going to have a massive impact on everyone’s lives; it’s such a hugely important topic that we can’t just leave it to technologists and governments to think about. Business people, teachers, students and parents - everyone needs to learn more about it. In How AI Thinks, Nigel Toon provides us with an accessible and sensible read that helps demystify AI and lets us all understand more about this incredibly powerful tool. -- Deborah Meaden, entrepreneur and star of Dragon's DenNigel Toon is not only a visionary leader in the field of artificial intelligence, but also a captivating storyteller who takes us on a journey through his own fascinating history and the evolution of our young industry. He has a gift for explaining complex concepts in simple terms, making this book accessible and engaging for anyone interested in AI. He also offers a prescriptive and optimistic view of the future of AI, showing how it can transform our lives and society for the better. This book is a must-read for anyone who wants to understand the past, present and future of artificial intelligence. -- Marc Tremblay PhD, Distinguished Engineer, MicrosoftAn insightful, informative, inspiring book which takes the reader on a journey of discovery, it ultimately paints a hopeful and reasoned vision of how humanity can move on from a position of fear and trepidation, and embrace AI, deriving profound benefit from all it makes possible. Nigel has a skill in taking highly technical content and making AI not just comprehensible, but also engaging. -- Professor Evelyn Welch, Vice-Chancellor and President, University of BristolAs a business leader, it was great to have all the strands that have created AI pulled together. Nigel Toon synthesizes everything so clearly, simply and in such an inspiring way. How AI Thinks delivers the perspective that leaders and politicians need so that they can regulate AI well. -- Sir Andrew MacKenzie, Chairman of Shell

Read more less

Book Synopsis

Read more less

Book Synopsis1.Transforming Software Development: From Traditional Methods to Generative Artificial Intelligence.- 2.Case Study: Transforming Operational and Organizational Efficiency Using Artificial Intelligence and Machine Learning.- 3.Revolutionizing Software Development: The Transformative Influence of Machine Learning Integrated SDLC Model.- 4.Generative Coding: Unlocking Ontological AI.- 5.Case Studies: Machine Learning Approaches for Software Development Effort Estimation.- 6.Hybridizing Metaheuristics and Analogy-based Methods with Ensemble Learning for Improved Software Cost Estimation.- 7.A Review on Detection of Design Pattern in Source Code Using Machine Learning Techniques.- 8.Machine Learning Techniques for the Measurement of Software Attributes.- 9.An Effective Analysis of New Metaheuristic Algorithms and its Performance Comparison.- 10.Empowering Software Security: Leveraging Machine Learning for Anomaly Detection and Threat Prevention.- 11.Sentiment Analysis on Movie Reviews Using the Convolutional LSTM (Co-LSTM) Model.- 12.An Overview of AI Workload Optimization Techniques.- 13.Opportunity Discovery for Effective Innovation Using Artificial Intelligence.- 14.Applications of Machine Learning Algorithms in Open Innovation.

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

Book SynopsisThis book highlights new trends and challenges in intelligent systems, which play an important part in the digital transformation of many areas of science and practice. It includes papers offering a deeper understanding of the human-centred perspective on artificial intelligence, of intelligent value co-creation, ethics, value-oriented digital models, transparency, and intelligent digital architectures and engineering to support digital services and intelligent systems, the transformation of structures in digital businesses and intelligent systems based on human practices, as well as the study of interaction and the co-adaptation of humans and systems. All papers were originally presented at the International KES Conference on Human Centred Intelligent Systems 2020 (KES HCIS 2020), held on June 17–19, 2020, in Split, Croatia.

Read more less

Book SynopsisThis beginner-oriented book will help you understand and perform anomaly detection by learning cutting-edge machine learning and deep learning techniques. This updated second edition focuses on supervised, semi-supervised, and unsupervised approaches to anomaly detection. Over the course of the book, you will learn how to use Keras and PyTorch in practical applications. It also introduces new chapters on GANs and transformers to reflect the latest trends in deep learning. Beginning Anomaly Detection Using Python-Based Deep Learning begins with an introduction to anomaly detection, its importance, and its applications. It then covers core data science and machine learning modeling concepts before delving into traditional machine learning algorithms such as OC-SVM and Isolation Forest for anomaly detection using scikit-learn. Following this, the authors explain the essentials of machine learning and deep learning, and how to implement multilayer perceptrons for supervised anomaly detection in both Keras and PyTorch. From here, the focus shifts to the applications of deep learning models for anomaly detection, including various types of autoencoders, recurrent neural networks (via LSTM), temporal convolutional networks, and transformers, with the latter three architectures applied to time-series anomaly detection. This edition has a new chapter on GANs (Generative Adversarial Networks), as well as new material covering transformer architecture in the context of time-series anomaly detection. After completing this book, you will have a thorough understanding of anomaly detection as well as an assortment of methods to approach it in various contexts, including time-series data. Additionally, you will have gained an introduction to scikit-learn, GANs, transformers, Keras, and PyTorch, empowering you to create your own machine learning- or deep learning-based anomaly detectors. What You Will Learn Understand what anomaly detection is, why it it is important, and how it is applied Grasp the core concepts of machine learning. Master traditional machine learning approaches to anomaly detection using scikit-kearn. Understand deep learning in Python using Keras and PyTorch Process data through pandas and evaluate your model's performance using metrics like F1-score, precision, and recall Apply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications Who This Book Is For Data scientists and machine learning engineers of all levels of experience interested in learning the basics of deep learning applications in anomaly detection.Table of Contents

Read more less

Book SynopsisThis book will teach you the underlying concepts of large language models (LLMs), as well as the technologies associated with them. The book starts with an introduction to the rise of conversational AIs such as ChatGPT, and how they are related to the broader spectrum of large language models. From there, you will learn about natural language processing (NLP), its core concepts, and how it has led to the rise of LLMs. Next, you will gain insight into transformers and how their characteristics, such as self-attention, enhance the capabilities of language modeling, along with the unique capabilities of LLMs. The book concludes with an exploration of the architectures of various LLMs and the opportunities presented by their ever-increasing capabilities—as well as the dangers of their misuse. After completing this book, you will have a thorough understanding of LLMs and will be ready to take your first steps in implementing them into your own projects. What You Will Learn Grasp the underlying concepts of LLMs Gain insight into how the concepts and approaches of NLP have evolved over the years Understand transformer models and attention mechanisms Explore different types of LLMs and their applications Understand the architectures of popular LLMs Delve into misconceptions and concerns about LLMs, as well as how to best utilize them Who This Book Is For Anyone interested in learning the foundational concepts of NLP, LLMs, and recent advancements of deep learningTable of ContentsChapter 1: Introduction.- Chapter 2: NLP Through the Ages.- Chapter 3: Transformers.- Chapter 4: What Makes LLMs Large?.- Chapter 5: Popular LLMs.- Chapter 6: Threats, Opportunities, and Misconceptions.

Read more less

Book SynopsisUnderstand and apply the design patterns outlined in this book to design, develop, and deploy scalable AI solutions that meet your organization's needs and drive innovation in the era of intelligent automation. This book begins with an overview of scalable AI systems and the importance of design patterns in creating robust intelligent solutions. It covers fundamental concepts and techniques for achieving scalability in AI systems, including data engineering practices and strategies. The book also addresses scalable algorithms, models, infrastructure, and architecture considerations. Additionally, it discusses deployment, productionization, real-time and streaming data, edge computing, governance, and ethics in scalable AI. Real-world case studies and best practices are presented, along with insights into future trends and emerging technologies. The book focuses on scalable AI and design patterns, providing an understanding of the challenges involved in developing AI systems that can handle large amounts of data, complex algorithms, and real-time processing. By exploring scalability, you will be empowered to design and implement AI solutions that can adapt to changing data requirements. What You Will LearnDevelop scalable AI systems that can handle large volumes of data, complex algorithms, and real-time processingKnow the significance of design patterns in creating robust intelligent solutionsUnderstand scalable algorithms and models to handle extensive data and computing requirements and build scalable AI systemsBe aware of the ethical implications of scalable AI systemsWho This Book Is ForAI practitioners, data scientists, and software engineers with intermediate-level AI knowledge and experience

Read more less

Book SynopsisThis transformative book explores the power of artificial intelligence (AI) in revolutionizing the utilities industry. It covers crucial topics such as intelligent grids, decentralized energy resources, customer engagement, electric vehicle integration, and more, providing a comprehensive and practical guide to successfully navigate the energy transition. In today's world, the urgency of addressing climate change and transitioning to sustainable energy systems is undeniable. With approximately 60 percent of global greenhouse gas emissions attributed to the energy sector, utilities play a vital role in achieving sustainability goals. The traditional utility business model faces disruption from renewable energy, changing consumer expectations, and regulatory shifts. Embracing AI emerges as a key solution to optimize operations, enhance grid reliability, and meet evolving customer demands. Through compelling case studies and industry-specific use cases, you will discover how AI drives innovation, improves operational efficiency, and contributes to a greener and more sustainable world. As the demand for cleaner and more sustainable energy practices grows, this book demonstrates how AI can support utilities in meeting these demands, making them more resilient, agile, and customer-centric. Whether you're a seasoned industry expert or a curious student, this book equips you with the knowledge and insights to embrace sustainability, navigate the complex energy landscape, leverage AI to shape a positive future, and join the movement towards a greener world, empowered by AI's potential in the utilities industry. What You Will LearnUnderstand the challenges and opportunities for utilities in the context of climate change, energy poverty, and the evolving business landscapeDiscover how rapid transformation is needed in the utilities sector to overcome challenges and leverage opportunities for a sustainable futureGain insight into the role of technology, particularly artificial intelligence (AI), as a critical tool for utilities in their transformation journeyBe aware of how AI can be applied in building the future utility industry, including its potential impact on energy efficiency, intelligent energy ecosystems, community engagement, and new business modelsGain knowledge of the adoption of AI and machine learning technologies in the utility industry, including the current state, barriers, significant influencing factors, and an AI adoption maturity model for utilitiesRecognize the sustainability imperative for utilities and how AI can help in achieving sustainable energy practicesBecome familiar with the transformation of power generation, microgrids, intelligent transmission and distribution systems, utilities retail, mobility through electric vehicles, and the integration of distributed energy resources(DER) using AIGain insight into the potential of AI in addressing challenges and driving innovation in the energy ecosystem, such as optimizing power generation assets, enhancing grid intelligence, improving customer service, and enabling clean energy awareness in the metaverseWho This Book Is ForProfessionals and decision makers in the global utilities industry who want to leverage artificial intelligence (AI) technologies to transform their operations and address challenges and opportunities in the energy sector. This book may also appeal to researchers, academics, and students in the fields of energy engineering, environmental science, data analytics, and AI who want to gain a deeper understanding of AI in the utilities sector and its implications for sustainable energy systems.

Read more less

Book SynopsisExplore the capabilities of ChatGPT and gain insight on how to utilize this AI tool in your daily tasks, and marketing endeavors. This book introduces ChatGPT, covering its architecture, training process, and applications across various fields. Start by delving into the benefits of integrating ChatGPT into everyday routines, emphasizing its potential to streamline tasks, optimize time management, and provide valuable insights that can revolutionize individuals' work approaches. You'll then look more closely at ChatGPT's mechanics, its capabilities, limitations, and unique features. The book also outlines the best practices for utilizing ChatGPT, offering practical tips, techniques, and strategies to enhance output quality and reliability, while minimizing errors and maximizing results. You'll focus on ChatGPT's relevance in marketing tasks, such as generating product descriptions, creating email templates, automating social media posts, and addressing customer inquiries. The book concludes by exploring techniques for marketing with ChatGPT, including integration with other tools, data analysis, reporting, and customizing ChatGPT to meet specific marketing needs. In the end, you'll have the knowledge and skills needed to leverage ChatGPT's AI marketing capabilities and to harness its power for success in the digital age. What You'll LearnUnderstand the concepts and workings of ChatGPT, its architecture, and the training processApply the best practices for ChatGPTCreate email templates and automate social media posts using ChatGPTUse ChatGPT for data analysis and reportingWho This Book Is ForMarketing professionals, business owners and entrepreneurs, content creators, and customer service representatives

Read more less

Book SynopsisThis book will help you comprehend the impact of artificial intelligence (AI) on information security, data privacy, and data security. The book starts by explaining the basics and setting the goals for a complete understanding of how AI, Information Security, Data Privacy, and Data Security all connect.

Read more less

Book SynopsisChapter 1: Introduction to Machine Learning Applications Development and the UN SDGs.- Chapter 2: Utilizing Machine Learning Algorithms for Power generation prediction and classification in Wind Farms.- Chapter 3: Crop Recommendation System Using Machine Learning Algorithms for achieving SDGs 2, 9, and 12.- Chapter 4: Aligning Manufacturing Emissions with SDGs 9 and 13 Using Machine Learning Algorithms.- Chapter 5: Water Potability Testing Using Machine Learning.- Applying Machine Learning for Air Quality Monitoring Targeting SDG 3 and 13.- Chapter 7: Clustering the Development of Worldwide Internet Connectivity with Unsupervised Learning for SDGs 7, 9, and 11.

Read more less

Book SynopsisChapter 1: Evolution and Significance of Large Language Models.- Chapter 2: What Are Large Language Models?.- Chapter 3: Python for LLMs.- Chapter 4: Python and Other Programming Approaches.- Chapter 5: Basic overview of the components of the LLM architectures.- Chapter 6: Applications of LLMs in Python.- Chapter 7: Harnessing Python 3.11 and Python Libraries for LLM Development.

Read more less

Book SynopsisThis book delves into a broad spectrum of topics, covering the foundational aspects of Large Language Models (LLMs) such as PaLM, LLaMA, BERT, and GPT, among others. The book takes you through the complexities involved in creating and deploying applications based on LLMs, providing you with an in-depth understanding of the model architecture. You will explore techniques such as fine-tuning, prompt engineering, and retrieval augmented generation (RAG). The book also addresses different ways to evaluate LLM outputs and discusses the benefits and limitations of large models. The book focuses on the tools, techniques, and methods essential for developing Large Language Models. It includes hands-on examples and tips to guide you in building applications using the latest technology in Natural Language Processing (NLP). It presents a roadmap to assist you in navigating challenges related to constructing and deploying LLM-based applications. By the end of the book, you will understand LLMs and build applications with use cases that align with emerging business needs and address various problems in the realm of language processing. What You Will LearnBe able to answer the question: What are Large Language Models?Understand techniques such asprompt engineering, fine-tuning, RAG, and vector databasesKnowthe best practices for effective implementationKnow the metrics and frameworks essential for evaluating the performance of Large Language ModelsWho This Book Is ForAn essential resource for AI-ML developers and enthusiasts eager to acquire practical, hands-on experience in this domain; also applies to individuals seeking a technical understanding of Large Language Models (LLMs) and those aiming to build applications using LLMs

Read more less

Book SynopsisAs artificial intelligence (AI) rapidly transforms education, tools like ChatGPT and Claude are revolutionizing the way we teach and learn. This book is a groundbreaking book that empowers parents and students to navigate this exciting new frontier, filling a critical gap in the current literature. As the first comprehensive guide to generative AI in education designed for parents and students, Generative AI in Education is positioned to become an indispensable resource. It provides the knowledge and strategies needed to effectively integrate AI into their learning journeys, transforming educational outcomes and preparing students for success in a rapidly changing world.You'll gain a deep understanding of how tools like ChatGPT and Claude work, and how they can be leveraged to support learning across various subjects and grade levels. You'll then see how to create clear, specific, and engaging prompts that elicit valuable responses from AI-powered tools. This book contains all the techniques for tailoring prompts to different learning objectives, styles, and contexts, and how they can use AI tools to support reading comprehension, writing skills, problem-solving, and creative thinking.What You Will LearnApply generative AI in educationCraft effective prompts for personalized learning experiencesUtilize AI tools to support learning, creativity, and problem-solvingWho This Book is ForParents and students who are eager to harness the power of generative AI to enhance learning experiences and prepare for success in an AI-driven future

Read more less

Book SynopsisPart 1: Defining Your AI Application.- Chapter 1: Human Flexibility and AI Specialization.- Chapter 2: Meta Systems.- Chapter 3: Prediction Machines.- Part 2: Designing Your AI Application.- Chapter 4: Anatomy of an AI Application.- Chapter 5: Data, Machine Learning, and Reasoners.- Chapter 6: Large Language Models (LLMs).- Chapter 7: AI Agents.- Part 3: Maintaining Your AI Application.- Chapter 8: Testing Your Enterprise AI Application.- Chapter 9: Testing automation for enterprise ai applications.- Chapter 10: Security.- Chapter 11: Information Curation.- Part 4: AI Enabled Teams.- Chapter 12: Remote Work and Reskilling.- Chapter 13: Expert Personas.- Chapter 14: The Role of the AI Handler.- Chapter 15: Legal and Ethical Considerations.

Read more less

Book SynopsisThis is a comprehensive exploration into the world of Artificial Intelligence, designed to bridge the gap between theoretical concepts and practical, real-world applications. This book unravels the mystique of AI, breaking down its components into understandable elements. From the early dawn of AI's inception to its current state of rapid evolution, we cover the essential building blocks necessary for leveraging AI in business, and personal development, and understanding its broader impacts on society. Through an engaging conversational format, readers are guided through the intricacies of AI, covering topics such as machine learning, AI governance,, data security, and the ethical challenges facing AI today. This book is an invaluable resource for those looking to understand the fundamentals of AI, its practical applications, and its significant implications for the future. After reading this book, you will be able to integrate AI into your business strategies and learn the intricacies of AI advancements. What You Will Learn:Key concepts and definitions within AI, including types of AI, machine learning, and neural networks and how they are utilized in AI apps like M365 CopilotPractical applications of AI for personal and business growth, focusing on the pillars of using AI to evolve these fronts effectively and sustainablyHow AI is transforming businesses and what organizational shifts must be made to realize the valueNavigating the challenges and ethical considerations in AI to ensure informed and responsible usageWho This Book Is For:Professionals looking to integrate AI into their business strategies or organizations.

Read more less

Book SynopsisThis comprehensive guide provides practical strategies for optimizing Generative AI systems to be more sustainable and responsible. As advances in Generative AI such as large language models accelerate, optimizing these resource-intensive workloads for efficiency and alignment with human values grows increasingly urgent. The book starts with the concept of Generative AI and its wide-ranging applications, while also delving into the environmental impact of AI workloads and the growing importance of adopting sustainable AI practices. It then delves into the fundamentals of efficient AI workload management, providing insights into understanding AI workload characteristics, measuring performance, and identifying bottlenecks and inefficiencies. Hardware optimization strategies are explored in detail, covering the selection of energy-efficient hardware, leveraging specialized AI accelerators, and optimizing hardware utilization and scheduling for sustainable operations. You are also guided through software optimization techniques tailored for Generative AI, including efficient model architecture, compression, and quantization methods, and optimization of software libraries and frameworks. Data management and preprocessing strategies are also addressed, emphasizing efficient data storage, cleaning, preprocessing, and augmentation techniques to enhance sustainability throughout the data life cycle. The book further explores model training and inference optimization, cloud and edge computing strategies for Generative AI, energy-efficient deployment and scaling techniques, and sustainable AI life cycle management practices, and concludes with real-world case studies and best practicesBy the end of this book, you will take away a toolkit of impactful steps you can implement to minimize the environmental harms and ethical risks of Generative AI. For organizations deploying any type of generative model at scale, this essential guide provides a blueprint for developing responsible AI systems that benefit society. What You Will LearnUnderstand how Generative AI can be more energy-efficient through improvements such asmodel compression, efficient architecture, hardware optimization, and carbon footprint trackingKnowthe techniques to minimize data usage, includingevaluation, filtering, synthesis, few-shot learning, and monitoring data demands over timeUnderstand spanning efficiency, data minimization, and alignment for comprehensive responsibilityKnow the methods for detecting, understanding, and mitigating algorithmic biases, ensuring diversity in data collection, and monitoring model fairnessWho This book Is ForProfessionals seeking to adopt responsible and sustainable practices in their Generative AI work; leaders and practitioners who need actionable strategies and recommendations that can be implemented directly in real-world systems and organizational workflows; ML engineers and data scientists building and deploying Generative AI systems in industry settings; and researchers developing new generative AI techniques, such as at technology companies or universities

Read more less

Book SynopsisChapter 1: Decoding Ecommerce: Business Models for Delivering Value.- Chapter 2: Ecommerce Platform: Journey from Click to Conversion.- Chapter 3: Ecommerce Merchandising: Presenting Curated Products.- Chapter 4: Ecommerce Search: Matching Query to Products.- Chapter 5: Recommendations: Creating Curated Choices.- Chapter 6: Ranking Algorithms: The Science of Sorting.- Chapter 7: Personalization: AI-crafted Personalized Experiences.- Chapter 8: Efficiency: Efficient Ecommerce Deliveries.

Read more less

Book SynopsisUnlock the future of software development and empower yourself to elevate your Python applications by harnessing the power of AI as this field continues to grow and evolve. Perfect for beginner to intermediate Python programmers, this book breaks down the essentials of using ChatGPT and OpenAI APIs. You'll start with the basics, learning to authenticate, send prompts, generate responses, test in the Playground, and handle errors with ease. Each chapter includes hands-on exercises that bring concepts to life, demonstrating different API functionalities and practical applications. You'll master models like GPT-4o, GPT-4, GPT-3.5, Whisper, and DALL-E, enabling you to enhance your applications with cutting-edge AI. Discover how generative AI tools like ChatGPT can automate tedious tasks rather than replace jobs. Leverage ChatGPT's powerful Natural Language Processing (NLP) capabilities to handle various formats of unstructured text within your Python apps. Quickly see how easy it is to use ChatGPT as your AI-pair programmer, boosting your productivity and speed. This step-by-step guide will have you creating intelligent chatbots that can automatically process messages from Slack or Discord. With Beginning ChatGPT for Python, you'll master the ChatGPT and OpenAI APIs, building intelligent applications that offer a personalized and engaging user experience. What You'll LearnConnect with the ChatGPT and OpenAI APIs and send effective prompts. Harness parameters like temperature and top_p to create unique and engaging responses from ChatGPT. Create an intelligent assistant bot for Slack that automates tasks and enhances productivity. Develop a bot that can moderate conversations and manage communities on Discord. Add context to your prompts to get more accurate and relevant responses. Who This Book Is ForPython developers and enthusiasts who aspire to employ OpenAI and ChatGPT in the creation of intelligent applications to enhance productivity.

Read more less

Book SynopsisChapter 1: Unraveling the Basics of Generative AI.- Chapter 2: Exploring the Transformative Potential of Generative AI.- Chapter 3: Revolutionizing Content: Generative AI in Marketing and Advertising.- Chapter 4: Elevating Customer Interactions with Generative AI.- Chapter 5: Streamlining Operations through Generative AI.- Chapter 6: Pioneering Products with Generative AI.- Chapter 7: Charting the Course: Strategies for Successful Generative AI Implementation.- Chapter 8: Navigating Risks and Legalities of Generative AI.- Chapter 9: Quantifying Success: Evaluating the ROI of Generative AI Initiatives.- Chapter 10: Looking Ahead: Preparing for the Future of Generative AI.

Read more less

Book SynopsisAs leaders plan to make significant investments to harness the power of foundational models such as ChatGPT, they need to understand the changes in organizational behaviors required for the successful implementation of such systems. The size, complexity and nature of this new wave of technologies requires a refresh in roles and responsibilities in conventional IT organizations. This book reveals practical and no-nonsense guidance on how to leverage generative AI to transform your business processes and organizational structures to achieve breakthroughs in efficiency, effectiveness and competitive advantage. Written in a lively, engaging, and often humorous style, this work provides practical insights and timely survival skills for leaders with anonymous but real-world experiences and case studies. If you're looking to understand how large language foundation models such as ChatGPT are reshaping managerial roles and organizational structures, and how they can leverage this knowledge to survive and thrive in this brave new world then Organizing for Generative AI and the Productivity Revolution is the book for you. What You Will Learn Review the key changes in current state roles and responsibilities that are required to successfully deploy generative AI systemsExamine the organizational reporting structures and associated incentives that form a strong generative AI systemUnderstand the financial, regulatory, and operational risks created by organizational behavioral issues that arise when organizations build and deploy large language modelsCompare the strategic differences in emerging versus traditional organizational behaviors, incentives, roles and responsibilitiesWho This Book Is ForExecutives and team leaders at enterprises large and small. The book addresses an important topic: how to set up an organization for success, particularly in Generative AI. Generative AI brings new challenges to organizations in terms of how to structure the organization for success, mitigating risks, software development lifecycle, and tracking ROI. I could think of no better person to tackle these issues than Arthur O'Connor, who has extensive experience in technology within some of the largest enterprises in Wall Street, complemented by his academic background. He brings an insightful and unique perspective for technology leaders who want to set up their organizations for success in Generative AI.- Joseph Sabelja, Generative AI Firmwide Platform Lead, J P Morgan

Read more less

Book SynopsisChapter 1: Introduction: General Purpose Robotics.- Chapter 2: Robot Perception: Sensors and Image Processing.- Chapter 3: Robot Perception: 3D Data and Sensor Fusion.- Chapter 4: Foundation Models in Robotics.- Chapter 5: Simulation.- Chapter 6: Mapping, Localization, and Navigation.- Chapter 7: Reinforcement Learning and Control.- Chapter 8: Self Driving Cars.- Chapter 9: Industrial Robotics.- Chapter 10: Humanoid Robotics.- Chapter 11: Data-Driven Robotics in Practice.

Read more less

Book SynopsisThis book discusses deep learning, from its fundamental principles to its practical applications, with hands-on exercises and coding. It focuses on deep learning techniques and shows how to apply them across a wide range of practical scenarios. The book begins with an introduction to the core concepts of deep learning. It delves into topics such as transfer learning, multi-task learning, and end-to-end learning, providing insights into various deep learning models and their real-world applications. Next, it covers neural networks, progressing from single-layer perceptrons to multi-layer perceptrons, and solving the complexities of backpropagation and gradient descent. It explains optimizing model performance through effective techniques, addressing key considerations such as hyperparameters, bias, variance, and data division. It also covers convolutional neural networks (CNNs) through two comprehensive chapters, covering the architecture, components, and significance of kernels implementing well-known CNN models such as AlexNet and LeNet. It concludes with exploring autoencoders and generative models such as Hopfield Networks and Boltzmann Machines, applying these techniques to a diverse set of practical applications. These applications include image classification, object detection, sentiment analysis, COVID-19 detection, and ChatGPT. By the end of this book, you will have gained a thorough understanding of deep learning, from its fundamental principles to its innovative applications, enabling you to apply this knowledge to solve a wide range of real-world problems. What You Will LearnWhat are deep neural networks?What is transfer learning, multi-task learning, and end-to-end learning?What are hyperparameters, bias, variance, and data division?What are CNN and RNN?Who This Book Is ForMachine learning engineers, data scientists, AI practitioners, software developers, and engineers interested in deep learning

Read more less

Book SynopsisChapter 1: Introduction.- Chapter 2: Obtaining Data.- Chapter 3: ETL Pipeline.- Chapter 4: Exploratory Data Analysis.- Chapter 5: Machine Learning Models.- Chapter 6: Evaluating Models.- Chapter 7: When To Use Machine Learning Models.- Chapter 8: Where Machine Learning Models Live.- Chapter 9: Telemetry.- Chapter 10: Adversaries and Abuse.- Chapter 11: Working With Models.

Read more less

Book SynopsisPart 1: Foundations and Case Study Introduction.- Chapter 1: NLP Essentials.- Chapter 2: Oracle Cloud for NLP.- Chapter 3: Healthcare NLP Case Study.- Part2: Case Study Implementation.- Chapter 4: Tenancy Preparation.- Chapter 5: Dataset Preparation.- Chapter 6: Model Fine-tuning.- Part 3: Case Study Deployment and Wrap-Up.- Chapter 7: Model Deployment and Monitoring.- Chapter 8: MLOps and Conclusion.

Read more less

Book SynopsisChapter 1: Introduction to MLOps.- Chapter 2: Foundations of MLOps on AWS.- Chapter 3: Operational Excellence in MLOps.- Chapter 4: Security in MLOps.- Chapter 5: Reliability in MLOps.- Chapter 6: Performance Efficiency in MLOps.- Chapter 7: Cost Optimization in MLOps.- Chapter 8 MLOps Best Practices and Case Studies.- Chapter 9: MLOps for GenAI.- Chapter 10: Future Trends in MLOps.

Read more less

Book Synopsis

Read more less

Book SynopsisPart I: Introduction.- Chapter 1: Responsible AI and AI Governance.- Part II: Technical risks (Internal to an organisation).- Chapter 2. Accuracy.- Chapter 3. Robustness and Security.- Chapter 4: Explainability.- Part III: Ethical risks (External).- Chapter 5. Fairness and Human Rights.- Chapter 6: Privacy.- Chapter 7: Sustainability.- Chapter 8: Human-Centered AI.- Part IV: Governance and Case studies.- Chapter 9: Governance Processes.- Chapters 10: Case Study.

Read more less

Book SynopsisChapter 1: Introduction to Generative AI.- Chapter 2: Generative AI with AWS.- Chapter 3: Introduction to Amazon Bedrock.- Chapter 4: Overview of Prompt Engineering and In-Context Learning.- Chapter 5: Overview of Use Cases in this Book.- Chapter 6: Overview of Retrieval-Augmented Generation (RAG).- Chapter 7: Overview of Amazon Bedrock Knowledge Base.- Chapter 8: Overview of Safeguard's Practice.- Chapter 9: Overview of Amazon Bedrock Agents.- Chapter 10: Overview of Model Customization.- Chapter 11: Overview of Model Evaluation.- Chapter 12: Overview of Best Model Selection and Best Practices.- Chapter 13: Overview of Security and Privacy of Amazon Bedrock.- Chapter 14: Overview of GenAIOps.- Chapter 15: Overview of Prompt Management.- Chapter 16: Overview of Prompt Flow. – Chapter 17: Overview of Provisional Throughput. – Chapter 18: Overview of Image Capabilities. – Chapter 19: Overview of Multimodal Capabilities. – Chapter 20: Conclusion.

Read more less

Book SynopsisChapter 1: Conceptual Representations.- Chapter 2: Conceptual Modelling.- Chapter 3: Balance Equation.- Chapter 4: Ratios.- Chapter 5: Time and Behavioral Patterns.-Chapter 6: Additional Variables.- Chapter 7: Things to Know About ABTs.- Chapter 8 The Building Plan and Variable Management.- Chapter 9: Target Population.- Chapter 10: The ABT Building Process.- Chapter 11: A Brief Introduction to the use of SAS® Enterprise MinerTM.- Chapter 12: Partitioning.- Chapter 13: Univariable Analysis.- Chapter 14: Collinearity Analysis.- Chapter 15: Weight of Evidence.- Chapter 16: Multivariable Selection Methods.- Chapter 17: Experimental Design and Hyperoptimization.- Chapter 18: The Main-Effects Model.- Chapter 19: The Scoring Process.- Chapter 20: Closing Thoughts.

Read more less

Book SynopsisChapter 1: LangChain and Python: Basics.- Chapter 2: LangChain and Python: Adanced Components.-  Chapter 3: Building Advanced Applications Powered by LLMs with LangChain and Python.- Chapter 4: Deploying LLM-powered Applications.- Chapter 5: Building and Fine-tuning LLMs.

Read more less

Book SynopsisChapter 1. Natural Language Processing: An Introduction.- Chapter 2. Collecting and Extracting the Data for NLP Projects.- Chapter 3. NLP Data Preprocessing Tasks Involving Strings & Python Regular Expressions.- Chapter 4. NLP Data Preprocessing Tasks with nltk.- Chapter 5. Lexical Analysis.- Chapter 6. Syntactic and Semantic Techniques in NLP.- Chapter 7. Advanced Pragmatic Techniques and Specialized Topics in NLP.- Chapter 8. Transformers, Generative AI, & LangChain.- Chapter 9. Advancing with LangChain & OpenAI.- Chapter 10. Case Study on Symantec Analysis.

Read more less

Book Synopsis1. Megabrains 101: Generative AI & LLMs Unboxed.- 2. First Contact: “Hello, LLM” with Spring Boot.- 3. The Transformer Saga—From Attention to Fine-Tuning.- 4. Bring Your Own Model: Self-Hosting with Ollama.- 5. Power Tools: LangChain4j Quick-Start.- 6. Integrating LLMs with Java Applications.- 7. From Chatty to Clever: Retrieval-Augmented Generation.- 8. Spring AI Ninja Moves.- 9. Prompt Alchemy: Patterns that Make Models Look Smarter.- 10. Swiss-Army LLMs: Tool Calls in Spring AI.- 11. Agents Assemble! Building Autonomous Workflows.- 12. Quarkus + LangChain4j: Lightning-Fast Gen AI.- 13. Jlama & Friends: Hosting Models the Java Way.- 14. Seeing Is Believing: Multimodal LLMs & Image Hacking.- 15. Does It Even Work? Testing & Evaluating LLM Apps.- 16. Cloud Power-Ups—Bedrock, Vertex & Azure OpenAI.- 17. Talking in Protocols: The MCP Revolution.- 18. Can You See Me Now? Observability for LLM Pipelines.- 19. Native-Speed Machine Learning in Java: DJL, ONNX & JNI.- 20. Architectures of Tomorrow: From Monoliths to Modular Minds.

Read more less

Book SynopsisChapter 1: Introduction to Large Language Models in Finance.- Chapter 2: Infrastructure Setup for LLMs.- Chapter 3: Training and Fine-Tuning LLMs.- Chapter 4: Deployment Strategies for LLMs.- Chapter 5: Ensuring Data Privacy and Security.- Chapter 6: Integrating LLMs into Financial Systems.- Chapter 7: Monitoring and Maintenance of LLMs.- Chapter 8: Future Trends in LLM Ops for Finance.

Read more less

Book SynopsisChapter 1: Introduction to LangChain.- Chapter 2: Core Components of LangChain.- Chapter 3: Advanced Components and Integrations.- Chapter 4: Building Chatbots Using LangChain.- Chapter 5: Building Retrieval-Augmented Generation (RAG) Systems with LangChain.- hapter 6: Advanced Techniques with LangServe and LangSmith.- Chapter 7: LangChain and NLP: Enhancing Language Understanding.- Chapter 8: Building AI Agents with LangChain.- Chapter 9: LangChain Framework Integrations.- Chapter 10: Deploying LangChain Applications.- Chapter 11: Best Practices and Practical Aspects.

Read more less

Book Synopsis1. Introduction to Generative AI and R.- 2. Setting up your R Environment for Generative AI.- 3. Fundamentals of Generative AI .- 4. Implementing Basic Generative Models in R.- 5. Generating Synthetic Data with R.- 6. Advanced Generative Models and Techniques.- 7. Generative AI for Predictive Modeling.- 8. Creative Applications of Generative AI in R.- 9. Ethical Considerations and Future Directions.

Read more less

Book SynopsisPART I: Machine Learning for Forecasting.- Chapter 1: Models for Forecasting.- Chapter 2: Model Evaluation for Forecasting.- Chapter 3: Model Management and Benchmarking using MLflow.- PART II: Univariate Time Series Models.- Chapter 4: The AR model.- Chapter 5: The MA model.- Chapter 6: The ARMA model.- Chapter 7: The ARIMA model.- Chapter 8: The SARIMA model.- PART III: Multivariate Time Series Models.- Chapter 9: The SARIMAX model.- Chapter 10: The VAR model.- Chapter 11: The VARMAX model.- PART IV: Supervised Models.- Chapter 12: The Linear Regression.- Chapter 13: The Decision Tree Model.- Chapter 14: The kNN model.- Chapter 15: The Random Forest.- Chapter 16: Gradient Boosting with XGBoost, LightGBM, and CatBoost.- Chapter 17: Bayesian Models with pyBATS.- PART V: Neural Networks.- Chapter 18: Neural Networks.- Chapter 19: RNNs using SimpleRNN and GRU.- Chapter 20: LSTM RNNs.- PART VI: Black Box and Cloud Based Models.- Chapter 21: The NBEATS model with Darts.- Chapter 22: The Transformer model with Darts.- Chapter 23: The NeuralProphet model.- Chapter 24: The DeepAR model and AWS Sagemaker AI.- Chapter 25: Uber's Orbit Model.- Chapter 26: AutoML with Microsoft Azure.- Chapter 27: AutoML with Vertex AI on Google Cloud Platform.- Chapter 28: Nixtla Suite and TimeGPT.- Chapter 29: Model Selection.

Read more less