Natural language and machine translation Books

Book SynopsisThis best-selling book uses concrete examples, minimal theory, and production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.

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Book Synopsis Numa Dhamani is a natural-language-processing researcher known for translating complex language science into actionable insight. With years at the intersection of technology and society, Numa brings clarity, curiosity, and social awareness to every page. She distills her expertise into practical guidance that empowers readers to explore AI responsibly.?  Maggie Engler is an engineer and safety researcher working on large language models at a pioneering AI lab. With deep experience in measuring harms and building safeguards, Maggie offers balanced, evidence-based advice wrapped in an empathetic voice. She turns cutting-edge research into usable frameworks that help readers champion trustworthy AI systems.

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Book SynopsisThis accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI-including regression, neural networks, optimization, backpropagation, and Markov chains.

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Book SynopsisDescription Modern NLP techniques based on machine learning radically improve the ability of software to recognize patterns, use context to infer meaning, and accurately discern intent from poorly-structured text. In Natural Language Processing in Action, readers explore carefully chosen examples and expand their machine's knowledge which they can then apply to a range of challenges. Key Features • Easy-to-follow • Clear examples • Hands-on-guide Audience A basic understanding of machine learning and some experience with a modern programming language such as Python, Java, C++, or JavaScript will be helpful. About the technology Natural Language Processing (NLP) is the discipline of teaching computers to read more like people, and readers can see examples of it in everything from chatbots to the speech-recognition software on their phone. Hobson Lane has more than 15 years of experience building autonomous systems that make important decisions on behalf of humans. Hannes Hapke is an Electrical Engineer turned Data Scientist with experience in deep learning. Cole Howard is a carpenter and writer turned Deep Learning expert.

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Book SynopsisThis is an open access title. It is made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 International license. It is available to read and download as a PDF version on the Oxford Academic platform.We live in a world where artificial intelligence and intensive use of personal data has become normalized. Companies across the world are developing and launching technologies to infer and interact with emotions, mental states, and human conditions. However, the methods and means of mediating information about people and their emotional states are incomplete and problematic. Automating Empathy offers a critical exploration of technologies that sense intimate dimensions of human life and the modern ethical questions raised by attempts to perform and simulate empathy. It traces the ascendance of empathic technologies from their origins in physiognomy and pathognomy to the modern day and explores technologies in nations with non-Western ethical histories and appTable of ContentsChapter 1: Automating Empathy SECTION I: THEORY AND ETHICS Chapter 2: Hyperreal Emotion Chapter 3: Assessing the Physiognomic Critique Chapter 4: Hybrid Ethics Chapter 5: The Context Imperative: Extractivism, Japan, and Holism SECTION II: APPLICATIONS AND IMPLICATIONS Chapter 6: Positive Education Chapter 7: Automating Vulnerability: Sensing Interiors Chapter 8: Hybrid Work: Automated for the People? Chapter 9: Waveforms of Human Intention: Towards Everyday Neurophenomenology Chapter 10: Selling Emotions: Moral Limits of Intimate Data Markets Chapter 11: Uncertainty for Good: Inverting Automated Empathy References

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Book SynopsisHumans do a great job of reading text, identifying key ideas, summarizing, making connections, and other tasks that require comprehension and context. Recent advances in deep learning make it possible for computer systems to achieve similar results. Deep Learning for Natural Language Processing teaches you to apply deep learning methods to natural language processing (NLP) to interpret and use text effectively. In this insightful book, NLP expert Stephan Raaijmakers distills his extensive knowledge of the latest state-of-the-art developments in this rapidly emerging field. Key features An overview of NLP and deep learning • Models for textual similarity • Deep memory-based NLP • Semantic role labeling • Sequential NLP Audience For those with intermediate Python skills and general knowledge of NLP. No hands-on experience with Keras or deep learning toolkits is required. About the technology Natural language processing is the science of teaching computers to interpret and process human language. Recently, NLP technology has leapfrogged to exciting new levels with the application of deep learning, a form of neural network-based machine learning Stephan Raaijmakers is a senior scientist at TNO and holds a PhD in machine learning and text analytics. He’s the technical coordinator of two large European Union-funded research security-related projects. He’s currently anticipating an endowed professorship in deep learning and NLP at a major Dutch university.

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Book SynopsisWith this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology.

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Book SynopsisLearn how to use ChatGPT, Bing Chat / Copilot, and Bard to get the most out of these powerful tools. ChatGPT has become a tool hundreds of millions use every day - yet few have mastered the art of sweet-talking these new AI chatbots into providing detailed and accurate responses to user prompts. It's vital for every professional tech user to have an understanding of how AI chatbots work, and how best to put them to work. While the fear is that AI will take people’s jobs, it is more likely to be someone using AI that will! Getting Started with ChatGPT and AI Chatbots explores the ‘big three’ AI chatbots - OpenAI ChatGPT, Microsoft / Windows Copilot and Google Bard - demystifying their operation, and providing a wealth of tools for thinking about how to talk to these smart tools. Whether you're a business user, a tech student, or a curious professional, this book is for you to understand and demystify Large Language Models (LLMs) and generative AI tools, harnessing them to enhance your role.Trade ReviewIn this ever so timely book, one of my favourite technologists Mark Pesce, takes us through and behind the screen to explain how the latest generation of AI chatbots actually work. But this is not a book about chatbots, this is a book about how to use AI-powered chatbots in daily life, and how to use them to their fullest potential. -- Genevieve Bell, The Australian National UniversityThis book is a must-read for anyone considering using generative AI in business. Authored in non-technical language, it walks through the origins of the first chatbot into the current generative AI landscape including ChatGPT, Copilot and Bard. Furthermore, the guidance on effective prompting to achieve the right results, and a list of critical dos and don'ts to protect sensitive data really make this an all-round winner. Top marks BCS, highly recommended! -- Pauline Norstrom, Founder and CEO, Anekanta® Consulting and Anekanta® AIThis book provides practical guidance on the use of a rapidly growing technologies referred to as AI chatbots, that offer a substantial productivity advantage to those adept at crafting precise prompts and understanding their diverse applications. It is a valuable resource for mastering these emerging tools, ensuring not only competitiveness but also enabling a focus on the creative and compelling facets of one's work. I highly recommend everyone to read this book and glean insights on wielding this potent power proficiently to remain competent. -- Rashik Parmar MBE FBCS, Group CEO, BCS, The Chartered Institute for ITIn late November 2022, ChatGPT introduced a major advance in Artificial Intelligence that surprised almost everyone, including many experts in AI. Trained on text from billions of books and webpages, it can thoughtfully answer questions on a huge variety of topics and languages. The increase in our capability to access human knowledge is analogous to the increase provided by the internet in 1993. For both technologies, getting good answers requires knowing how to ask the right questions. In this timely and important book, Mark Pesce, who has been at the forefront of new digital technologies for over 30 years, provides a compelling and comprehensive introduction to ChatGPT and how to use it. -- Professor Ken Goldberg, William S. Floyd Jr. Distinguished Chair in Engineering, UC BerkeleyEverything you need to educate yourself and your colleagues about the latest hot topic is found within these pages, it’s a must share for colleagues to stimulate conversations about how your business can best adopt AI safely and where you will find business cases. From useful tips on prompts to security concerns about data, biases, and hallucinations with comparisons, get the facts you need on AI here. These are exactly the kinds of conversations I’m having two or three times a day, helping organisations bring their AI aspirations in line with what their business needs are. -- David Starkings, AI Adoption Consultant, tts digital adoption solutionsGetting Started with ChatGPT and AI Chatbots is perfectly targeted to give an introduction to ChatGPT and AI Chatbots, it compares and contrasts a range of the major options and covers how each of the competing Chatbots can answer the same question very differently. It also poses some interesting thoughts around ‘Prompt Engineering’ and how this may become a whole new skillset that people need to learn. I thought that Chapter 6 brought insight to ‘hallucinations’ and how AI chatbots have the ability to sound very confident, even when they are wrong, the importance of fact checking and using human experts is stressed well. -- Richard Parker MBCS, Chair, AELP Sector Forum, IT & DigitalMark's book is a practical and pragmatic guide to contemporary spellcasting - the magic needed to evoke useful, safe and factual results from the emerging field of large language models. Importantly the book provides the framework needed to evaluate LLM's critically and ground them in reality - a must-have resource for explorers in this field. -- Bhautik Joshi, Principal Applied Scientist, CanvaEverybody is talking about AI, and soon it will be integrated into every part of our electronic devices - always on and always available. Getting Started with ChatGPT and AI Chatbots explains in an easy-to-understand way how AI works and how to get the best results from it, safely and securely. -- David Smith MBCS MIET, Lead Business Analyst, Lloyds Banking GroupWith this book, Mark creates a path to a dialog with new and emerging AI platforms, one that I’ll be referring to again and again as these technologies evolve. Ever heard of autonomous agents? You have now. A brilliant, timely, and superbly helpful pathfinder. -- Dr Catherine Ball PhD DSc GAICD, CompIEAust, Associate Professor, The Australian National UniversityAn essential read for those venturing into Generative AI, this book seamlessly blends historical and theoretical insights with practical examples. The book adeptly navigates concerns surrounding Generative AI, making it a valuable resource for students, academics, and professionals alike. -- Professor Lasith Gunawardena FBCS, Department Head of Information Technology, University of Sri Jayewardenepura, Sri LankaIf you’ve only dabbled or toyed with ChatGPT or other generative AI tools, you’ll find Mark Pesce’s book invaluable. The book will educate you about how to use AI chatbots effectively and safely. It covers the basics of various chatbots, the technology behind them, and the concept of 'prompt engineering' to elicit desired responses. It also addresses safety and security concerns and explores advanced techniques like using personas and chain-of-reason prompts. Highly recommended! -- Tim Clements FBCS CITP FIP CIPP/E CIPM CIPT, Business Owner, Purpose and MeansA concise yet thorough take on AI and how to use it to its advantage. Taking us from our place of trust in the output of computers to encourage us think further about what we input, and to question the completion. ‘If you wouldn’t shout it from the rooftop, you shouldn’t type it into a chatbot.’ -- Kym Glover CITP MBCS MAPM, Program Manager, ForgeRock.Not yet started with an AI chatbot? This book is your call-to-action and your how-to rolled into one. A smooth and informative read that'll kickstart your practical learning and give you great ideas to get the best out of Generative AIs. A real confidence builder. -- Bronia Anderson-Kelly, IT Change Consultant, Sabiduria LtdThis book is a fantastic resource for anyone starting out with Gen AI tools. It not only covers the fundamental aspects of the leading Gen AI tools available today, but also the essentials of prompt engineering. With the widespread adoption of Gen AI in the coming years, just like the internet, this book is a useful guide. Mark not only provides a solid foundation in the basics but also delivers valuable insights into the history and potential future of these tools. Highly recommended for its clarity and depth. -- Graeme Vermeulen, Head of Technical Architecture, AdvancedTable of ContentsIntroduction 1. Getting Started 2. How AI Chatbots Work 3. Security and Privacy 4. Simple Prompts 5. Reasoning and Summarising 6. Truthiness and Chatbots 7. Character, Context and Conflict 8. Using Character, Context and Challenge to Craft Powerful Prompts 9. Chain of Thought Prompts 10. Computer Says No 11. Creating Images with Bing Chat 12. Windows Copilot 13. Autonomous Agents 14. Will and AI Chatbot Take My Job 15. What the Future Holds / Next Steps

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Book SynopsisSince the last edition of this book (2014), progress has been astonishing in all areas of Natural Language Processing, with recent achievements in Text Generation that spurred a media interest going beyond the traditional academic circles.

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Book SynopsisTackle a variety of tasks in natural language processing by learning how to use the R language and tidy data principles. This practical guide provides examples and resources to help you get up to speed with dplyr, broom, ggplot2, and other tidy tools from the R ecosystem.

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Book SynopsisTable of ContentsList of Figures xi About the Companion Website xii 1 Introduction 1 1.1 Why Program? Why Python? 1 1.2 Course Overview and Aims 4 1.3 A Brief Note on the Exercises 5 1.4 Conventions Used in this Book 6 1.5 Installing Python 6 1.5.1 Installing on Windows 6 1.5.2 Installing on the Mac 7 1.5.3 Installing on Linux 8 1.6 Introduction to the Command Line/Console/Terminal 8 1.6.1 Activating the Command Line on Windows 9 1.6.2 Activating the Command Line on the Mac or Linux 9 1.7 Editors and IDEs 10 1.8 Installing and Setting Up WingIDE Personal 10 1.9 Discussions 11 2 Programming Basics I 15 2.1 Statements, Functions, and Variables 15 2.2 Data Types – Overview 17 2.3 Simple Data Types 18 2.3.1 Strings 18 2.3.2 Numbers 20 2.3.3 Binary Switches/Values 21 2.4 Operators – Overview 21 2.4.1 String Operators 21 2.4.2 Mathematical Operators 22 2.4.3 Logical Operators 24 2.5 Creating Scripts/Programs 25 2.6 Commenting Your Code 26 2.7 Discussions 28 3 Programming Basics II 33 3.1 Compound Data Types 33 3.2 Lists 35 3.3 Simple Interaction with Programs and Users 37 3.4 Problem Solving and Damage Control 38 3.4.1 Getting Help from Your IDE 38 3.4.2 Using the Debugger 39 3.5 Control Structures 40 3.5.1 Conditional Statements 41 3.5.2 Loops 42 3.5.3 while Loops 43 3.5.4 for Loops 44 3.5.5 Discussions 45 4 Intermediate String Processing 53 4.1 Understanding Strings 53 4.2 Cleaning Up Strings 54 4.3 Working with Sequences 55 4.3.1 Overview 55 4.3.2 Slice Syntax 56 4.4 More on Tuples 57 4.5 ‘Concatenating’ Strings More Efficiently 59 4.6 Formatting Output 60 4.6.1 Using the % Operator 60 4.6.2 The format Method 61 4.6.3 f- Strings 61 4.6.4 Formatting Options 62 4.7 Handling Case 62 4.8 Discussions 63 5 Working with Stored Data 71 5.1 Understanding and Navigating File Systems 71 5.1.1 Showing Folder Contents 72 5.1.2 Navigating and Creating Folders 74 5.1.3 Relative Paths 75 5.2 Stored Data 76 5.3 Opening and Closing Files 76 5.3.1 File Opening Modes 77 5.3.2 File Access Options 77 5.4 Reading File Contents 78 5.5 Error Handling 79 5.6 Writing to Files 82 5.7 Working with Folders and Paths 83 5.7.1 The os Module 83 5.7.2 The Path Object of the libpath Module 84 5.8 Discussions 86 6 Recognising and Working with Language Patterns 93 6.1 The re Module 93 6.2 General Syntax 94 6.3 Understanding and Working with the Match Object 94 6.4 Character Classes 96 6.5 Quantification 97 6.6 Masking and Using Special Characters 98 6.7 Regex Error Handling 98 6.8 Anchors, Groups and Alternation 99 6.9 Constraining Results Further 101 6.10 Compilation Flags 101 6.11 Discussions 102 7 Developing Modular Programs 109 7.1 Modularity 109 7.2 Dictionaries 109 7.3 User- defined Functions 111 7.4 Understanding Modules 112 7.5 Documenting Your Module 115 7.6 Installing External Modules 116 7.7 Classes and Objects 117 7.7.1 Methods 118 7.7.2 Class Schema 118 7.8 Testing Modules 119 7.9 Discussions 120 8 Word Lists, Frequencies and Ordering 129 8.1 Introduction to Word and Frequency Lists 129 8.2 Generating Word Lists 129 8.3 Sorting Basics 130 8.4 Generating Basic Word Frequency Lists 131 8.5 Lambda Functions 132 8.6 Discussions 134 9 Interacting with Data and Users Through GUIs 143 9.1 Graphical User Interfaces 143 9.2 PyQt Basics 144 9.2.1 The General Approach to Designing GUI- based Programs 144 9.2.2 Useful PyQt Widgets 145 9.2.3 A Minimal PyQt Program 146 9.2.4 Deriving from a Main Window 148 9.2.5 Working with Layouts 148 9.2.6 Defining Widgets and Assigning Layouts 150 9.2.7 Widget Properties, Methods and Signals 150 9.2.8 Adding Interactive Functionality 152 9.3 Designing More Advanced GUIs 153 9.3.1 Actions 153 9.3.2 Creating Menus, Tool and Status Bars 153 9.3.3 Working with Files and Folder in PyQt 155 9.4 Discussions 159 10 Web Data and Annotations 171 10.1 Markup Languages 171 10.2 Brief Intro to HTML 172 10.3 Using the urllib.request Module 174 10.4 Extracting Text from Web Pages 177 10.5 List and Dictionary Comprehension 178 10.6 Brief Intro to XML 179 10.7 Complex Regex Replacements Using Functions 182 10.8 Brief Intro to the TEI Scheme 182 10.8.1 The Header 183 10.8.2 The Text Body 184 10.9 Discussions 188 11 Basic Visualisation 201 11.1 Using Matplotlib for Basic Visualisation 201 11.2 Creating Word Clouds 207 11.3 Filtering Frequency Data Through Stop- Words 208 11.4 Working with Relative Frequencies 210 11.5 Comparing Frequency Data Visually 212 11.6 Discussions 216 12 Conclusion 227 Appendix – Program Code 231 Index 273

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Book SynopsisMaster Java persistence using the industry-leading tools Spring Data and Hibernate. In Java Persistence with Spring Data and Hibernate you will learn: Mapping persistent classes, value types, and inheritance Mapping collections and entity associations Processing transactions with Spring Data and Hibernate Creating fetch plans, strategies, and profiles Filtering data Building Spring Data REST projects Using Java persistence with non-relational databases Querying JPA with QueryDSL Testing Java persistence applications Java Persistence with Spring Data and Hibernate teaches you the ins-and-outs of Java persistence with hands-on examples using Spring Data, JPA and Hibernate. The book carefully analyzes the capabilities of the major Java persistence tools, and guides you through the most common use cases. You'll learn how to make and utilize mapping strategies, and efficiently test Java persistence applications. The practical techniques are demonstrated with both relational and non-relational databases. about the technology Persistence enables an application's data to exist for the long term, even after a program is stopped or terminated. Whether you're saving state from session to session or maintaining long-term records, Java persistence tools like Spring Data, JPA, and Hibernate help deliver the object relational mapping that connects code's objects with your database. about the book Java Persistence with Spring Data and Hibernate explores persistence with the most popular available tools. You'll benefit from detailed coverage of Spring Data JPA, Spring Data JDBC, Spring Data REST, JPA, and Hibernate, comparing and contrasting the alternatives so you can pick what's best for your code. Begin with a hands-on introduction to object-relational mapping (ORM), then dive into mapping strategies for linking up objects and your database. You'll learn about the different approach to transactions for both Hibernate and Spring Data, and even how to deliver Java persistence with non-relational databases. Finally, you'll explore testing strategies for persistent applications to keep your code clean and bug free. Trade Review"Want to learn Java persistence without having to dig through the reference documentation? Read it and you'll know what to do (and what to avoid)." Marcus Geselle "This book is crucial not only for newbies but also for any senior developers working with JVM Persistence." Özay Duman "This book gives a great foundation for working with JPA and Hibernate. If I were to teach the subject, I would not hesitate to use this book." Kim Kjærsulf "Excellent introduction to how Java persistence is handled in the real world." Daniel Carl

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Book SynopsisHow does a parser, a device that imposes an analysis on a string of symbols so that they can be interpreted, work? More specifically, how does the parser in the human cognitive mechanism operate? Using a wide range of empirical data concerning human natural language processing, Bradley Pritchett demonstrates that parsing performance depends on grammatical competence, not, as many have thought, on perception, computation, or semantics. Pritchett critiques the major performance-based parsing models to argue that the principles of grammar drive the parser; the parser, furthermore, is the apparatus that tries to enforce the conditions of the grammar at every point in the processing of a sentence. In comparing garden path phenomena, those instances when the parser fails on the first reading of a sentence and must reanalyze it, with occasions when the parser successfully functions the first time around, Pritchett makes a convincing case for a grammar-derived parsing theory.

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Book SynopsisTable of Contents1. Linguistics: Core Concepts and Principles 2. Grammars 3. Open-Source Libraries, Application Frameworks, Workflow Systems, and Other Resources 4. Mathematical Essentials 5. Probability 6. Inference and Prediction Methods 7. Random Processes 8. Bayesian Methods 9. Machine Learning 10. Artificial Neural Networks for Natural Language Processing 11. Information Retrieval 12. Language Core Tasks 1 13. Language Core Tasks 2 14. Language Understanding Applications 1 15. Language Understanding Applications 2 16. Deep Learning for Natural Language Processing 17. Text Mining for Modeling Cyberattacks 18. World Languages and Crosslinguistics 19. Linguistic Elegance of the Languages of South India 20. Current Trends and Open Problems

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Book SynopsisThis Element presents an alternative eye tracking methodology for investigating translators' web search behaviour as well as a systematic approach to gauging the reasoning behind translators' highly complex and context-dependent interaction with search engines and the Web.Table of Contents1. Introduction; 2. Existing studies; 3. Methodology; 4. Findings and discussion; 5. Conclusion; References.

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Book SynopsisAdvancing Natural Language Processing in Educational Assessment examines the use of natural language technology in educational testing, measurement, and assessment. Recent developments in natural language processing (NLP) have enabled large-scale educational applications, though scholars and professionals may lack a shared understanding of the strengths and limitations of NLP in assessment as well as the challenges that testing organizations face in implementation. This first-of-its-kind book provides evidence-based practices for the use of NLP-based approaches to automated text and speech scoring, language proficiency assessment, technology-assisted item generation, gamification, learner feedback, and beyond. Spanning historical context, validity and fairness issues, emerging technologies, and implications for feedback and personalization, these chapters represent the most robust treatment yet about NLP for education measurement researchers, psychometricians, testing profeTable of ContentsPreface by Victoria Yaneva and Matthias von DavierSection I: Automated ScoringChapter 1: The Role of Robust Software in Automated Scoring by Nitin Madnani, Aoife Cahill, and Anastassia LoukinaChapter 2: Psychometric Considerations when Using Deep Learning for Automated Scoring by Susan Lottridge, Chris Ormerod, and Amir JafariChapter 3: Speech Analysis in Assessment by Jared C. Bernstein and Jian ChengChapter 4: Assessment of Clinical Skills: A Case Study in Constructing an NLP-Based Scoring System for Patient Notes by Polina Harik, Janet Mee, Christopher Runyon, and Brian E. ClauserSection II: Item DevelopmentChapter 5: Automatic Generation of Multiple-Choice Test Items from Paragraphs Using Deep Neural Networks by Ruslan Mitkov, Le An Ha, Halyna Maslak, Tharindu Ranasinghe, and Vilelmini SosoniChapter 6: Training Optimus Prime, M.D.: A Case Study of Automated Item Generation using Artificial Intelligence – From Fine-Tuned GPT2 to GPT3 and Beyond by Matthias von DavierChapter 7: Computational Psychometrics for Digital-first Assessments: A Blend of ML and Psychometrics for Item Generation and Scoring by Geoff LaFlair, Kevin Yancey, Burr Settles, Alina A von DavierSection III: Validity and FairnessChapter 8: Validity, Fairness, and Technology-based Assessment by Suzanne LaneChapter 9: Evaluating Fairness of Automated Scoring in Educational Measurement by Matthew S. Johnson and Daniel F. McCaffreySection IV: Emerging TechnologiesChapter 10: Extracting Linguistic Signal from Item Text and Its Application to Modeling Item Characteristics by Victoria Yaneva, Peter Baldwin, Le An Ha, and Christopher RunyonChapter 11: Stealth Literacy Assessment: Leveraging Games and NLP in iSTART by Ying Fang, Laura K. Allen, Rod D. Roscoe, and Danielle S. McNamaraChapter 12: Measuring Scientific Understanding Across International Samples: The Promise of Machine Translation and NLP-based Machine Learning Technologies by Minsu Ha and Ross H. NehmChapter 13: Making Sense of College Students’ Writing Achievement and Retention with Automated Writing Evaluation by Jill Burstein, Daniel McCaffrey, Steven Holtzman & Beata Beigman KlebanovContributor Biographies

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Book SynopsisLearn to build cost-effective apps using Large Language Models In Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications, Principal Data Scientist at Amazon Web Services, Shreyas Subramanian, delivers a practical guide for developers and data scientists who wish to build and deploy cost-effective large language model (LLM)-based solutions. In the book, you''ll find coverage of a wide range of key topics, including how to select a model, pre- and post-processing of data, prompt engineering, and instruction fine tuning. The author sheds light on techniques for optimizing inference, like model quantization and pruning, as well as different and affordable architectures for typical generative AI (GenAI) applications, including search systems, agent assists, and autonomous agents. You''ll also find: Effective strategies to address the challenge of the high computational cost associated with LLMs Assist

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Book SynopsisPerspectives on Artificial Intelligence in Times of Turbulence: Theoretical Background to Applications offers a comprehensive exploration of the intricate relationship between artificial intelligence (AI) and the ever-changing landscape of our society. The book defines AI as machines capable of performing tasks that were once exclusive to human cognition. However, it emphasizes the current limitations of AI, dispelling the notion of sophisticated cyborgs depicted in popular culture. These machines lack self-awareness, struggle with understanding context—especially in language—and are constrained by historical data and predefined parameters. This distinction sets the stage for examining AI's impact on the job market and the evolving roles of humans and machines. Rather than portraying AI as a threat, this book highlights the symbiotic relationship between humans and machines. It recognizes that while certain jobs may become obsolete, new opportunities will emerge. The unique abilities of human beings—such as relational skills, emotional intelligence, adaptability, and understanding of differences—will continue to be indispensable in a rapidly transforming society. The book further explores key objectives and strategies for organizations navigating the AI-driven landscape. From maintaining focus on strategic goals to adapting to new productivity paradigms, from fostering effective communication to promoting feedback and continuous improvement, the chapters provide practical insights and methodologies for managing change and harnessing AI's potential. Its perspectives cover a wide range of topics such as business sustainability, change management, cybersecurity, digital economy and transformation, information systems management, management models and tools, and continuous improvement are comprehensively addressed. Additionally, the book delves into healthcare, telemedicine, Health 4.0, privacy and security, knowledge management, learning, and presents real-world case studies. Designed for researchers and professionals seeking to enhance their knowledge and research capabilities, this book offers a consistent theoretical and practical foundation. It serves as a springboard for further studies, supports change management initiatives within organizations, and facilitates knowledge sharing among experts. This book is an essential companion for colleges with master's and Ph.D. degree investigators, and researchers across a wide range of disciplines.

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Book SynopsisIn the global research community, English has become the main language of scholarly publishing in many disciplines. At the same time, online machine translation systems have become increasingly easy to access and use. Is this a researcher’s match made in heaven, or the road to publication perdition? Here Lynne Bowker and Jairo Buitrago Ciro introduce the concept of machine translation literacy, a new kind of literacy for scholars and librarians in the digital age. For scholars, they explain how machine translation works, how it is (or could be) used for scholarly communication, and how both native and non-native English-speakers can write in a translation-friendly way in order to harness its potential. Native English speakers can continue to write in English, but expand the global reach of their research by making it easier for their peers around the world to access and understand their works, while non-native English speakers can write in their mother tongues, but leverage machine translation technology to help them produce draft publications in English. For academic librarians, the authors provide a framework for supporting researchers in all disciplines as they grapple with producing translation-friendly texts and using machine translation for scholarly communication—a form of support that will only become more important as campuses become increasingly international and as universities continue to strive to excel on the global stage. Machine Translation and Global Research is a must-read for scientists, researchers, students, and librarians eager to maximize the global reach and impact of any form of scholarly work.Trade ReviewBowker and Ciro describe ways that machine translation is used in the context of scholarly communication, and suggest how to use it more effectively. Getting an online machine translation is easy, they say, but it is often more complex to make critical and effective use of a machine translation as part of the scholarly communication process. They cover scholarly communication, machine translation, expanding the reach of knowledge through translation-friendly writing, some wider implications of using machine translation for scholarly communication, and towards a framework for machine translation literacy. -- Annotation ©2019 * (protoview.com) *Table of ContentsIntroduction Chapter 1. Scholarly Communication Chapter 2. Machine Translation Chapter 3. Expanding the Reach of Knowledge through Translation-Friendly Writing Chapter 4. Some Wider Implications of Using Machine Translation for Scholarly Communication Chapter 5. Towards a Framework for Machine Translation Literacy

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Book SynopsisDiscover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R librariesKey Features Implement deep learning algorithms to build AI models with the help of tips and tricks Understand how deep learning models operate using expert techniques Apply reinforcement learning, computer vision, GANs, and NLP using a range of datasets Book DescriptionDeep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them.This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples.What you will learn Learn how to create binary and multi-class deep neural network models Implement GANs for generating new images Create autoencoder neural networks for image dimension reduction, image de-noising and image correction Implement deep neural networks for performing efficient text classification Learn to define a recurrent convolutional network model for classification in Keras Explore best practices and tips for performance optimization of various deep learning models Who this book is forThis book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to develop their skills and knowledge to implement deep learning techniques and algorithms using the power of R. A solid understanding of machine learning and working knowledge of the R programming language are required.Table of ContentsTable of Contents Revisiting Deep Learning architecture and techniques Deep Neural Networks for multiclass classification Deep Neural Networks for regression Image classification and recognition Image classification using convolutional neural networks Applying Autoencoder neural networks using Keras Image classification for small data using transfer learning Creating new images using generative adversarial networks Deep network for text classification Text classification using recurrent neural networks Text classification using Long Short-Term Memory Network Text classification using convolutional recurrent networks Tips, tricks and the road ahead

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisThis book contains a comprehensive treatment of advanced LaTeX features. The focus is on the development of high quality documents and presentations, by revealing powerful insights into the LaTeX language. The well-established advantages of the typesetting system LaTeX are the preparation and publication of platform-independent high-quality documents and automatic numbering and cross-referencing of illustrations or references. These can be extended beyond the typical applications, by creating highly dynamic electronic documents. This is commonly performed in connection with the portable document format (PDF), as well as other programming tools which allow the development of extremely flexible electronic documents.Trade Review“This book can serve as a guide for long-term users of the language in any discipline. Along with online resources, any professor or researcher could benefit from having this book in their library as a reference guide when writing and editing a lengthy work, like a book or an article collection, or when preparing presentations and posters. … Finally, teachers and instructors, whether in higher or secondary education, will find useful information for preparing consistent exam tests and textbooks.” (Lazaros Moysis, zbMATH 1491.68005, 2022)Table of ContentsIntroduction: The Basics.- Advanced Formatting.- Floating Objects.- Presentations.- Exams (Tests, Quizzes).- E-Learning: Blended Learning and Flipped Classroom Support.

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Book Synopsis​This book constitutes the refereed proceedings of the 11th Conference on Artificial Intelligence and Natural Language, AINL 2022, held in St. Petersburg, Russia, in April 2022. The 8 revised full papers and 1 short paper were carefully reviewed and selected from 20 submissions. The volume presents recent research in areas of of text mining, speech technologies, dialogue systems, information retrieval, machine learning, articial intelligence, and robotics. Table of ContentsInferring image background from text description.- Topical Extractive Summarization.- The Semantic Shifts of the Topical Structure in the Corpus of Lentach News Posts.- Development of folklore motif classifie using limited data.- Morphological and Emotional Features of the Speech in Children with Typical Development, Autism Spectrum Disorders and Down Syndrome.- WikiMulti: a Corpus for Cross-Lingual Summarization.- Rethinking Crowd Sourcing for Semantic Similarity.- Interplay of Visual and Acoustic Cues of Irony Perception: a Case Study of Actor’s Speech.- Findings of Biomedical Russian to English Machine Translation Competition.- Translation of medical texts with ensembling and knowledge distillation.

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Book SynopsisIntroduction.- AQG System Architectures.- Generating Questions from Ontologies and Knowledge Graphs.- Use Cases.- Advances in AQG for Training Automatic Question Answering (QA) Systems.- Evaluation.- Content Selection and Question Focusing.- Related and Future Research Directions.

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Book SynopsisFrom proposal to examination, producing a dissertation or thesis is a challenge. Grounded in decades of experience with research training and supervision, this fully updated and revised edition takes an integrated, down-to-earth approach drawing on case studies and examples to guide you step-by-step towards productive success.Early chapters frame the tasks ahead and show you how to get started. From there, practical advice and illustrations take you through the elements of formulating research questions, working with software, and purposeful writing of each of the different kinds of chapters, and finishes with a focus on revision, dissemination and deadlines. How to Write a Better Thesis presents a cohesive approach to research that will help you succeed. Trade ReviewFrom the book reviews:“After reading the book, you are left with no doubt as to what is required to write a thesis, as well as how to undertake the task using a systematic approach. … It should be mandatory reading for all postgraduate students embarking on a master’s degree or higher academic qualification. I highly recommend it.” (S. M. Godwin, Computing Reviews, August, 2014)Table of ContentsWhat is a Thesis?.- Thesis Structure.- Mechanics of Writing.- Making a Strong Start.- The Introductory Chapter.- Background Chapters.- Establishing Your Contribution.- Outcomes and Results.- The Discussion or Interpretation.- The Conclusion.- Before You Submit.- Beyond the Thesis.

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Book SynopsisFor millennia humans have used visible marks to communicate information. Modern examples of conventional graphical symbols include written language, and non-linguistic symbol systems such as mathematical symbology or traffic signs. The latter kinds of symbols convey information without reference to language. This book presents the first systematic study of graphical symbol systems, including a history of graphical symbols from the Paleolithic onwards, a taxonomy of non-linguistic systems – systems that are not tied to spoken language – and a survey of more than 25 such systems. One important feature of many non-linguistic systems is that, as in written language, symbols may be combined into complex “messages” if the information the system represents is itself complex. To illustrate, the author presents an in-depth comparison of two systems that had very similar functions, but very different structure: European heraldry and Japanese kamon. Writing first appeared in Mesopotamia about 5,000 years ago and is believed to have evolved from a previous non-linguistic accounting system. The exact mechanism is unknown, but crucial was the discovery that symbols can represent the sounds of words, not just the meanings. The book presents a novel neurologically-inspired hypothesis that writing evolved in an institutional context in which symbols were “dictated”, thus driving an association between symbol and sound, and provides a computational simulation to support this hypothesis. The author further discusses some common fallacies about writing and non-linguistic systems, and how these relate to widely cited claims about statistical “evidence” for one or another system being writing. The book ends with some thoughts about the future of graphical symbol systems. The intended audience includes students, researchers, lecturers, professionals and scientists from fields like Natural Language Processing, Machine Learning, Archaeology and Semiotics, as well as general readers interested in language and/or writing systems and symbol systems.Trade Review“The book is the first systematic study of graphical symbol systems, ranging from the imagery found in Paleolithic cave paintings, through ancient and contemporary writing systems employing both phonetic and logographic symbols, to modern language-independent symbols such as meteorological icons and emoji.” (Andrew Robinson, Science, science.org, Vol. 382 (6669), October 27, 2023)Table of ContentsPreface1 Introduction 1.1 What’s in a Symbol? 1.2 Syntax 1.3 What this book is about 2 Semiotics 2.1 Introduction 2.2 The Field of Semiotics 2.3 Iconicity 2.4 Syntax 2.5 Articulation 3 Taxonomy 3.1 Introduction 3.2 History 3.3 Preliminary Taxonomy 3.4 Examples of systems 3.5 Kamon/Heraldry 3.5.1 Kamon 3.5.2 British heraldry 3.5.3 Structural Differences: Summary 3.A Symbol system survey (A detailed analysis of 26 symbol systems) 3.B Statistics of kamon 4 Writing Systems 4.1 Introduction 4.2 Writing 4.2.1 Preliminaries 4.2.2 Types of Writing Systems 4.2.3 Blissymbolics 4.3 Limitations of writing 4.3.1 Inclusiveness 4.3.2 Graphocentrism 4.3.3 Summary 4.4 Writing: A summary 5 Symbols in the Brain 5.1 Brain areas 5.2 Meaning in the brain 5.3 Reading in the brain 5.3.1 The letterbox 5.3.2 Summary: the evolution of the letterbox 5.4 Non-linguistic symbols in the brain 5.5 A Hypothesis 6 The Evolution of Writing 6.1 Evolution 6.2 A Hypothesis 6.3 Schools 7 Simulations 7.1 Prior work 7.2 Simulation 7.2.1 Description of the model 7.2.2 Simulation of evolution 7.2.3 Summary and discussion 7.3 Pre-writing 7.4 Summary 7.A Details 7.A.1 Data Generation 7.A.2 Model 7.B Compounds 7.B.1 Monosyllabic cases 7.B.2 Sesquisyllabic cases 7.B.3 Disyllabic cases 8 Misrepresentations 8.1 Introduction 8.2 What does it mean to say something "Looks like writing"? 8.3 Statistics 8.3.1 Statistical analysis of the Indus Valley inscriptions 8.3.2 More on structure in the Indus inscriptions 8.3.3 Variations of distributions of symbols 8.4 Summary 9 The Future 9.1 The Dream of a Universal Written Language 9.2 Semasiography 9.3 The Prestige of Writing 9.4 Final Thoughts

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Book SynopsisIf you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library.

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Book SynopsisThis book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition and domain adaptation. This useful toolkit enables readers to apply machine learning techniques to address practical issues, such as robustness under adverse acoustic environments and domain mismatch, when deploying speaker recognition systems. Presenting state-of-the-art machine learning techniques for speaker recognition and featuring a range of probabilistic models, learning algorithms, case studies, and new trends and directions for speaker recognition based on modern machine learning and deep learning, this is the perfect resource for graduates, researchers, practitioners and engineers in electrical engineering, computer science and applied mathematics.Trade Review'There is a need for an accessible textbook to help newcomers to enter the field [of automatic speaker recognition]. Machine Learning for Speaker Recognition by Man-Wai Mak and Jen-Tzung Chien serves such a need. Both authors are highly seasoned in the field. They cover both fundamental techniques and state-of-the-art methods at an accessible level using the language of modern probabilistic machine learning. The authors cover different components of speaker recognition systems including feature extraction, back-end modeling and scoring, along with various case studies. The book is well suited for the needs of graduate students and researchers in electrical engineering and computer science, along with practitioners. Apart from basic prerequisites in calculus, linear algebra, probabilities and statistics, the textbook provides a coherent and self-contained journey into what modern automatic speaker recognition is about.' Tomi Kinnunen, University of Eastern Finland'The topical coverage is spot-on, and the text discusses many key algorithms that support statistical learning approaches, including hybrid models, deep learning classification, and generative methods. In addition, the authors provide a deep mathematical exploration into versions of algorithms, optimization approaches, and domain adaptation statistics within the context of signal processing. The extensive diagrams, linear algebra notation, and mathematical calculus machinery will support developers who are building new implementations or need to look under the hood of existing systems. Highly Recommended.' J. Brzezinski, ChoiceTable of ContentsPart I. Fundamental Theories: 1. Introduction; 2. Learning algorithms; 3. Machine learning models; Part II. Advanced Studies: 4. Deep learning models; 5. Robust speaker verification; 6. Domain adaptation; 7. Dimension reduction and data augmentation; 8. Future direction; Index.

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Book SynopsisWhen viewed through a political lens, the act of defining terms in natural language arguably transforms knowledge into values. This unique volume explores how corporate, military, academic, and professional values shaped efforts to define computer terminology and establish an information engineering profession as a precursor to what would become computer science. As the Cold War heated up, U.S. federal agencies increasingly funded university researchers and labs to develop technologies, like the computer, that would ensure that the U.S. maintained economic prosperity and military dominance over the Soviet Union. At the same time, private corporations saw opportunities for partnering with university labs and military agencies to generate profits as they strengthened their business positions in civilian sectors. They needed a common vocabulary and principles of streamlined communication to underpin the technology development that would ensure national prosperity and military dominance. investigates how language standardization contributed to the professionalization of computer science as separate from mathematics, electrical engineering, and physics examines traditions of language standardization in earlier eras of rapid technology development around electricity and radio highlights the importance of the analogy of “the computer is like a human” to early explanations of computer design and logic traces design and development of electronic computers within political and economic contexts foregrounds the importance of human relationships in decisions about computer design This in-depth humanistic study argues for the importance of natural language in shaping what people come to think of as possible and impossible relationships between computers and humans. The work is a key reference in the history of technology and serves as a source textbook on the human-level history of computing. In addition, it addresses those with interests in sociolinguistic questions around technology studies, as well as technology development at the nexus of politics, business, and human relations.Table of ContentsChapter 1 Introduction Chapter 2 From Hot War to Cold PeaceChapter 3 Who Will Control Atomic PowerChapter 4 Sharing Information (or Not) for Computer DevelopmentChapter 5 Defining Relationships among Computers, People, and InformationChapter 6 Technology Development Strains Standardization of Human Communication Chapter 7 Defining Terms and Establishing PrioritiesChapter 8 Establishing the Field of Computer Science

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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