AI: Teach me How to Write a Book - Second Edition
AI: Teach me How to Write a Book - Second Edition

Author: John Nunez

Publisher: John Nunez

Published: 2024-04-12

Total Pages: 631

ISBN-13:

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"AI: Teach Me How to Write a Book - 2nd Edition" is a comprehensive guide designed for writers at all levels to harness the capabilities of Artificial Intelligence in creative writing. This book offers a deep dive into the integration of AI tools with traditional writing practices, aimed at enhancing creativity, improving narrative structure, and optimizing the writing process across various genres. Key Features AI Tools and Techniques: The book introduces readers to a variety of AI tools that can assist in plot generation, character development, and emotional depth. It discusses how these tools can serve as co-creators, helping you, the writer, to generate ideas, overcome creative blocks, and refine their narratives. Practical Application: Each chapter is structured to provide actionable advice on how to apply AI in real-world writing scenarios. This includes prompts, how-to guides, and step-by-step instructions on getting AI to collaborate in everything from drafting dialogues to world-building. Genre-Specific Writing Assistance: The content is tailored to address the specific needs of different genres, including science fiction, fantasy, romance, and historical fiction, ensuring that the guidance is relevant and applicable to a your specific field. Balancing AI and Human Creativity: A significant focus is placed on maintaining your voice in control and ensuring that AI complements rather than overrides the human creative process. This is crucial if you are concern about the authenticity and originality of your work. Ethical Considerations: The book also explores the ethical implications of using AI in writing, discussing topics like authorship, originality, and the responsible use of AI tools. Structure The publication, 600+ long, is divided into several key sections, each focusing on different aspects of AI-assisted writing: Introduction to AI in Writing: This part covers the basics of AI technologies and sets the stage for their application in creative writing. Developing Characters and Plot with AI: Detailed chapters discuss how AI can aid in developing complex characters and intricate plots, with tools for emotional analysis and dynamic storytelling. Enhancing Dialogue and Narrative: The book offers strategies for using AI to craft realistic dialogue and maintain narrative coherence, providing examples of how AI can enhance narrative depth and reader engagement. World-Building: Extensive guidelines on using AI to create vivid, immersive worlds, especially in genres like fantasy and science fiction where detailed world-building is pivotal. Specialized Applications: Separate areas of the book address the use of AI in specific genres, providing tailored advice for crafting genre-specific narratives and character archetypes. Practical Exercises and Prompts: Throughout the book, readers are encouraged to engage with practical exercises and AI-generated prompts to practice the skills discussed. In few words The publication concludes with a look at the future of AI in writing, discussing upcoming trends and how you can stay ahead of the curve. It emphasizes continuous learning and adaptation, encouraging us to evolve with technology while staying true to their creative vision. "AI: Teach Me How to Write a Book - 2nd Edition" is ideal for aspiring and experienced writers like you, interested in integrating technology into their creative process. It is also useful for educators and students in creative writing courses seeking to understand the intersection of technology and literature. This detailed guide combines theoretical insights with practical advice, making it a valuable resource for anyone looking to explore the possibilities of AI in enhancing the art of writing.

Artificial Intelligence By Example
Artificial Intelligence By Example

Author: Denis Rothman

Publisher: Packt Publishing Ltd

Published: 2020-02-28

Total Pages: 579

ISBN-13: 1839212810

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Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examples Key FeaturesAI-based examples to guide you in designing and implementing machine intelligenceBuild machine intelligence from scratch using artificial intelligence examplesDevelop machine intelligence from scratch using real artificial intelligenceBook Description AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing. By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions. What you will learnApply k-nearest neighbors (KNN) to language translations and explore the opportunities in Google TranslateUnderstand chained algorithms combining unsupervised learning with decision treesSolve the XOR problem with feedforward neural networks (FNN) and build its architecture to represent a data flow graphLearn about meta learning models with hybrid neural networksCreate a chatbot and optimize its emotional intelligence deficiencies with tools such as Small Talk and data loggingBuilding conversational user interfaces (CUI) for chatbotsWriting genetic algorithms that optimize deep learning neural networksBuild quantum computing circuitsWho this book is for Developers and those interested in AI, who want to understand the fundamentals of Artificial Intelligence and implement them practically. Prior experience with Python programming and statistical knowledge is essential to make the most out of this book.

Aum Golly: Poems on Humanity by an Artificial Intelligence
Aum Golly: Poems on Humanity by an Artificial Intelligence

Author: Gpt- Ai

Publisher: Kertojan Aani

Published: 2021-10-09

Total Pages: 74

ISBN-13: 9789527397237

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What does AI know about love, happiness and making a difference? Aum Golly is a book of poems written in 24 hours. It was made possible by GPT-3 - an advanced autoregressive language model published in 2020 by OpenAI. "... a collection that surprises with humor and delicateness..." - Goodreads review "... I have to say reading it was a pleasure..." - Finnish radio host Ruben Stiller on Yle "... a beautiful dialogue between man and machine..." - a review of the Finnish audiobook The deep learning model can generate text that is virtually indistinguishable from text written by humans: poems, recipes, summaries, legal text and even pieces of code. GPT-3 is autofill on steroids. Good poetry makes us feel something and see the world differently. Despite the gut reaction some of us may have towards AI-enhanced creativity, Aum Golly is a book like any other. You will love some of the poems. You will hate others. Some will make you wonder, but all of them will make you think. Award-winning writer and TEDx speaker Jukka Aalho has guided the AI and chosen the poems for the collection.

Artificial Intelligence
Artificial Intelligence

Author: David L. Poole

Publisher: Cambridge University Press

Published: 2017-09-25

Total Pages: 821

ISBN-13: 110719539X

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Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains.

Learn AI-Assisted Python Programming, Second Edition
Learn AI-Assisted Python Programming, Second Edition

Author: Leo Porter

Publisher: Simon and Schuster

Published: 2024-10-29

Total Pages: 334

ISBN-13: 1633435997

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See how an AI assistant can bring your ideas to life immediately! Once, to be a programmer you had to write every line of code yourself. Now tools like GitHub Copilot can instantly generate working programs based on your description in plain English. An instant bestseller, Learn AI-Assisted Python Programming has taught thousands of aspiring programmers how to write Python the easy way--with the help of AI. It's perfect for beginners, or anyone who's struggled with the steep learning curve of traditional programming. In Learn AI-Assisted Python Programming, Second Edition you'll learn how to: - Write fun and useful Python applications--no programming experience required! - Use the GitHub Copilot AI coding assistant to create Python programs - Write prompts that tell Copilot exactly what to do - Read Python code and understand what it does - Test your programs to make sure they work the way you want them to - Fix code with prompt engineering or human tweaks - Apply Python creatively to help out on the job AI moves fast, and so the new edition of Learn AI-Assisted Python Programming, Second Edition is fully updated to take advantage of the latest models and AI coding tools. Written by two esteemed computer science university professors, it teaches you everything you need to start programming Python in an AI-first world. You'll learn skills you can use to create working apps for data analysis, automating tedious tasks, and even video games. Plus, in this new edition, you'll find groundbreaking techniques for breaking down big software projects into smaller tasks AI can easily achieve. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology AI has changed the way we write computer programs forever. You describe in plain English what you want your program to do, and AI coding assistants like Github Copilot can generate the code for you instantly! If you can use a web browser and move files around on your computer, you can create useful software. This book shows you how. About the book Learn AI-Assisted Python Programming, Second Edition teaches you how to create your own games, tools, and other simple applications using Copilot and the user-friendly Python language. You'll be amazed how quickly you can go from an idea to a working program! Authors Leo Porter and Dan Zingaro guide you step by step as you go from creating simple functions, like a small program that tells you if a password is strong enough, to writing games and tools that help you automate tedious tasks. As you go, you'll learn just enough Python to understand and improve what Copilot creates. About the reader No experience required! About the author Dr. Leo Porter is a Teaching Professor at UC San Diego. Dr. Daniel Zingaro is an Associate Teaching Professor at the University of Toronto. The technical editor on this book was Peter Morgan.

Artificial Intelligence
Artificial Intelligence

Author: Richard E. Neapolitan

Publisher: CRC Press

Published: 2018-03-12

Total Pages: 481

ISBN-13: 1351384392

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The first edition of this popular textbook, Contemporary Artificial Intelligence, provided an accessible and student friendly introduction to AI. This fully revised and expanded update, Artificial Intelligence: With an Introduction to Machine Learning, Second Edition, retains the same accessibility and problem-solving approach, while providing new material and methods. The book is divided into five sections that focus on the most useful techniques that have emerged from AI. The first section of the book covers logic-based methods, while the second section focuses on probability-based methods. Emergent intelligence is featured in the third section and explores evolutionary computation and methods based on swarm intelligence. The newest section comes next and provides a detailed overview of neural networks and deep learning. The final section of the book focuses on natural language understanding. Suitable for undergraduate and beginning graduate students, this class-tested textbook provides students and other readers with key AI methods and algorithms for solving challenging problems involving systems that behave intelligently in specialized domains such as medical and software diagnostics, financial decision making, speech and text recognition, genetic analysis, and more.

Lifelong Machine Learning, Second Edition
Lifelong Machine Learning, Second Edition

Author: Zhiyuan Sun

Publisher: Springer Nature

Published: 2022-06-01

Total Pages: 187

ISBN-13: 3031015819

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Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.

Reinforcement Learning, second edition
Reinforcement Learning, second edition

Author: Richard S. Sutton

Publisher: MIT Press

Published: 2018-11-13

Total Pages: 549

ISBN-13: 0262352702

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The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Data Mining
Data Mining

Author: Ian H. Witten

Publisher: Elsevier

Published: 2011-02-03

Total Pages: 665

ISBN-13: 0080890369

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Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. - Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects - Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods - Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Deep Learning for Coders with fastai and PyTorch
Deep Learning for Coders with fastai and PyTorch

Author: Jeremy Howard

Publisher: O'Reilly Media

Published: 2020-06-29

Total Pages: 624

ISBN-13: 1492045497

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Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala