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The ChatGPT Complete Guide: Learn Midjourney, ChatGPT 4 is a comprehensive guidebook that explores the capabilities, applications, and ethical considerations of ChatGPT and its integration with Midjourney. This guide provides an overview of ChatGPT, including its history, development, and new improvements. It delves into the various features of ChatGPT, such as enhanced language understanding and multi-modal capabilities, and explains how it can be used for Conversational AI, content generation, language translation, customer support, creative writing, and more. The book also emphasizes the importance of ethical use and provides strategies for mitigating biases and ensuring responsible deployment. Additionally, it discusses training and fine-tuning techniques, enterprise integration, security and privacy considerations, industry-specific use cases, and the potential of ChatGPT in various fields.
Design and use generative AI prompts that get helpful and practical results In The Quick Guide to Prompt Engineering, renowned technology futurist, management consultant, and AI thought leader Ian Khan delivers a practical and insightful discussion on taking the first steps in understanding and learning how to use generative AI. In this concise and quick start guide, you will learn how to design and use prompts to get the most out of Large Language Model generative AI applications like ChatGPT, DALL-E, Google’s Bard, and more. In the book, you’ll explore how to understand generative artificial intelligence and how to engineer prompts in a wide variety of industry use cases. You’ll also find thoughtful and illuminating case studies and hands-on exercises, as well as step-by-step guides, to get you up to speed on prompt engineering in no time at all. The book has been written for the non-technical user to take the first steps in the world of generative AI. Along with a helpful glossary of common terms, lists of useful additional reading and resources, and other resources, you’ll get: Explanations of the basics of generative artificial intelligence that help you to learn what’s going on under the hood of ChatGPT and other LLMs Stepwise guides to creating effective, efficient, and ethical prompts that help you get the most utility possible from these exciting new tools Strategies for generating text, images, video, voice, music, and other audio from various publicly available artificial intelligence tools Perfect for anyone with an interest in one of the newest and most practical technological advancements recently released to the public, The Quick Guide to Prompt Engineering is a must-read for tech enthusiasts, marketers, content creators, technical professionals, data experts, and anyone else expected to understand and use generative AI at work or at home. No previous experience is required.
This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.
A Teacher’s Guide to Conversational AI explores the practical role that language-based artificial intelligence tools play in classroom teaching, learning experiences, and student assessment. Today’s educators are well aware that conversational and generative AI—chatbots, intelligent tutoring systems, large language models, and more—represent a complex new factor in teaching and learning. This introductory primer offers comprehensive, novice-friendly guidance into the challenges and opportunities of incorporating AI into K-12 schools and college classes in ways that are appropriate, nourishing to students, and outcomes-driven. Opening with an informative overview of the foundational properties, key terminology, and ethical considerations of these tools, the book offers a coherent and realistic vision of classrooms that are enhanced, rather than stymied, by AI systems. This includes strategies for: · designing assessments that are conducive to students’ beneficial use of AI while mitigating overreliance or dishonesty; · using AI to generate lesson examples for student critique or custom content that reinforces course principles; · leveraging chatbots as a co-instructor or a tutor, a guide during student-driven learning, a virtual debate or brainstorming partner, and a design project; and · creating course content, lesson plans and activities, expanded language and accessibility options, and beyond. Through the depth of understanding and applied approach provided in these chapters, teachers and leaders in training and in service, alongside private tutors, college instructors, and other educators, will be better prepared to future-proof their efforts to serve new generations of learners.
Is your brilliant screenplay gathering digital dust on your hard drive? It’s time to give it new life as a novel! In How to Turn Your Screenplay Into a Novel, you’ll discover the step-by-step process to adapt your script into a riveting book. From expanding dialogue and action to crafting captivating prose to navigating indie publishing, this comprehensive guide will show you how to transform your screenplay into a novel that attracts readers and makes money. Don’t let your story remain untold; turn it into a novel today. Give your screenplay a second chance at success and start earning money as an indie author!
Understand how adversarial attacks work against predictive and generative AI, and learn how to safeguard AI and LLM projects with practical examples leveraging OWASP, MITRE, and NIST Key Features Understand the connection between AI and security by learning about adversarial AI attacks Discover the latest security challenges in adversarial AI by examining GenAI, deepfakes, and LLMs Implement secure-by-design methods and threat modeling, using standards and MLSecOps to safeguard AI systems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionAdversarial attacks trick AI systems with malicious data, creating new security risks by exploiting how AI learns. This challenges cybersecurity as it forces us to defend against a whole new kind of threat. This book demystifies adversarial attacks and equips cybersecurity professionals with the skills to secure AI technologies, moving beyond research hype or business-as-usual strategies. The strategy-based book is a comprehensive guide to AI security, presenting a structured approach with practical examples to identify and counter adversarial attacks. This book goes beyond a random selection of threats and consolidates recent research and industry standards, incorporating taxonomies from MITRE, NIST, and OWASP. Next, a dedicated section introduces a secure-by-design AI strategy with threat modeling to demonstrate risk-based defenses and strategies, focusing on integrating MLSecOps and LLMOps into security systems. To gain deeper insights, you’ll cover examples of incorporating CI, MLOps, and security controls, including open-access LLMs and ML SBOMs. Based on the classic NIST pillars, the book provides a blueprint for maturing enterprise AI security, discussing the role of AI security in safety and ethics as part of Trustworthy AI. By the end of this book, you’ll be able to develop, deploy, and secure AI systems effectively.What you will learn Understand poisoning, evasion, and privacy attacks and how to mitigate them Discover how GANs can be used for attacks and deepfakes Explore how LLMs change security, prompt injections, and data exposure Master techniques to poison LLMs with RAG, embeddings, and fine-tuning Explore supply-chain threats and the challenges of open-access LLMs Implement MLSecOps with CIs, MLOps, and SBOMs Who this book is for This book tackles AI security from both angles - offense and defense. AI builders (developers and engineers) will learn how to create secure systems, while cybersecurity professionals, such as security architects, analysts, engineers, ethical hackers, penetration testers, and incident responders will discover methods to combat threats and mitigate risks posed by attackers. The book also provides a secure-by-design approach for leaders to build AI with security in mind. To get the most out of this book, you’ll need a basic understanding of security, ML concepts, and Python.
Teach Your Kids to Code is a parent's and teacher's guide to teaching kids basic programming and problem solving using Python, the powerful language used in college courses and by tech companies like Google and IBM. Step-by-step explanations will have kids learning computational thinking right away, while visual and game-oriented examples hold their attention. Friendly introductions to fundamental programming concepts such as variables, loops, and functions will help even the youngest programmers build the skills they need to make their own cool games and applications. Whether you've been coding for years or have never programmed anything at all, Teach Your Kids to Code will help you show your young programmer how to: –Explore geometry by drawing colorful shapes with Turtle graphics –Write programs to encode and decode messages, play Rock-Paper-Scissors, and calculate how tall someone is in Ping-Pong balls –Create fun, playable games like War, Yahtzee, and Pong –Add interactivity, animation, and sound to their apps Teach Your Kids to Code is the perfect companion to any introductory programming class or after-school meet-up, or simply your educational efforts at home. Spend some fun, productive afternoons at the computer with your kids—you can all learn something!
The book introduces key Artificial Intelligence (AI) concepts in an easy-to-read format with examples and illustrations. A complex, long, overly mathematical textbook does not always serve the purpose of conveying the basic AI concepts to most people. Someone with basic knowledge in Computer Science can have a quick overview of AI (heuristic searches, genetic algorithms, expert systems, game trees, fuzzy expert systems, natural language processing, super intelligence, etc.) with everyday examples. If you are taking a basic AI course and find the traditional AI textbooks intimidating, you may choose this as a “bridge” book, or as an introductory textbook. For students, there is a lower priced edition (ISBN 978-1944708016) of the same book. Published by CSTrends LLP.
In today's dynamic marketing landscape, artificial intelligence (AI) stands at the forefront of innovation, offering unprecedented opportunities for marketers. This book enables marketing professionals to harness the power of AI, featuring in-depth discussions on key topics such as generative AI, personalized customer experiences, team and workflow augmentation, and data analysis optimization. It is divided into four parts, each addressing a different aspect of AI and marketing. Part I covers the fundamentals of AI in marketing, including machine learning and generative AI. Part II explores growth areas for marketing and AI, using generative AI and customer journey personalization. Part III goes into detail about how to use AI, particularly generative AI tools, to enhance the marketing function. Part IV is about integration and optimization, providing insights on when and where to invest in AI and how to prepare your team for an AI-driven marketing future.