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Prompt Design Patterns: the ultimate guide in prompt engineering. This game-changer provides a proven method to rapidly advance from novice to expert, unlocking unprecedented productivity and innovation in AI.
Ever notice that—in spite of their pervasiveness—designing web applications is still challenging? While their benefits motivate their creation, there are no well-established guidelines for design. This often results in inconsistent behaviors and appearances, even among web applications created by the same company. Design patterns for web applications, similar in concept to those for web sites and software design, offer an effective solution. In Web Application Design Patterns, Pawan Vora documents design patterns for web applications by not only identifying design solutions for user interaction problems, but also by examining the rationale for their effectiveness, and by presenting how they should be applied. Design interfaces faster, with a better rationale for the solutions you choose. Learn from over more than 100 patterns, with extensive annotation on use and extension. Take a short-cut into understanding the industry with more than 500 full-color screenshots.
A Comprehensive Guide to Leverage Generative AI in the Modern Enterprise KEY FEATURES ● Gain a comprehensive understanding of LLMs within the framework of Generative AI, from foundational concepts to advanced applications. ● Dive into practical exercises and real-world applications, accompanied by detailed code walkthroughs in Python. ● Explore LLMOps with a dedicated focus on ensuring trustworthy AI and best practices for deploying, managing, and maintaining LLMs in enterprise settings. ● Prioritize the ethical and responsible use of LLMs, with an emphasis on building models that adhere to principles of fairness, transparency, and accountability, fostering trust in AI technologies. DESCRIPTION “Mastering Large Language Models with Python” is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects. Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation. Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of artificial intelligence. WHAT WILL YOU LEARN ● In-depth study of LLM architecture and its versatile applications across industries. ● Harness open-source and proprietary LLMs to craft innovative solutions. ● Implement LLM APIs for a wide range of tasks spanning natural language processing, audio analysis, and visual recognition. ● Optimize LLM deployment through techniques such as quantization and operational strategies like LLMOps, ensuring efficient and scalable model usage. ● Master prompt engineering techniques to fine-tune LLM outputs, enhancing quality and relevance for diverse use cases. ● Navigate the complex landscape of ethical AI development, prioritizing responsible practices to drive impactful technology adoption and advancement. WHO IS THIS BOOK FOR? This book is tailored for software engineers, data scientists, AI researchers, and technology leaders with a foundational understanding of machine learning concepts and programming. It's ideal for those looking to deepen their knowledge of Large Language Models and their practical applications in the field of AI. If you aim to explore LLMs extensively for implementing inventive solutions or spearheading AI-driven projects, this book is tailored to your needs. TABLE OF CONTENTS 1. The Basics of Large Language Models and Their Applications 2. Demystifying Open-Source Large Language Models 3. Closed-Source Large Language Models 4. LLM APIs for Various Large Language Model Tasks 5. Integrating Cohere API in Google Sheets 6. Dynamic Movie Recommendation Engine Using LLMs 7. Document-and Web-based QA Bots with Large Language Models 8. LLM Quantization Techniques and Implementation 9. Fine-tuning and Evaluation of LLMs 10. Recipes for Fine-Tuning and Evaluating LLMs 11. LLMOps - Operationalizing LLMs at Scale 12. Implementing LLMOps in Practice Using MLflow on Databricks 13. Mastering the Art of Prompt Engineering 14. Prompt Engineering Essentials and Design Patterns 15. Ethical Considerations and Regulatory Frameworks for LLMs 16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning) Index
The Gang of Four’s seminal catalog of 23 patterns to solve commonly occurring design problems Patterns allow designers to create more flexible, elegant, and ultimately reusable designs without having to rediscover the design solutions themselves. Highly influential, Design Patterns is a modern classic that introduces what patterns are and how they can help you design object-oriented software and provides a catalog of simple solutions for those already programming in at last one object-oriented programming language. Each pattern: Describes the circumstances in which it is applicable, when it can be applied in view of other design constraints, and the consequences and trade-offs of using the pattern within a larger design Is compiled from real systems and based on real-world examples Includes downloadable C++ source code that demonstrates how patterns can be implemented and Python From the preface: “Once you the design patterns and have had an ‘Aha!’ (and not just a ‘Huh?’) experience with them, you won't ever think about object-oriented design in the same way. You'll have insights that can make your own designs more flexible, modular, reusable, and understandable - which is why you're interested in object-oriented technology in the first place, right?”
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly
Software -- Software Engineering.
Understanding UI patterns is invaluable to anyone creating websites for the first time. It helps you make connections between which tools are right for which jobs, understand the processes, and think deeply about the context of a problem. This is your concise guide to the tested and proven general mechanisms for solving recurring user interface problems, so that you don't have to reinvent the wheel. You'll see how to find a pattern you can apply to a given UI problem and how to deconstruct patterns to understand them in depth, including their constraints. UI patterns lead to better use of existing conventions and converging web standards. This book shows you how to spot anti-patterns, how to mix and match patterns, and how they inform design systems. By helping the non-web professionals and junior web professionals of the world use basic patterns, the web industry can put its best foot forward as new interfaces such as VR/AR/MR, conversational UIs, machine learning, voice input, evolving gestural interactions and more infiltrate the market. Given the emerging popularity of design systems and space of DesignOps, as well as the rise of companies competing on design and usability, now is the time to think about how we use and evolve UI patterns and scale design systems. What You'll Learn Produce intuitive products through consistency and familiarity.Save time instead of starting from scratch.Communicate design decisions with evidence to support solutions.Use smart defaults without extensive product design experience.Improve a user's experience.Scale growing business with design. Who This Book Is For Those familiar with creating websites and want to learn more, WordPress bloggers, or marketers who want to weave components together into a usable, revenue-generating experience.
From learning about the most sought-after design patterns to a comprehensive coverage of architectural patterns and code testing, this book is all you need to write clean, reusable code Key FeaturesWrite clean, reusable and maintainable code, and make the most of the latest Swift version.Analyze case studies of some of the popular open source projects and give your workflow a huge boostChoose patterns such as MVP, MVC, and MVVM depending on the application being builtBook Description Swift keeps gaining traction not only amongst Apple developers but also as a server-side language. This book demonstrates how to apply design patterns and best practices in real-life situations, whether that's for new or already existing projects. You’ll begin with a quick refresher on Swift, the compiler, the standard library, and the foundation, followed by the Cocoa design patterns – the ones at the core of many cocoa libraries – to follow up with the creational, structural, and behavioral patterns as defined by the GoF. You'll get acquainted with application architecture, as well as the most popular architectural design patterns, such as MVC and MVVM, and learn to use them in the context of Swift. In addition, you’ll walk through dependency injection and functional reactive programming. Special emphasis will be given to techniques to handle concurrency, including callbacks, futures and promises, and reactive programming. These techniques will help you adopt a test-driven approach to your workflow in order to use Swift Package Manager and integrate the framework into the original code base, along with Unit and UI testing. By the end of the book, you'll be able to build applications that are scalable, faster, and easier to maintain. What you will learnWork efficiently with Foundation and Swift Standard libraryUnderstand the most critical GoF patterns and use them efficientlyUse Swift 4.2 and its unique capabilities (and limitations) to implement and improve GoF patternsImprove your application architecture and optimize for maintainability and performanceWrite efficient and clean concurrent programs using futures and promises, or reactive programming techniquesUse Swift Package Manager to refactor your program into reusable componentsLeverage testing and other techniques for writing robust codeWho this book is for This book is for intermediate developers who want to apply design patterns with Swift to structure and scale their applications. You are expected to have basic knowledge of iOS and Swift.
This text offers advice on creating user-friendly interface designs - whether they're delivered on the Web, a CD, or a 'smart' device like a cell phone. It presents solutions to common UI design problems as a collection of patterns - each containing concrete examples, recommendations, and warnings.