Dive Into Refactoring Github
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Large Language Models: A Deep Dive
Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs—their intricate architecture, underlying algorithms, and ethical considerations—require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models. This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs. Key Features: Over 100 techniques and state-of-the-art methods, including pre-training, prompt-based tuning, instruction tuning, parameter-efficient and compute-efficient fine-tuning, end-user prompt engineering, and building and optimizing Retrieval-Augmented Generation systems, along with strategies for aligning LLMs with human values using reinforcement learning Over 200 datasets compiled in one place, covering everything from pre- training to multimodal tuning, providing a robust foundation for diverse LLM applications Over 50 strategies to address key ethical issues such as hallucination, toxicity, bias, fairness, and privacy. Gain comprehensive methods for measuring, evaluating, and mitigating these challenges to ensure responsible LLM deployment Over 200 benchmarks covering LLM performance across various tasks, ethical considerations, multimodal applications, and more than 50 evaluation metrics for the LLM lifecycle Nine detailed tutorials that guide readers through pre-training, fine- tuning, alignment tuning, bias mitigation, multimodal training, and deploying large language models using tools and libraries compatible with Google Colab, ensuring practical application of theoretical concepts Over 100 practical tips for data scientists and practitioners, offering implementation details, tricks, and tools to successfully navigate the LLM life- cycle and accomplish tasks efficiently
Programming with GitHub Copilot
Accelerate your programming with the most popular AI coding tool on the market: GitHub Copilot In Programming with GitHub Copilot: Write Better Code — Faster, veteran software developer and GitHub community hero Kurt Dowswell delivers an insightful and hands-on exploration of GitHub's powerful, new AI coding assistant, Copilot. In the book, you'll discover how to use the tool's capabilities to push the boundaries of what you thought was possible in programming. Even if you've used autocomplete tools—like VS Code's TabNine extension—before, you'll be floored by GitHub Copilot's potential to transform the way you code. You'll learn how to install, configure, and use the software, from employing it's most common and widely used features to deploying business and enterprise functionality. You'll even discover how to fix runtime and compilation bugs and write unit, integration, and end-to-end tests. You'll also find: Prompt strategies to get GitHub Copilot to help you brainstorm new code solutions What the future looks like for AI-assisted coding, including discussions of issues like code licensing and ethics Directions for chatting with Copilot, including common commands and prompts to help you guide the conversation to where you want it to go Perfect for practicing programmers, developers, and software engineers, Programming with GitHub Copilot is also an essential resource for coders and other IT practitioners-in-training who want to expand their knowledge and improve the scope and depth of their programming skillsets.
Domain-Driven Refactoring
Author: Alessandro Colla
language: en
Publisher: Packt Publishing Ltd
Release Date: 2025-05-16
Apply domain-driven design practices effortlessly to evolve your system into a modern, robust application while mastering refactoring techniques that drive real-world results Key Features Learn how to modernize your system to make it as frictionless as possible Gain hands-on experience in applying strategic and tactical patterns through real-world examples Transform your architecture with practical guidance for seamless refactoring Purchase of the print or Kindle book includes a free PDF eBook Book Description As software development continues to grow, mastering domain-driven design (DDD) will help transform your approach to complex systems. Filled with actionable insights and practical examples, this book is your essential guide to implementing DDD principles, covering its key concepts and practical applications in modern architecture. Alessandro, an eCommerce specialist and DDD expert with 30 years of experience, and Alberto, a dedicated backend developer, tap into their extensive expertise to help you refactor your monolith into a modular structure, whether it be evolving into microservices or enhancing a maintainable monolith, resulting in a system that adapts to changing business needs and non-functional requirements. You’ll explore vital DDD patterns like strategic design with bounded contexts and ubiquitous language, improving communication between technical and domain experts. The chapters take you through modeling techniques to manage complexity and increase flexibility, while also addressing microservices integration, including inter-service communication, transaction management, and data strategies. By the end of this book, you’ll be able to decompose a monolith and refine its architecture for adaptability, all while ensuring business logic remains central to your software design and development. What you will learn Find out how to recognize the boundaries of your system's components Apply strategic patterns such as bounded contexts and ubiquitous language Master tactical patterns for building aggregates and entities Discover principal refactoring patterns and learn how to implement them Identify pain points in a complex code base and address them Explore event-driven architectures for component decoupling Get skilled at writing tests that validate and maintain architectural integrity Who this book is for This book is ideal for software developers, architects, and team leads looking to modernize legacy applications using domain-driven design principles. If you're a backend developer or software engineer looking to enhance your understanding of DDD, this guide will elevate your skills in designing robust systems. Team leads and architects will find valuable insights for guiding their teams through the transition from monoliths to microservices. Familiarity with C# is a must, as the book provides practical examples in this language.