Wydawca: Packt Publishing
Colin Holgate
LiveCode is a tool for developing mobile apps designed for those who don't want to use Objective-C, C++ or Java. Although it is a tool full of rich features to create apps it can be challenging to get beyond the basics and build interactive and fun apps. Using this book, you can develop various apps and this book guides you through till you upload the apps in the appstore.LiveCode Mobile Development Beginner's Guide will explain how to create applications with the easiest, most practical cross platform framework available, Livecode Mobile and upload the apps to the appstore with minimal effort.Throughout the book, you'll learn details that will help you become a pro at mobile app development using LiveCode. You begin with simple calculator application and quickly enhance it using LiveCode Mobile. Start by learning the interface controls for videos and images of LiveCode's environment. Dig into configuring devices, building user interfaces, and making rich media applications, then finish by uploading the mobile applications to App Stores. You will learn how to build apps for devices such as iPhone, Android with the recently developed LiveCode Mobile through sample applications of increasing complexity.
LLM Design Patterns. A Practical Guide to Building Robust and Efficient AI Systems
Ken Huang
This practical guide for AI professionals enables you to build on the power of design patterns to develop robust, scalable, and efficient large language models (LLMs). Written by a global AI expert and popular author driving standards and innovation in Generative AI, security, and strategy, this book covers the end-to-end lifecycle of LLM development and introduces reusable architectural and engineering solutions to common challenges in data handling, model training, evaluation, and deployment.You’ll learn to clean, augment, and annotate large-scale datasets, architect modular training pipelines, and optimize models using hyperparameter tuning, pruning, and quantization. The chapters help you explore regularization, checkpointing, fine-tuning, and advanced prompting methods, such as reason-and-act, as well as implement reflection, multi-step reasoning, and tool use for intelligent task completion. The book also highlights Retrieval-Augmented Generation (RAG), graph-based retrieval, interpretability, fairness, and RLHF, culminating in the creation of agentic LLM systems.By the end of this book, you’ll be equipped with the knowledge and tools to build next-generation LLMs that are adaptable, efficient, safe, and aligned with human values.
Paul Iusztin, Maxime Labonne, Julien Chaumond, Hamza...
Artificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. The guide walks you through building an LLM-powered twin that’s cost-effective, scalable, and modular. It moves beyond isolated Jupyter notebooks, focusing on how to build production-grade end-to-end LLM systems.Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM Twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects.By the end of this book, you will be proficient in deploying LLMs that solve practical problems while maintaining low-latency and high-availability inference capabilities. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will deepen your understanding of LLMs and sharpen your ability to implement them effectively.
LLM Prompt Engineering for Developers. The Art and Science of Unlocking LLMs' True Potential
Aymen El Amri
LLM Prompt Engineering For Developers begins by laying the groundwork with essential principles of natural language processing (NLP), setting the stage for more complex topics. It methodically guides readers through the initial steps of understanding how large language models work, providing a solid foundation that prepares them for the more intricate aspects of prompt engineering.As you proceed, the book transitions into advanced strategies and techniques that reveal how to effectively interact with and utilize these powerful models. From crafting precise prompts that enhance model responses to exploring innovative methods like few-shot and zero-shot learning, this resource is designed to unlock the full potential of language model technology.This book not only teaches the technical skills needed to excel in the field but also addresses the broader implications of AI technology. It encourages thoughtful consideration of ethical issues and the impact of AI on society. By the end of this book, readers will master the technical aspects of prompt engineering & appreciate the importance of responsible AI development, making them well-rounded professionals ready to focus on the advancement of this cutting-edge technology.
Ahmed Menshawy, Mahmoud Fahmy
The integration of large language models (LLMs) into enterprise applications is transforming how businesses use AI to drive smarter decisions and efficient operations. LLMs in Enterprise is your practical guide to bringing these capabilities into real-world business contexts. It demystifies the complexities of LLM deployment and provides a structured approach for enhancing decision-making and operational efficiency with AI.Starting with an introduction to the foundational concepts, the book swiftly moves on to hands-on applications focusing on real-world challenges and solutions. You’ll master data strategies and explore design patterns that streamline the optimization and deployment of LLMs in enterprise environments. From fine-tuning techniques to advanced inferencing patterns, the book equips you with a toolkit for solving complex challenges and driving AI-led innovation in business processes.By the end of this book, you’ll have a solid grasp of key LLM design patterns and how to apply them to enhance the performance and scalability of your generative AI solutions.
LLVM Code Generation. A deep dive into compiler backend development
Quentin Colombet, Kristof Beyls
The LLVM infrastructure is a popular compiler ecosystem widely used in the tech industry and academia. This technology is crucial for both experienced and aspiring compiler developers looking to make an impact in the field. Written by Quentin Colombet, a veteran LLVM contributor and architect of the GlobalISel framework, this book provides a primer on the main aspects of LLVM, with an emphasis on its backend infrastructure; that is, everything needed to transform the intermediate representation (IR) produced by frontends like Clang into assembly code and object files.You’ll learn how to write an optimizing code generator for a toy backend in LLVM. The chapters will guide you step by step through building this backend while exploring key concepts, such as the ABI, cost model, and register allocation. You’ll also find out how to express these concepts using LLVM's existing infrastructure and how established backends address these challenges. Furthermore, the book features code snippets that demonstrate the actual APIs.By the end of this book, you’ll have gained a deeper understanding of LLVM. The concepts presented are expected to remain stable across different LLVM versions, making this book a reliable quick reference guide for understanding LLVM.
