AI has acquired startling new language capabilities in just the past few years. Driven by rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend is enabling new features, products, and entire industries. Through this book's visually educational nature, readers will learn practical tools and concepts they need to use these capabilities today. You'll understand how to use pretrained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; and use existing libraries and pretrained models for text classification, search, and clusterings. This book also helps you: Understand the architecture of Transformer language models that excel at text generation and representationBuild advanced LLM pipelines to cluster text documents and explore the topics they coverBuild semantic search engines that go beyond keyword search, using methods like dense retrieval and rerankersExplore how generative models can be used, from prompt engineering all the way to retrieval-augmented generationGain a deeper understanding of how to train LLMs and optimize them for specific applications using generative model fine-tuning, contrastive fine-tuning, and in-context learning Read more

Step into the world of LLMs with this practical guide that takes you from the fundamentals to deploying advanced applications using LLMOps best practicesPurchase of the print or Kindle book includes a free eBook in PDF format“This book is instrumental in making sure that as many people as possible can not only use LLMs but also adapt them, fine-tune them, quantize them, and make them efficient enough to deploy in the real world.”- Julien Chaumond, CTO and Co-founder, Hugging FaceBook DescriptionThis LLM book provides practical 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.What you will learnImplement robust data pipelines and manage LLM training cyclesCreate your own LLM and refine with the help of hands-on examplesGet started with LLMOps by diving into core MLOps principles like IaCPerform supervised fine-tuning and LLM evaluationDeploy end-to-end LLM solutions using AWS and other toolsExplore continuous training, monitoring, and logic automationLearn about RAG ingestion as well as inference and feature pipelinesWho this book is forThis book is for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. Basic knowledge of LLMs and the Gen AI landscape, Python and AWS is recommended. Whether you are new to AI or looking to enhance your skills, this book provides comprehensive guidance on implementing LLMs in real-world scenarios.Table of ContentsUndersstanding the LLM Twin Concept and ArchitectureTooling and InstallationData EngineeringRAG Feature PipelineSupervised Fine-tuningFine-tuning with Preference AlignmentEvaluating LLMsInference OptimizationRAG Inference PipelineInference Pipeline DeploymentMLOps and LLMOpsAppendix: MLOps Principles Read more

Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up!In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You’ll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to: • Plan and code all the parts of an LLM • Prepare a dataset suitable for LLM training • Fine-tune LLMs for text classification and with your own data • Use human feedback to ensure your LLM follows instructions • Load pretrained weights into an LLM Build a Large Language Model (from Scratch) takes you inside the AI black box to tinker with the internal systems that power generative AI. As you work through each key stage of LLM creation, you’ll develop an in-depth understanding of how LLMs work, their limitations, and their customization methods. Your LLM can be developed on an ordinary laptop, and used as your own personal assistant. About the technology Physicist Richard P. Feynman reportedly said, “I don’t understand anything I can’t build.” Based on this same powerful principle, bestselling author Sebastian Raschka guides you step by step as you build a GPT-style LLM that you can run on your laptop. This is an engaging book that covers each stage of the process, from planning and coding to training and fine-tuning. About the book Build a Large Language Model (From Scratch) is a practical and eminently-satisfying hands-on journey into the foundations of generative AI. Without relying on any existing LLM libraries, you’ll code a base model, evolve it into a text classifier, and ultimately create a chatbot that can follow your conversational instructions. And you’ll really understand it because you built it yourself! What's inside • Plan and code an LLM comparable to GPT-2 • Load pretrained weights • Construct a complete training pipeline • Fine-tune your LLM for text classification • Develop LLMs that follow human instructions About the reader Readers need intermediate Python skills and some knowledge of machine learning. The LLM you create will run on any modern laptop and can optionally utilize GPUs. About the author Sebastian Raschka is a Staff Research Engineer at Lightning AI, where he works on LLM research and develops open-source software. The technical editor on this book was David Caswell. Table of Contents 1 Understanding large language models 2 Working with text data 3 Coding attention mechanisms 4 Implementing a GPT model from scratch to generate text 5 Pretraining on unlabeled data 6 Fine-tuning for classification 7 Fine-tuning to follow instructions A Introduction to PyTorch B References and further reading C Exercise solutions D Adding bells and whistles to the training loop E Parameter-efficient fine-tuning with LoRA Read more

Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. Learn how to empower AI to work for you. This book explains: The structure of the interaction chain of your program's AI model and the fine-grained steps in between How AI model requests arise from transforming the application problem into a document completion problem in the model training domain The influence of LLM and diffusion model architecture—and how to best interact with it How these principles apply in practice in the domains of natural language processing, text and image generation, and code Read more

Leverage cutting-edge generative AI techniques such as RAG to realize the potential of your data and drive innovation as well as gain strategic advantageKey FeaturesOptimize data retrieval and generation using vector databasesBoost decision-making and automate workflows with AI agentsOvercome common challenges in implementing real-world RAG systemsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGenerative AI is helping organizations tap into their data in new ways, with retrieval-augmented generation (RAG) combining the strengths of large language models (LLMs) with internal data for more intelligent and relevant AI applications. The author harnesses his decade of ML experience in this book to equip you with the strategic insights and technical expertise needed when using RAG to drive transformative outcomes.The book explores RAG’s role in enhancing organizational operations by blending theoretical foundations with practical techniques. You’ll work with detailed coding examples using tools such as LangChain and Chroma’s vector database to gain hands-on experience in integrating RAG into AI systems. The chapters contain real-world case studies and sample applications that highlight RAG’s diverse use cases, from search engines to chatbots. You’ll learn proven methods for managing vector databases, optimizing data retrieval, effective prompt engineering, and quantitatively evaluating performance. The book also takes you through advanced integrations of RAG with cutting-edge AI agents and emerging non-LLM technologies.By the end of this book, you’ll be able to successfully deploy RAG in business settings, address common challenges, and push the boundaries of what’s possible with this revolutionary AI technique.What you will learnUnderstand RAG principles and their significance in generative AIIntegrate LLMs with internal data for enhanced operationsMaster vectorization, vector databases, and vector search techniquesDevelop skills in prompt engineering specific to RAG and design for precise AI responsesFamiliarize yourself with AI agents' roles in facilitating sophisticated RAG applicationsOvercome scalability, data quality, and integration issuesDiscover strategies for optimizing data retrieval and AI interpretabilityWho this book is forThis book is for AI researchers, data scientists, software developers, and business analysts looking to leverage RAG and generative AI to enhance data retrieval, improve AI accuracy, and drive innovation. It is particularly suited for anyone with a foundational understanding of AI who seeks practical, hands-on learning. The book offers real-world coding examples and strategies for implementing RAG effectively, making it accessible to both technical and non-technical audiences. A basic understanding of Python and Jupyter Notebooks is required.Table of ContentsWhat Is Retrieval-Augmented Generation (RAG)Code Lab – An Entire RAG PipelinePractical Applications of RAGComponents of a RAG SystemManaging Security in RAG ApplicationsInterfacing with RAG and GradioThe Key Role Vectors and Vector Stores Play in RAGSimilarity Searching with VectorsEvaluating RAG Quantitatively and with VisualizationsKey RAG Components in LangChainUsing LangChain to Get More from RAGCombining RAG with the Power of AI Agents and LangGraphUsing Prompt Engineering to Improve RAG EffortsAdvanced RAG-Related Techniques for Improving Results Read more

Goes beyond academic discussions deeply into the applications layer of Foundation Models.This practical book offers clear, example-rich explanations of how LLMs work, how you can interact with them, and how to integrate LLMs into your own applications. Find out what makes LLMs so different from traditional software and ML, discover best practices for working with them out of the lab, and dodge common pitfalls with experienced advice. In LLMs in Production you will: • Grasp the fundamentals of LLMs and the technology behind them • Evaluate when to use a premade LLM and when to build your own • Efficiently scale up an ML platform to handle the needs of LLMs • Train LLM foundation models and finetune an existing LLM • Deploy LLMs to the cloud and edge devices using complex architectures like PEFT and LoRA • Build applications leveraging the strengths of LLMs while mitigating their weaknesses LLMs in Production delivers vital insights into delivering MLOps so you can easily and seamlessly guide one to production usage. Inside, you’ll find practical insights into everything from acquiring an LLM-suitable training dataset, building a platform, and compensating for their immense size. Plus, tips and tricks for prompt engineering, retraining and load testing, handling costs, and ensuring security. Foreword by Joe Reis. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Most business software is developed and improved iteratively, and can change significantly even after deployment. By contrast, because LLMs are expensive to create and difficult to modify, they require meticulous upfront planning, exacting data standards, and carefully-executed technical implementation. Integrating LLMs into production products impacts every aspect of your operations plan, including the application lifecycle, data pipeline, compute cost, security, and more. Get it wrong, and you may have a costly failure on your hands. About the book LLMs in Production teaches you how to develop an LLMOps plan that can take an AI app smoothly from design to delivery. You’ll learn techniques for preparing an LLM dataset, cost-efficient training hacks like LORA and RLHF, and industry benchmarks for model evaluation. Along the way, you’ll put your new skills to use in three exciting example projects: creating and training a custom LLM, building a VSCode AI coding extension, and deploying a small model to a Raspberry Pi. What's inside • Balancing cost and performance • Retraining and load testing • Optimizing models for commodity hardware • Deploying on a Kubernetes cluster About the reader For data scientists and ML engineers who know Python and the basics of cloud deployment. About the author Christopher Brousseau and Matt Sharp are experienced engineers who have led numerous successful large scale LLM deployments. Table of Contents 1 Words’ awakening: Why large language models have captured attention 2 Large language models: A deep dive into language modeling 3 Large language model operations: Building a platform for LLMs 4 Data engineering for large language models: Setting up for success 5 Training large language models: How to generate the generator 6 Large language model services: A practical guide 7 Prompt engineering: Becoming an LLM whisperer 8 Large language model applications: Building an interactive experience 9 Creating an LLM project: Reimplementing Llama 3 10 Creating a coding copilot project: This would have helped you earlier 11 Deploying an LLM on a Raspberry Pi: How low can you go? 12 Production, an ever-changing landscape: Things are just getting started A History of linguistics B Reinforcement learning with human feedback C Multimodal latent spaces Read more

“This is the most comprehensive textbook to date on building LLM applications - all essential topics in an AI Engineer's toolkit."- Jerry Liu, Co-founder and CEO of LlamaIndexTL;DR(UPDATED ON OCTOBER 2024) With amazing feedback from industry leaders, this book is an end-to-end resource for anyone looking to enhance their skills or dive into the world of AI and develop their understanding of Generative AI and Large Language Models (LLMs). It explores various methods to adapt "foundational" LLMs to specific use cases with enhanced accuracy, reliability, and scalability. Written by over 10 people on our Team at Towards AI and curated by experts from Activeloop, LlamaIndex, Mila, and more, it is a roadmap to the tech stack of the future.The book aims to guide developers through creating LLM products ready for production, leveraging the potential of AI across various industries. It is tailored for readers with an intermediate knowledge of Python.What's Inside this 470-page Book? (Updated on October 2024)Hands-on Guide on LLMs, Prompting, Retrieval Augmented Generation (RAG) & Fine-tuningRoadmap for Building Production-Ready Applications using LLMsFundamentals of LLM TheorySimple-to-Advanced LLM Techniques & FrameworksCode Projects with Real-World ApplicationsColab Notebooks that you can run right awayCommunity access and our own AI TutorTable of ContentsChapter I Introduction to Large Language ModelsChapter II LLM Architectures & LandscapeChapter III LLMs in PracticeChapter IV Introduction to PromptingChapter V Retrieval-Augmented GenerationChapter VI Introduction to LangChain & LlamaIndexChapter VII Prompting with LangChainChapter VIII Indexes, Retrievers, and Data PreparationChapter IX Advanced RAGChapter X AgentsChapter XI Fine-TuningChapter XII DeploymentWhat Experts Think About The Book"A truly wonderful resource that develops understanding of LLMs from the ground up, from theory to code and modern frameworks. Grounds your knowledge in research trends and frameworks that develop your intuition around what's coming. Highly recommend."- Pete Huang, Co-founder of The Neuron“This book is filled with end-to-end explanations, examples, and comprehensive details. Louis and the Towards AI team have written an essential read for developers who want to expand their AI expertise and apply it to real-world challenges, making it a valuable addition to both personal and professional libraries.”- Alex Volkov, AI Evangelist at Weights & Biases and Host of ThursdAI news"This book is the most thorough overview of LLMs I've come across. An excellent primer for newcomers and a valuable reference for experienced practitioners."- Shaw Talebi, Founder of The Data Entrepreneurs, AI Educator and AdvisorWhether you're looking to enhance your skills or dive into the world of AI for the first time as a programmer or software student, our book is for you. From the basics of LLMs to mastering fine-tuning and RAG for scalable, reliable AI applications, we guide you every step of the way. Read more

Create LLM-powered autonomous agents and intelligent assistants tailored to your business and personal needs.From script-free customer service chatbots to fully independent agents operating seamlessly in the background, AI-powered assistants represent a breakthrough in machine intelligence. In AI Agents in Action, you'll master a proven framework for developing practical agents that handle real-world business and personal tasks.Author Micheal Lanham combines cutting-edge academic research with hands-on experience to help you: Understand and implement AI agent behavior patterns Design and deploy production-ready intelligent agents Leverage the OpenAI Assistants API and complementary tools Implement robust knowledge management and memory systems Create self-improving agents with feedback loops Orchestrate collaborative multi-agent systems Enhance agents with speech and vision capabilitiesThis book teaches you to build trustworthy AI capable of handling high-stakes negotiations. You'll master prompt engineering to create agents with distinct personas and profiles, and develop multi-agent collaborations that thrive in unpredictable environments. Beyond just learning a new technology, you'll discover a transformative approach to problem-solving.About the technologyMost production AI systems require many orchestrated interactions between the user, AI models, and a wide variety of data sources. AI agents capture and organize these interactions into autonomous components that can process information, make decisions, and learn from interactions behind the scenes. This book will show you how to create AI agents and connect them together into powerful multi-agent systems.About the authorMicheal Lanham is a software and technology innovator with over 20 years of industry experience. He has authored books on deep learning, including Manning’s Evolutionary Deep Learning. Read more

Docker Deep Dive (2025 Edition) - The best Docker book just got even better!The #1 best-selling and most comprehensive Docker book - fully updated for 2025!For nearly a decade, Docker Deep Dive has been the go-to guide for developers and IT professionals looking to master Docker. With more reviews than any other Docker book and a track record as the most popular Docker book on the market, this Amazon bestseller is your ultimate resource for mastering Docker.What's New in the 2025 Edition?This latest edition is fully updated with cutting-edge advancements in the Docker ecosystem, including:✅ Wasm containers (WebAssembly) - The future of fast, portable, high-performance workloads✅ Multi-container LLM chatbot apps - Deploy and manage AI-powered chatbots with Docker Compose & Ollama✅ Professional development environment, app templates, debugging, and vulnerability scanning, with Docker Desktop, Docker Init, Docker Debug, and Docker ScoutThe demand for Docker expertise is skyrocketing as organizations build cloud-native and AI-powered applications. Whether you're deploying apps locally or in the cloud, this book equips you with everything you need to succeed in the real world.What You'll Learn:✔ Set up a professional Docker development environment - for free✔ Create and manage containerized applications from scratch✔ Build and share container images using Docker Hub✔ Deploy and orchestrate multi-container apps with Docker Compose & Swarm✔ Create Wasm apps, build them as Docker containers, and run them with Docker✔ Deploy and manage LLM chatbots with Docker Compose✔ Create production-ready images that run on x86 and ARM✔ Easy and powerful debugging with Docker Debug✔ Vulnerability scanning with Docker Scout✔ Leverage the latest Docker technologies, including Docker Desktop, Docker Debug, Docker Init, Docker Scout, and moreWhether you're a beginner looking for a step-by-step guide or an experienced developer who wants to stay ahead of the curve, Docker Deep Dive (2025 Edition) is the ultimate resource for mastering Docker.Get your copy today and take your container skills to the next level! Read more

Learn how to defend AI and LLM systems against manipulation and intrusion through adversarial attacks such as poisoning, trojan horses, and model extraction, leveraging DevSecOps, MLOps and other methods to secure systems Key FeaturesUnderstand the unique security challenges presented by predictive and generative AIExplore common adversarial attack strategies as well as emerging threats such as prompt injectionMitigate the risks of attack on your AI system with threat modeling and secure-by-design methodsPurchase of the print or Kindle book includes a free PDF eBookBook 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 you with the skills to secure AI technologies, moving beyond research hype or business-as-usual activities.This strategy-based book is a comprehensive guide to AI security, presenting you with a structured approach with practical examples to identify and counter adversarial attacks. In Part 1, you’ll touch on getting started with AI and learn about adversarial attacks, before Parts 2, 3 and 4 move through different adversarial attack methods, exploring how each type of attack is performed and how you can defend your AI system against it. Part 5 is dedicated to introducing secure-by-design AI strategy, including threat modeling and MLSecOps and consolidating recent research, industry standards and taxonomies such as OWASP and NIST. Finally, 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 against the threat of adversarial attacks effectively.What you will learnSet up a playground to explore how adversarial attacks workDiscover how AI models can be poisoned and what you can do to prevent thisLearn about the use of trojan horses to tamper with and reprogram modelsUnderstand supply chain risksExamine how your models or data can be stolen in privacy attacksSee how GANs are weaponized for Deepfake creation and cyberattacksExplore emerging LLM-specific attacks, such as prompt injectionLeverage DevSecOps, MLOps and MLSecOps to secure your AI systemWho this book is forThis book tackles AI security from both angles - offense and defence. AI 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 to AI and mitigate the 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.Table of ContentsGetting Started with AIBuilding Our Adversarial PlaygroundSecurity and Adversarial AIPoisoning AttacksModel Tampering with Trojan Horses and Model ReprogrammingSupply Chain Attacks and Adversarial AIEvasion Attacks against Deployed AIPrivacy Attacks – Stealing ModelsPrivacy Attacks – Stealing DataPrivacy-Preserving AIGenerative AI – A New FrontierWeaponizing GANs for Deepfakes and Adversarial Attacks(N.B. Please use the Read Sample option to see further chapters) Read more

The Logic of Subchapter K was originally intended for use as a text for a law school course in Partnership Taxation. Together with the accompanying problem set and teachers manual, it guides students through the conceptual framework of subchapter K, while thoroughly covering the many difficult technical matters in the statutes and regulations, with the goal of giving students a firm understanding of this most difficult subject. Each chapter begins with a basic explanation of the relevant provisions and the roles that they play in the overall structure of subchapter K. It includes an increasingly detailed discussion of the specific rules, including multiple illustrative examples. Each chapter builds on the earlier chapters, leading the student through subchapter K. The authors have successfully used the text and problems for both JD and LLM courses at NYU School of Law, Yale Law School, Cardozo School of Law, and Hastings College of the Law.Since the publication of the first edition of the book in 1996, it has also been used widely in business and accounting courses outside of the law school setting. It is also on the shelf of many practitioners. This 6th Edition addresses multiple changes made by the Tax Cuts and Jobs Act of 2017, including Section 199A qualified business deduction, the expensing of assets under Section 168(k), partnership terminations under Section 708, and an assortment of regulatory changes made in the three years since publication of the 5th Edition. Read more

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Goes beyond academic discussions deeply into the applications layer of Foundation Models.This practical book offers clear, example-rich explanations of how LLMs work, how you can interact with them, and how to integrate LLMs into your own applications. Find out what makes LLMs so different from traditional software and ML, discover best practices for working with them out of the lab, and dodge common pitfalls with experienced advice. In LLMs in Production you will: • Grasp the fundamentals of LLMs and the technology behind them • Evaluate when to use a premade LLM and when to build your own • Efficiently scale up an ML platform to handle the needs of LLMs • Train LLM foundation models and finetune an existing LLM • Deploy LLMs to the cloud and edge devices using complex architectures like PEFT and LoRA • Build applications leveraging the strengths of LLMs while mitigating their weaknesses LLMs in Production delivers vital insights into delivering MLOps so you can easily and seamlessly guide one to production usage. Inside, you’ll find practical insights into everything from acquiring an LLM-suitable training dataset, building a platform, and compensating for their immense size. Plus, tips and tricks for prompt engineering, retraining and load testing, handling costs, and ensuring security. Foreword by Joe Reis. About the technology Most business software is developed and improved iteratively, and can change significantly even after deployment. By contrast, because LLMs are expensive to create and difficult to modify, they require meticulous upfront planning, exacting data standards, and carefully-executed technical implementation. Integrating LLMs into production products impacts every aspect of your operations plan, including the application lifecycle, data pipeline, compute cost, security, and more. Get it wrong, and you may have a costly failure on your hands. About the book LLMs in Production teaches you how to develop an LLMOps plan that can take an AI app smoothly from design to delivery. You’ll learn techniques for preparing an LLM dataset, cost-efficient training hacks like LORA and RLHF, and industry benchmarks for model evaluation. Along the way, you’ll put your new skills to use in three exciting example projects: creating and training a custom LLM, building a VSCode AI coding extension, and deploying a small model to a Raspberry Pi. What's inside • Balancing cost and performance • Retraining and load testing • Optimizing models for commodity hardware • Deploying on a Kubernetes cluster About the reader For data scientists and ML engineers who know Python and the basics of cloud deployment. About the author Christopher Brousseau and Matt Sharp are experienced engineers who have led numerous successful large scale LLM deployments. Read more

Large language models (LLMs) are revolutionizing the world, promising to automate tasks and solve complex problems. A new generation of software applications are using these models as building blocks to unlock new potential in almost every domain, but reliably accessing these capabilities requires new skills. This book will teach you the art and science of prompt engineering-the key to unlocking the true potential of LLMs. Industry experts John Berryman and Albert Ziegler share how to communicate effectively with AI, transforming your ideas into a language model-friendly format. By learning both the philosophical foundation and practical techniques, you'll be equipped with the knowledge and confidence to build the next generation of LLM-powered applications.Understand LLM architecture and learn how to best interact with itDesign a complete prompt-crafting strategy for an applicationGather, triage, and present context elements to make an efficient promptMaster specific prompt-crafting techniques like few-shot learning, chain-of-thought prompting, and RAG Read more

Get hands-on with GPT 3.5, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applicationsKey FeaturesEmbed LLMs into real-world applicationsUse LangChain to orchestrate LLMs and their components within applicationsGrasp basic and advanced techniques of prompt engineeringBook DescriptionBuilding LLM Powered Applications delves into the fundamental concepts, cutting-edge technologies, and practical applications that LLMs offer, ultimately paving the way for the emergence of large foundation models (LFMs) that extend the boundaries of AI capabilities.The book begins with an in-depth introduction to LLMs. We then explore various mainstream architectural frameworks, including both proprietary models (GPT 3.5/4) and open-source models (Falcon LLM), and analyze their unique strengths and differences. Moving ahead, with a focus on the Python-based, lightweight framework called LangChain, we guide you through the process of creating intelligent agents capable of retrieving information from unstructured data and engaging with structured data using LLMs and powerful toolkits. Furthermore, the book ventures into the realm of LFMs, which transcend language modeling to encompass various AI tasks and modalities, such as vision and audio.Whether you are a seasoned AI expert or a newcomer to the field, this book is your roadmap to unlock the full potential of LLMs and forge a new era of intelligent machines.What you will learnExplore the core components of LLM architecture, including encoder-decoder blocks and embeddingsUnderstand the unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLMUse AI orchestrators like LangChain, with Streamlit for the frontendGet familiar with LLM components such as memory, prompts, and toolsLearn how to use non-parametric knowledge and vector databasesUnderstand the implications of LFMs for AI research and industry applicationsCustomize your LLMs with fine tuningLearn about the ethical implications of LLM-powered applicationsWho this book is for Software engineers and data scientists who want hands-on guidance for applying LLMs to build applications. The book will also appeal to technical leaders, students, and researchers interested in applied LLM topics.We don’t assume previous experience with LLM specifically. But readers should have core ML/software engineering fundamentals to understand and apply the content.Table of ContentsIntroduction to Large Language ModelsLLMs for AI-Powered ApplicationsChoosing an LLM for Your ApplicationPrompt EngineeringEmbedding LLMs within Your ApplicationsBuilding Conversational ApplicationsSearch and Recommendation Engines with LLMsUsing LLMs with Structured DataWorking with CodeBuilding Multimodal Applications with LLMsFine-Tuning Large Language ModelsResponsible AIEmerging Trends and Innovations Read more

The definitive guide to LLMs, from architectures, pretraining, and fine-tuning to Retrieval Augmented Generation (RAG), multimodal Generative AI, risks, and implementations with ChatGPT Plus with GPT-4, Hugging Face, and Vertex AIKey FeaturesCompare and contrast 20+ models (including GPT-4, BERT, and Llama 2) and multiple platforms and libraries to find the right solution for your projectApply RAG with LLMs using customized texts and embeddingsMitigate LLM risks, such as hallucinations, using moderation models and knowledge basesPurchase of the print or Kindle book includes a free eBook in PDF formatBook DescriptionTransformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV).The book guides you through different transformer architectures to the latest Foundation Models and Generative AI. You’ll pretrain and fine-tune LLMs and work through different use cases, from summarization to implementing question-answering systems with embedding-based search techniques. You will also learn the risks of LLMs, from hallucinations and memorization to privacy, and how to mitigate such risks using moderation models with rule and knowledge bases. You’ll implement Retrieval Augmented Generation (RAG) with LLMs to improve the accuracy of your models and gain greater control over LLM outputs.Dive into generative vision transformers and multimodal model architectures and build applications, such as image and video-to-text classifiers. Go further by combining different models and platforms and learning about AI agent replication.This book provides you with an understanding of transformer architectures, pretraining, fine-tuning, LLM use cases, and best practices.What you will learnBreakdown and understand the architectures of the Original Transformer, BERT, GPT models, T5, PaLM, ViT, CLIP, and DALL-EFine-tune BERT, GPT, and PaLM 2 modelsLearn about different tokenizers and the best practices for preprocessing language dataPretrain a RoBERTa model from scratchImplement retrieval augmented generation and rules bases to mitigate hallucinationsVisualize transformer model activity for deeper insights using BertViz, LIME, and SHAPGo in-depth into vision transformers with CLIP, DALL-E 2, DALL-E 3, and GPT-4VWho this book is forThis book is ideal for NLP and CV engineers, software developers, data scientists, machine learning engineers, and technical leaders looking to advance their LLMs and generative AI skills or explore the latest trends in the field.Knowledge of Python and machine learning concepts is required to fully understand the use cases and code examples. However, with examples using LLM user interfaces, prompt engineering, and no-code model building, this book is great for anyone curious about the AI revolution.Table of ContentsWhat are Transformers?Getting Started with the Architecture of the Transformer ModelEmergent vs Downstream Tasks: The Unseen Depths of TransformersAdvancements in Translations with Google Trax, Google Translate, and GeminiDiving into Fine-Tuning through BERTPretraining a Transformer from Scratch through RoBERTaThe Generative AI Revolution with ChatGPTFine-Tuning OpenAI GPT ModelsShattering the Black Box with Interpretable ToolsInvestigating the Role of Tokenizers in Shaping Transformer Models(N.B. Please use the Read Sample option to see further chapters) Read more

Do you know C++ but not AI? Do you dream of writing your own Generative AI engine in C++? From beginner to advanced, this book covers the internals of GPT-style Transformer engines and Large Language Models (LLMs) in C++, with source code examples and research paper citations.Key FeaturesTransformer components in C++Faster and smarter AIOpen source LLMsAdvanced software developmentCutting-edge research optimizationsJust C++ code without all the mathResearch papers literature surveyTable of ContentsPart I: AI Projects in C++1. Introduction to AI in C++2. Transformers & LLMs3. AI Phones4. AI on Your Desktop5. Design Choices & Architectures6. Training, Fine-Tuning & RAG7. Deployment ArchitecturePart II: Basic C++ Optimizations8. Bitwise Operations9. Floating Point Arithmetic10. Arithmetic Optimizations11. Compile-Time Optimizations12. Pointer Arithmetic13. Algorithm Speedups14. Memory OptimizationsPart III: Parallel C++ Optimizations15. Loop Vectorization16. Hardware Acceleration17. AVX Intrinsics18. Parallel Data StructuresPart IV: Transformer Components in C++19. Encoders & Decoders20. Attention21. Activation Functions22. Vector Algorithms23. Tensors24. Normalization25. Softmax26. Decoding Algorithms27. Tokenizer and VocabularyPart V: Optimizing Transformers in C++28. Deslugging AI Engines29. Caching Optimizations30. Vectorization31. Kernel Fusion32. Quantization33. Pruning34. MatMul/GEMM35. Lookup Tables & Precomputation36. AI Memory OptimizationsPart VI: Enterprise AI in C++37. Tuning, Profiling & Benchmarking38. Platform Portability39. Quality40. Reliability41. Self-Testing Code42. DebuggingPart VII: Research on AI Optimization43. Overview of AI Research44. Advanced Quantization45. Knowledge Distillation46. Structured Pruning47. Early Exit and Layer Pruning48. Width Pruning49. Length Pruning50. Adaptive Inference51. Zero-Multiplication Models52. Logarithmic Models53. Arithmetic Optimization Research54. Ensemble Multi-Model Architectures55. Advanced Number Systems56. Neural Architecture SearchAppendix 1: C++ Slug Catalog Read more

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Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation.With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI.Learn how to empower AI to work for you. This book explains:The structure of the interaction chain of your program's AI model and the fine-grained steps in betweenHow AI model requests arise from transforming the application problem into a document completion problem in the model training domainThe influence of LLM and diffusion model architecture—and how to best interact with itHow these principles apply in practice in the domains of natural language processing, text and image generation, and code Read more

Every year, an estimated 10,000 to 15,000 people apply to Canadian law schools, vying for just over 2,000 coveted spots. The competition is even fiercer when applying for a law job. Adam Letourneau, QC, graduate of the University of Alberta Faculty of Law, recipient of a post-graduate LLM degree from University College London and Queen Mary University London Faculty of Laws, former Bencher of the Law Society of Alberta, former Co-Editor-in-Chief of the Alberta Law Review and Managing Partner of LETOURNEAU LLP, reveals in this 3rd Edition many insider tips on how to gain admittance to law school in Canada, how to cope and succeed in law school, and most importantly, how to land a coveted law job post-graduation. Drawing upon personal experience and the experiences of numerous Canadian law school graduates, Adam Letourneau, QC shares, in the 3rd edition, insights on the LSAT, applying for law school, study strategies, summer jobs, the articling application process, succeeding as a lawyer and much more. Letourneau, along with new co-author Mitchell Heyland, will save you hours of research, hours of study and tons of stress. Including updated admissions information, what being a lawyer is like, salary updates and more.

Python is an excellent way to get started in programming, and this clear, concise guide walks you through Python a step at a time—beginning with basic programming concepts before moving on to functions, data structures, and object-oriented design. This revised third edition reflects the growing role of large language models (LLMs) in programming and includes exercises on effective LLM prompts, testing code, and debugging skills.With this popular hands-on guide at your side, you'll get:A grounding in the syntax and semantics of the Python languageA clear definition of each programming concept, with emphasis on clear vocabularyHow to work with variables, statements, functions, and data structures in a logical progressionTechniques for reading and writing files and databasesA solid understanding of objects, methods, and object-oriented programmingDebugging strategies for syntax, runtime, and semantic errorsAn introduction to recursion, interface design, data structures, and basic algorithmsHow to use LLMs—including effective prompts, testing code, and debuggingAnd more Read more

Gain well-rounded knowledge of AI methods in cybersecurity and obtain hands-on experience in implementing them to bring value to your organizationKey FeaturesFamiliarize yourself with AI methods and approaches and see how they fit into cybersecurityLearn how to design solutions in cybersecurity that include AI as a key featureAcquire practical AI skills using step-by-step exercises and code examplesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionArtificial intelligence offers data analytics methods that enable us to efficiently recognize patterns in large-scale data. These methods can be applied to various cybersecurity problems, from authentication and the detection of various types of cyberattacks in computer networks to the analysis of malicious executables.Written by a machine learning expert, this book introduces you to the data analytics environment in cybersecurity and shows you where AI methods will fit in your cybersecurity projects. The chapters share an in-depth explanation of the AI methods along with tools that can be used to apply these methods, as well as design and implement AI solutions. You’ll also examine various cybersecurity scenarios where AI methods are applicable, including exercises and code examples that’ll help you effectively apply AI to work on cybersecurity challenges. The book also discusses common pitfalls from real-world applications of AI in cybersecurity issues and teaches you how to tackle them.By the end of this book, you’ll be able to not only recognize where AI methods can be applied, but also design and execute efficient solutions using AI methods.What you will learnRecognize AI as a powerful tool for intelligence analysis of cybersecurity dataExplore all the components and workflow of an AI solutionFind out how to design an AI-based solution for cybersecurityDiscover how to test various AI-based cybersecurity solutionsEvaluate your AI solution and describe its advantages to your organizationAvoid common pitfalls and difficulties when implementing AI solutionsWho this book is forThis book is for machine learning practitioners looking to apply their skills to overcome cybersecurity challenges. Cybersecurity workers who want to leverage machine learning methods will also find this book helpful. Fundamental concepts of machine learning and beginner-level knowledge of Python programming are needed to understand the concepts present in this book. Whether you’re a student or an experienced professional, this book offers a unique and valuable learning experience that will enable you to protect your network and data against the ever-evolving threat landscape.Table of ContentsBig Data in CybersecurityAutomation in CybersecurityCybersecurity Data AnalyticsAI, Machine Learning, and Statistics - A TaxonomyAI Problems and MethodsWorkflow, Tools, and Libraries in AI ProjectsMalware and Network Intrusion Detection and AnalysisUser and Entity Behavior AnalysisFraud, Spam, and Phishing DetectionUser Authentication and Access ControlThreat IntelligenceAnomaly Detection in Industrial Control SystemsLarge Language Models and CybersecurityData Quality and Its Usage in the AI and LLM EraCorrelation, Causation, Bias, and VarianceEvaluation, Monitoring, and Feedback LoopLearning in a Changing and Adversarial EnvironmentPrivacy, Accountability, Explainability, and Trust - Responsible AISummary Read more

Are you an entrepreneur or business leader, feeling overwhelmed by AI assistants, chatbots, machine learning, Agentic AI and the surge of Large Language Models, but don’t know how to make them work for your business? Missing out on AI and AI agents can mean lost opportunities, slower growth, and confusion in a changing market. What if you could integrate these cutting-edge tools into your business without draining your budget or getting lost in technical details?What if you could have repetitive and time-consuming tasks managed by Artificial Intelligence—freeing you, your team and your business to focus on innovation, strategic thinking, and next-level customer experiences?Whether you’re a total beginner or already know some AI basics but haven’t explored AI agents deeply, this book will guide you. Discover how AI agents can boost your daily operations, sharpen your leadership approaches, and transform your entire business. In this book, you’ll discover a straightforward path to adopting AI agents for leaders—finally empowering you to streamline workflows, boost customer satisfaction, and reclaim precious time.You will learn how to:Pick the right AI for business tools - such as AI assistants, chatbots, or LLM-based solutions to meet your specific goals, as you build a foundation understanding of machine learning.Map out a custom roadmap for deploying AI agents, from small pilot programs to larger-scale integrations, without massive disruptions or technical headaches.Leverage Agentic AI by mastering best practices in data handling, ethical considerations, and automated workflows.You’ll also find:Real-world case studies showcasing how entrepreneurs and business leaders successfully integrated artificial intelligence to streamline daily tasks.Step-by-step frameworks that align AI assistants, chatbots, and LLMs with your company culture and leadership style, ensuring smooth adoption.Easy methods for measuring ROI, helping you confidently expand your machine learning initiatives and prove the value of your investment.Long-term, actionable strategies to keep both beginners and experienced leaders agile in an ever-evolving AI landscape, so you can continue to grow and stay ahead of new opportunities and challenges.Plus you’ll receive free access to an exclusive chatbot agent designed by the author of this book to reinforce your learning experience. This bonus resource lets you test your knowledge, explore deeper insights from the book, and expand your understanding of AI agents—for free, no extra cost.Written by Narciso Silva, a veteran AI and digital transformation consultant, with over a decade of experience guiding companies in adopting AI for business success, this book combines proven strategies and encouraging success stories.Are you ready to lead the next wave of AI innovation in your business? Don’t let confusion around AI assistants, chatbots, or Agentic AI stand in your way. It’s time to embrace AI agents, transform your operations, and seize the competitive edge you deserve.Start your journey now and unlock a future of limitless growth and possibility! Read more

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2024 Edition – Get to grips with the LangChain framework to develop production-ready applications, including agents and personal assistants. The 2024 edition features updated code examples and an improved GitHub repository.Purchase of the print or Kindle book includes a free PDF eBook.Key FeaturesLearn how to leverage LangChain to work around LLMs’ inherent weaknessesDelve into LLMs with LangChain and explore their fundamentals, ethical dimensions, and application challengesGet better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into realityBook DescriptionChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Gemini. It demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis – illustrating the expansive utility of LLMs in real-world applications.Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.What you will learnCreate LLM apps with LangChain, like question-answering systems and chatbotsUnderstand transformer models and attention mechanismsAutomate data analysis and visualization using pandas and PythonGrasp prompt engineering to improve performanceFine-tune LLMs and get to know the tools to unleash their powerDeploy LLMs as a service with LangChain and apply evaluation strategiesPrivately interact with documents using open-source LLMs to prevent data leaksWho this book is forThe book is for developers, researchers, and anyone interested in learning more about LangChain. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs using LangChain.Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily.Table of ContentsWhat Is Generative AI?LangChain for LLM AppsGetting Started with LangChainBuilding Capable AssistantsBuilding a Chatbot like ChatGPTDeveloping Software with Generative AILLMs for Data ScienceCustomizing LLMs and Their OutputGenerative AI in ProductionThe Future of Generative Models Read more

Explore Generative AI, the engine behind ChatGPT, and delve into topics like LLM-infused frameworks, autonomous agents, and responsible innovation, to gain valuable insights into the future of AI Key FeaturesGain foundational GenAI knowledge and understand how to scale GenAI/ChatGPT in the cloudUnderstand advanced techniques for customizing LLMs for organizations via fine-tuning, prompt engineering, and responsible AIPeek into the future to explore emerging trends like multimodal AI and autonomous agentsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGenerative artificial intelligence technologies and services, including ChatGPT, are transforming our work, life, and communication landscapes. To thrive in this new era, harnessing the full potential of these technologies is crucial. Generative AI for Cloud Solutions is a comprehensive guide to understanding and using Generative AI within cloud platforms.This book covers the basics of cloud computing and Generative AI/ChatGPT, addressing scaling strategies and security concerns. With its help, you’ll be able to apply responsible AI practices and other methods such as fine-tuning, RAG, autonomous agents, LLMOps, and Assistants APIs. As you progress, you’ll learn how to design and implement secure and scalable ChatGPT solutions on the cloud, while also gaining insights into the foundations of building conversational AI, such as chatbots. This process will help you customize your AI applications to suit your specific requirements.By the end of this book, you’ll have gained a solid understanding of the capabilities of Generative AI and cloud computing, empowering you to develop efficient and ethical AI solutions for a variety of applications and services.What you will learnGet started with the essentials of generative AI, LLMs, and ChatGPT, and understand how they function togetherUnderstand how we started applying NLP to concepts like transformersGrasp the process of fine-tuning and developing apps based on RAGExplore effective prompt engineering strategiesAcquire insights into the app development frameworks and lifecycles of LLMs, including important aspects of LLMOps, autonomous agents, and Assistants APIsDiscover how to scale and secure GenAI systems, while understanding the principles of responsible AIWho this book is forThis artificial intelligence book is for aspiring cloud architects, data analysts, cloud developers, data scientists, AI researchers, technical business leaders, and technology evangelists looking to understanding the interplay between GenAI and cloud computing. Some chapters provide a broad overview of GenAI, which are suitable for readers with basic to no prior AI experience, aspiring to harness AI's potential. Other chapters delve into technical concepts that require intermediate data and AI skills. A basic understanding of a cloud ecosystem is required to get the most out of this book. Table of ContentsCloud Computing Meets Generative AI: Bridging Infinite ImpossibilitiesNLP Evolution and Transformers: Exploring NLPs and LLMsFine Tuning: Building Domain Specific LLM ApplicationsRAGs to Riches: Elevating AI with External DataEffective Prompt Engineering Strategies: Unlocking Wisdom Through AIDeveloping and Operationalizing LLM-Based Cloud Applications: Exploring Dev Frameworks and LLMOpsDeploying ChatGPT in the Cloud: Architecture Design and Scaling StrategiesSecurity and Privacy Considerations for Gen AI: Building Safe and Secure LLMs(N.B. Please use the Read Sample option to see further chapters) Read more

Get to grips with the LangGraph framework from theory to production-ready applications. Code examples are regularly updated to keep you abreast of the latest LangGraph and LangChain changes.Purchase of the print or Kindle book includes a free PDF eBook and all code samples. AI agents are here to stay, and LangGraph makes it easy to build them and harness their capabilities!Today’s Large Language Models (LLMs) are impressive, but let’s be real. These LLMs are still primarily just sophisticated text processors.AI Agents are LLMs on steroids. The anatomy of an agent consists of:AI_AGENT = LLM + MEMORY + TOOLS + PLANNING + DO_WHILE_LOOPThe LangGraph framework is an excellent tool for implementing and orchestrating all of these components.Simply put, AI agents are LLMs with tools, that operate in a loop to accomplish specific goals.You can assign them tasks such as:a calendar manager to schedule and manage appointments, send reminders, and suggest optimal meeting timesa personal AI Agent assistant to organize trips, book hotels and plane tickets for a destination in a given budget.These are much closer to the capabilities we expect from real AI.AI Agents can solve very complex situations thus clear communication is essential to achieve the desired results.This is where LangGraph excels. Communicating with LLMs via code leads to far more reliable results than using only natural language (prompt engineering).In this book, we’ll take you on a fun, hands-on journey where each chapter will focus on essential concepts like tool management or human in the loop validation, while also coding practical implementations of these elements using LangGraph.You can see this book as your launchpad. You'll build your first AI Agent within minutes, and slowly become competent and knowledgeable in this technology, with each chapter featuring a full practical example.Let's start learning! Read more

Minimize AI hallucinations and build accurate, custom generative AI pipelines with RAG using embedded vector databases and integrated human feedbackPurchase of the print or Kindle book includes a free eBook in PDF formatKey FeaturesImplement RAG’s traceable outputs, linking each response to its source document to build reliable multimodal conversational agentsDeliver accurate generative AI models in pipelines integrating RAG, real-time human feedback improvements, and knowledge graphsBalance cost and performance between dynamic retrieval datasets and fine-tuning static dataBook DescriptionRAG-Driven Generative AI provides a roadmap for building effective LLM, computer vision, and generative AI systems that balance performance and costs.This book offers a detailed exploration of RAG and how to design, manage, and control multimodal AI pipelines. By connecting outputs to traceable source documents, RAG improves output accuracy and contextual relevance, offering a dynamic approach to managing large volumes of information. This AI book shows you how to build a RAG framework, providing practical knowledge on vector stores, chunking, indexing, and ranking. You’ll discover techniques to optimize your project’s performance and better understand your data, including using adaptive RAG and human feedback to refine retrieval accuracy, balancing RAG with fine-tuning, implementing dynamic RAG to enhance real-time decision-making, and visualizing complex data with knowledge graphs.You’ll be exposed to a hands-on blend of frameworks like LlamaIndex and Deep Lake, vector databases such as Pinecone and Chroma, and models from Hugging Face and OpenAI. By the end of this book, you will have acquired the skills to implement intelligent solutions, keeping you competitive in fields from production to customer service across any project.What you will learnScale RAG pipelines to handle large datasets efficientlyEmploy techniques that minimize hallucinations and ensure accurate responsesImplement indexing techniques to improve AI accuracy with traceable and transparent outputsCustomize and scale RAG-driven generative AI systems across domainsFind out how to use Deep Lake and Pinecone for efficient and fast data retrievalControl and build robust generative AI systems grounded in real-world dataCombine text and image data for richer, more informative AI responsesWho this book is forThis book is ideal for data scientists, AI engineers, machine learning engineers, and MLOps engineers. If you are a solutions architect, software developer, product manager, or project manager looking to enhance the decision-making process of building RAG applications, then you’ll find this book useful.Table of ContentsWhy Retrieval Augmented Generation?RAG Embedding Vector Stores with Deep Lake and OpenAIBuilding Index-Based RAG with LlamaIndex, Deep Lake, and OpenAIMultimodal Modular RAG for Drone TechnologyBoosting RAG Performance with Expert Human FeedbackScaling RAG Bank Customer Data with PineconeBuilding Scalable Knowledge-Graph-Based RAG with Wikipedia API and LlamaIndexDynamic RAG with Chroma and Hugging Face LlamaEmpowering AI Models: Fine-Tuning RAG Data and Human FeedbackRAG for Video Stock Production with Pinecone and OpenAI Read more

Master GenAI techniques to create images and text using variational autoencoders (VAEs), generative adversarial networks (GANs), LSTMs, and large language models (LLMs)Key FeaturesImplement real-world applications of LLMs and generative AIFine-tune models with PEFT and LoRA to speed up trainingExpand your LLM toolbox with Retrieval Augmented Generation (RAG) techniques, LangChain, and LlamaIndexPurchase of the print or Kindle book includes a free eBook in PDF formatBook DescriptionBecome an expert in Generative AI through immersive, hands-on projects that leverage today’s most powerful models for Natural Language Processing (NLP) and computer vision. Generative AI with Python and PyTorch is your end-to-end guide to creating advanced AI applications, made easy by Raghav Bali, a seasoned data scientist with multiple patents in AI, and Joseph Babcock, a PhD and machine learning expert. Through business-tested approaches, this book simplifies complex GenAI concepts, making learning both accessible and immediately applicable.From NLP to image generation, this second edition explores practical applications and the underlying theories that power these technologies. By integrating the latest advancements in LLMs, it prepares you to design and implement powerful AI systems that transform data into actionable intelligence.You’ll build your versatile LLM toolkit by gaining expertise in GPT-4, LangChain, RLHF, LoRA, RAG, and more. You’ll also explore deep learning techniques for image generation and apply styler transfer using GANs, before advancing to implement CLIP and diffusion models.Whether you’re generating dynamic content or developing complex AI-driven solutions, this book equips you with everything you need to harness the full transformative power of Python and AI.What you will learnGrasp the core concepts behind large language models and their capabilitiesCraft effective prompts using chain-of-thought, ReAct, and prompt query language to guide LLMs toward your desired outputsUnderstand how attention and transformers have changed NLPOptimize your diffusion models by combining them with VAEsBuild text generation pipelines based on LSTMs and LLMsLeverage the power of open-source LLMs, such as Llama and Mistral, for diverse applicationsWho this book is forThis book is for data scientists, machine learning engineers, and software developers seeking practical skills in building generative AI systems. A basic understanding of math and statistics and experience with Python coding is required.Table of ContentsIntroduction to Generative AI: Drawing Data from ModelsBuilding Blocks of Deep Neural NetworksThe Rise of Methods for Text GenerationNLP 2.0: Using Transformers to Generate TextLLM FoundationsOpen-Source LLMsPrompt EngineeringLLM ToolboxLLM Optimization TechniquesEmerging Applications in Generative AINeural Networks Using VAEsImage Generation with GANsStyle Transfer with GANsDeepfakes with GANs Diffusion Models and AI Art Read more

Elevate your career by mastering key .NET tools and skills, including debugging, source code management, testing, cloud-native development, intelligent apps and more.Purchase of the print or Kindle book includes a free PDF eBook.Key FeaturesCoverage of key .NET tools and skills including refactoring, source code management, debugging, memory troubleshooting, and morePractical guidance on using code editors effectively, implementing best practices, and protecting dataExplore cutting-edge techniques like building intelligent apps, cloud native development with .NET Aspire, and Docker containerizationBook DescriptionUnlock the full potential of .NET development with Tools and Skills for .NET 8.Dive into source code management using Git and learn how to navigate projects while ensuring version control. Discover advanced debugging techniques and troubleshooting strategies to identify and resolve issues, and gain practical insights on documenting your code, APIs, and services, fostering project clarity and maintainability. Delve into the world of cryptography, ensuring confidentiality and integrity throughout your development lifecycle.Elevate your skills as you explore cutting-edge topics such as building intelligent apps using custom LLM-based chat services, mastering dependency injection, optimizing performance through testing, and Docker containerization. Harness the power of cloud-native development with .NET Aspire, unlocking the benefits of modern cloud platforms. With guidance on software architecture best practices, this book empowers you to build robust, scalable and maintainable applications.Advance your career with invaluable insights on job readiness and interview preparation, positioning yourself as a top-tier candidate in today's competitive job market.Whether you're a seasoned .NET professional or an aspiring developer looking to enhance your skills, this book is your ultimate companion on the journey to .NET mastery.What you will learnMake the most of code editor tools for efficient developmentLearn advanced debugging techniques and troubleshooting strategiesUnderstand how to protect data and applications using cryptographyBuild a custom LLM-based chat serviceDiscover how to master dependency injectionOptimize performance through benchmarking and testingDelve into cloud-native development using .NET AspireAdvance your career with advice on job readiness and interviewsWho this book is for.NET professionals seeking to enhance their expertise, as well as aspiring developers aiming to advance their careers in the field. This book caters to individuals eager to master essential .NET tools, refine their development practices, explore advanced techniques and cutting-edge tools, and prepare themselves for job opportunities and interviews in the competitive landscape of .NET developmentTable of ContentsIntroducing Tools and Skills for .NETMaking the Most of the Tools in Your Code EditorSource Code Management Using GitDebugging and Memory TroubleshootingAlerts, Metrics, and Logging for ObservabilityDocumenting Code, APIs, and ServicesObserving and Modifying Code Execution DynamicallyProtecting Data and Apps Using CryptographyBuilding a Custom LLM-Based Chat ServiceDependency Injection, Containers, and Service LifetimeUnit Testing and MockingIntegration and Security TestingBenchmarking Performance, Load, and Stress Testing(N.B. Please use the Read Sample option to see further chapters) Read more