Unlock the potential of Generative AI with this Large Language Model production-ready playbook for seamless deployment, optimization, and scaling. This hands-on guide takes you beyond theory, offering expert strategies for integrating LLMs into real-world applications using retrieval-augmented generation (RAG), vector databases, PEFT, LoRA, and scalable inference architectures. Whether you're an ML engineer, data scientist, or MLOps practitioner, you’ll gain the technical know-how to operationalize LLMs efficiently, reduce compute costs, and ensure rock-solid reliability in production.What You’ll Learn: Master LLM Fundamentals – Understand tokenization, transformer architectures, and the evolution linguistics to the creation of foundation models. RAG & Vector Databases – Augment model capabilities with real-time retrieval and memory-optimized embeddings. Training vs Fine-tuning – Learn how to train your own model as well as cutting edge techniques like Distillation, RLHF, PEFT, LoRA, and QLoRA for cost-effective adaptation. Prompt Engineering – Discover the quickly evolving world of prompt engineering and go beyond simple prompt and pray methods and learn how to implement structured outputs, complex workflows, and LLM agents. Scaling & Cost Optimization – Deploy LLMs into your favorite cloud of choice, on commodity hardware, Kubernetes clusters, and edge devices. Securing AI Workflows – Implement guardrails for hallucination mitigation, adversarial testing, and compliance monitoring. MLOps for LLMs – Learn all about LLMOps, automate model lifecycle management, retraining pipelines, and continuous evaluation.Hands-on Projects Include: Training a custom LLM from scratch – Build and optimize an industry-specific model. AI-Powered VSCode Extension – Use LLMs to enhance developer productivity with intelligent code completion. Deploying on Edge Devices – Run a lightweight LLM on a Raspberry Pi or Jetson Nano for real-world AI applications. 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

“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

HARNESS THE FULL POTENTIAL OF GENERATIVE AI TO IMPROVE YOUR PRODUCTIVITY, TRANSFORM YOUR CAREER, AND BOOST YOUR CREATIVITY—EVEN IF YOU’RE JUST STARTING. NO TECH BACKGROUND NEEDED!Are you:…eager to learn how to get the most out of AI tools for your career & creative pursuits?…overwhelmed by complex AI terminologies like neural networks, large language models, and prompt engineering? …worried about falling behind in an ever-evolving tech landscape?…concerned about AI’s potential impact on your work, everyday life & society?…looking to protect yourself against misinformation, disinformation, scams, and other harms caused by the misuse of AI?…excited to quickly and easily move from novice to proficient by learning the basics and going beyond? If any of this resonates, you’re not alone. Generative AI is changing industries, jobs, productivity, and creativity—and the best time to learn is now.Why this book? Brought to you by the Amazon bestselling author of Essentials of AI for Beginners, this expertly crafted, comprehensive guide makes learning generative AI simple, engaging, and fun—whether you're a beginner or have experience, no matter your background or career path.What makes this book stand out:In-depth yet accessible: Learn generative AI in clear, easy-to-understand terms, starting with the basics and moving toward more advanced topics.Unique, relatable, real-world examples: Discover how generative AI transforms industries with compelling use cases from diverse fields.A balanced perspective: Explore the potential and pitfalls of generative AI for a well-rounded understanding of this technology.Hands-on learning: Follow step-by-step tutorials and activities that take you from beginner to proficient with practical skills.No tech background needed: Get started with zero prior experience and gain confidence as you progress.Five custom learning tracks: Choose from carefully designed pathways that help you focus on what matters most to you.Bonus material: Access exclusive activities, extra chapters, and curated resources for continued learning and career growth.Here's a preview of what’s inside:9 Foundational Concepts in Generative AI: Understand AI types, neural networks, generative models, and much more!13 Prompt Engineering Essentials: Learn proven techniques to harness AI’s full potential.30+ Tools of the Trade: Explore AI tools for generating text, code, images, videos, and audio.17+ Real-World Applications: See how AI transforms industries—from cultural preservation and urban design to drug discovery and more!3 Exclusive Bonus Chapters: Appreciate the future-ready skills needed to thrive in an AI-driven world and gain valuable career insights.16 Hands-On Learning Resources: Apply concepts with engaging activities and quizzes.10 Emerging Trends in Generative AI: Stay ahead with expert insights into AI’s future.Beyond the Basics: Explore advanced topics like retrieval-augmented generation (RAG), multimodal AI, and privacy optimization.16+ Ethical Considerations & Societal Impacts: Explore issues like bias, misinformation, and the risks of AI being misaligned with human values.AI Literacy & Critical Thinking: Learn to detect AI-generated content and approach AI with awareness.Responsible AI Initiatives: Discover efforts to ensure safe and ethical AI development.Ready to learn generative AI quickly & easily? Click “Add to Cart” and dive into this exciting topic today! Read more

Large language models (LLMs) have proven themselves to be powerful tools for solving a wide range of tasks, and enterprises have taken note. But transitioning from demos and prototypes to full-fledged applications can be difficult. This book helps close that gap, providing the tools, techniques, and playbooks that practitioners need to build useful products that incorporate the power of language models. Experienced ML researcher Suhas Pai offers practical advice on harnessing LLMs for your use cases and dealing with commonly observed failure modes. You’ll take a comprehensive deep dive into the ingredients that make up a language model, explore various techniques for customizing them such as fine-tuning, learn about application paradigms like RAG (retrieval-augmented generation) and agents, and more. Understand how to prepare datasets for training and fine-tuning Develop an intuition about the Transformer architecture and its variants Adapt pretrained language models to your own domain and use cases Learn effective techniques for fine-tuning, domain adaptation, and inference optimization Interface language models with external tools and data and integrate them into an existing software ecosystem Read more

Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. Apply generative AI to your business use casesDetermine which generative AI models are best suited to your task Perform prompt engineering and in-context learningFine-tune generative AI models on your datasets with low-rank adaptation (LoRA)Align generative AI models to human values with reinforcement learning from human feedback (RLHF)Augment your model with retrieval-augmented generation (RAG)Explore libraries such as LangChain and ReAct to develop agents and actionsBuild generative AI applications with Amazon Bedrock Read more

Your Playbook for Demystifying Generative AI, Empowering Employees, and Building an Intelligent Knowledge EcosystemGenerative AI is transforming industries, but are massive tech investments delivering promised results? Too often, the answer is no. The critical missing piece? Empowering your people. GenAI Ready: Educate, Orchestrate, Accelerate provides a comprehensive, actionable playbook for organizations aiming to harness the power of GenAI effectively and responsibly.Authored in April 2025 amidst rapid technological advancements, this book tackles the core challenges hindering AI adoption: the productivity paradox of investing in tech without people, the high cost of fragmented learning, and the struggle to access vital internal knowledge. It moves beyond generic tutorials to offer a strategic blueprint covering two crucial pillars:Strategic GenAI Education: Learn how to design, build, and scale a coordinated education program, starting with a foundational "GenAI 101" for all employees and progressing to tailored training for specific roles. Discover the Hub-and-Spoke model for efficient delivery, integrate essential responsible AI principles, and learn how to measure the true ROI of your L&D efforts.Intelligent Knowledge Orchestration: Explore the next frontier beyond basic chatbots. Understand the architecture (including RAG - Retrieval-Augmented Generation) and phased implementation of a GenAI Orchestration Layer – an intelligent engine connecting employees to verified internal knowledge precisely when needed, breaking down silos and scaling expertise.While specific technologies evolve, the core principles of strategic alignment, human-centric enablement, coordinated education, and intelligent knowledge access remain evergreen. This guide is essential for business leaders, L&D professionals, IT/AI managers, HR leaders, and consultants seeking to demystify GenAI, empower their workforce, mitigate risks, and build a truly intelligent, future-ready organization.Stop chasing AI hype and start building your GenAI-ready future. Get your playbook today! Read more

Apply cutting-edge machine learning techniques—from crowdsourced relevance and knowledge graph learning, to Large Language Models (LLMs)—to enhance the accuracy and relevance of your search results.Delivering effective search is one of the biggest challenges you can face as an engineer. AI-Powered Search is an in-depth guide to building intelligent search systems you can be proud of. It covers the critical tools you need to automate ongoing relevance improvements within your search applications. Inside you’ll learn modern, data-science-driven search techniques like: • Semantic search using dense vector embeddings from foundation models • Retrieval augmented generation (RAG) • Question answering and summarization combining search and LLMs • Fine-tuning transformer-based LLMs • Personalized search based on user signals and vector embeddings • Collecting user behavioral signals and building signals boosting models • Semantic knowledge graphs for domain-specific learning • Semantic query parsing, query-sense disambiguation, and query intent classification • Implementing machine-learned ranking models (Learning to Rank) • Building click models to automate machine-learned ranking • Generative search, hybrid search, multimodal search, and the search frontier AI-Powered Search will help you build the kind of highly intelligent search applications demanded by modern users. Whether you’re enhancing your existing search engine or building from scratch, you’ll learn how to deliver an AI-powered service that can continuously learn from every content update, user interaction, and the hidden semantic relationships in your content. You’ll learn both how to enhance your AI systems with search and how to integrate large language models (LLMs) and other foundation models to massively accelerate the capabilities of your search technology. Foreword by Grant Ingersoll. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Modern search is more than keyword matching. Much, much more. Search that learns from user interactions, interprets intent, and takes advantage of AI tools like large language models (LLMs) can deliver highly targeted and relevant results. This book shows you how to up your search game using state-of-the-art AI algorithms, techniques, and tools. About the book AI-Powered Search teaches you to create a search that understands natural language and improves automatically the more it is used. As you work through dozens of interesting and relevant examples, you’ll learn powerful AI-based techniques like semantic search on embeddings, question answering powered by LLMs, real-time personalization, and Retrieval Augmented Generation (RAG). What's inside • Sparse lexical and embedding-based semantic search • Question answering, RAG, and summarization using LLMs • Personalized search and signals boosting models • Learning to Rank, multimodal, and hybrid search About the reader For software developers and data scientists familiar with the basics of search engine technology. About the author Trey Grainger is the Founder of Searchkernel and former Chief Algorithms Officer and SVP of Engineering at Lucidworks. Doug Turnbull is a Principal Engineer at Reddit and former Staff Relevance Engineer at Spotify. Max Irwin is the Founder of Max.io and former Managing Consultant at OpenSource Connections. Table of Contents Part 1 1 Introducing AI-powered search 2 Working with natural language 3 Ranking and content-based relevance 4 Crowdsourced relevance Part 2 5 Knowledge graph learning 6 Using context to learn domain-specific language 7 Interpreting query intent through semantic search Part 3 8 Signals-boosting models 9 Personalized search 10 Learning to rank for generalizable search relevance 11 Automating learning to rank with click models 12 Overcoming ranking bias through active learning Part 4 13 Semantic search with dense vectors 14 Question answering with a fine-tuned large language model 15 Foundation models and emerging search paradigms A Running the code examples B Supported search engines and vector database Read more

Retrieval-Augmented Generation (RAG) 2.0 represents the next evolution in building intelligent, autonomous, and scalable AI systems. It combines the reasoning power of Large Language Models (LLMs) with real-time access to external knowledge via modular pipelines, vector databases, memory layers, and agentic orchestration. As the backbone of modern AI applications—from autonomous agents to enterprise-grade copilots—RAG 2.0 enables real-world deployment of systems that plan, retrieve, reason, and adapt intelligently.Written by expert AI systems architect Nathan Steele, this book blends advanced theory with real-world application. With hands-on code using LangChain, OpenAI, FAISS, Pinecone, ChromaDB, and LangGraph, it’s grounded in the latest research and field-tested practices powering autonomous AI frameworks across leading tech companies.Agentic AI with RAG 2.0 is your definitive, step-by-step guide to designing and deploying powerful modular AI systems that reason, retrieve, and act autonomously. From data preprocessing and vector store integration to prompt optimization and multi-agent collaboration, this book walks you through every layer of building scalable, high-performance agentic AI pipelines. Each chapter includes up-to-date tutorials, fully working examples, architectural best practices, and expert insights to help you build robust, production-ready systems with confidence.What’s Inside:Modular pipeline design using LangChain and LangGraphBuilding retrieval chains with FAISS, ChromaDB, PineconeVector search tuning and hybrid retrieval strategiesTool use, memory, and multi-step reasoning for autonomous agentsFine-tuning, hallucination mitigation, and prompt engineeringSecure deployment, logging, monitoring, and failure recoveryScaling infrastructure for high availability and cost optimizationComplete blueprints for deploying agentic RAG systems in real-world applicationsThis book is for machine learning engineers, AI developers, software architects, data scientists, and technical leaders looking to build cutting-edge AI systems with agentic behavior, modular pipelines, and scalable architecture. Whether you're deploying copilots, intelligent assistants, or autonomous systems, you’ll gain the skills to implement robust RAG-powered solutions.You don’t need to spend months reading research papers or debugging fragmented tutorials. This book distills years of expert practice into one coherent, actionable guide that saves you time and gets you building from day one. Learn not just the how, but also the why behind every architectural decision—quickly.Build smarter AI now.If you're serious about AI development, this is the most comprehensive book on Agentic RAG pipelines you'll find. Grab your copy of Agentic AI with RAG 2.0 today and start building autonomous, scalable AI systems that truly understand, reason, and act. 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

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

Discover a unique and fun approach to adopting modern typed functions programming patterns. This book uses playful metaphors and examples to help you learn Haskell through imagination, building on math without relying on imperative crutches or technical complexity. You’ll use math to build completely different Typed Functional patterns from the ground up and understand the link between building Mathematics through Types and constructing Haskell as a programming language. Intended for working with various applications, especially AI-powered apps, the book gently builds up to what are normally considered complex and difficult concepts all without needing a PhD to understand them. Illustrative explanations will guide you to tackle monads, using monad transformer stacks to structure real programs, foldable and traversable structures, as well as other Type classes. This book will also help you structure programs efficiently and apply your own abstractions to real-life problem domains. Next, you’ll explore exciting advancements in AI, including building with OpenAI APIs, creating a terminal chatbot, adding web functionality, and enhancing with retrieval-augmented generation. Finally, you’ll delve into AI multi-agents and future directions using Arrows abstraction, reinforcing Haskell’s design.Magical Haskell is a solution for programmers who feel limited by imperative programming languages but are also put off by excessively mathematical approaches.What You Will LearnGrasp a solid math foundation without complex technicalities for Types and Typeclasses.Solve problems via a typed functional approach and understand why it’s superior to what’s available in the imperative language world (“if it compiles, it runs”).Build your own abstractions to efficiently resolve problems in any given domain.Develop AI frameworks in Haskell, including chatbots, web functionality, and retrieval-augmented generation.Who This Book Is ForHaskell programmers of all levels and those interested in Type Theory. 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

Generative AI is transforming software development. Stay on the cutting edge with this guide to AI pair programming! Learn how to make the most of modern tools like ChatGPT and GitHub Copilot to improve your coding. Automate refactoring, debugging, and other tedious tasks, and use techniques such as prompt engineering and retrieval-augmented generation to get the code you need. Follow practical examples that show how you can program faster, more efficiently, and with fewer errors with the help of AI.Optimize your daily development tasks with AIWork with tools like GitHub Copilot, ChatGPT, and OpenHands to generate code, refactor programs, and debug scriptsExplore AI options for database design, unit testing, documentation, administration, and moreHype versus RealityLearn from expert developers to understand the possibilities (and pitfalls) of AI tools. With the help of practical code examples, see how to use AI helpers correctly to their full potential.Assistants for All TasksThis guide gives you an up-to-date overview of all aspects of AI-assisted coding: GitHub Copilot autocompletions, project bootstrapping with OpenHands, debugging and refactoring, application development, and more.In the Cloud or Local?Use OpenAI’s API to integrate AI models directly into your own scripts and automations—or use local large language models (LLMs) to work independently of cloud services.Structured programming with AIDebuggingUnit testingRefactoringAutomatic document creationDatabase development and designScripting and administrationLocal large language models (LLMs) Retrieval-augmented generation (RAG) ChatGPT and GitHub CopilotOpenHandsOpenAI API Read more

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 learningOver 200 datasets compiled in one place, covering everything from pre- training to multimodal tuning, providing a robust foundation for diverse LLM applicationsOver 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 deploymentOver 200 benchmarks covering LLM performance across various tasks, ethical considerations, multimodal applications, and more than 50 evaluation metrics for the LLM lifecycleNine 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 conceptsOver 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 Read more