Download or read book Mastering Large Language Models with Python written by Raj Arun R and published by Orange Education Pvt Ltd. This book was released on 2024-04-12 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Comprehensive Guide to Leverage Generative AI in the Modern Enterprise KEY FEATURES ● Gain a comprehensive understanding of LLMs within the framework of Generative AI, from foundational concepts to advanced applications. ● Dive into practical exercises and real-world applications, accompanied by detailed code walkthroughs in Python. ● Explore LLMOps with a dedicated focus on ensuring trustworthy AI and best practices for deploying, managing, and maintaining LLMs in enterprise settings. ● Prioritize the ethical and responsible use of LLMs, with an emphasis on building models that adhere to principles of fairness, transparency, and accountability, fostering trust in AI technologies. DESCRIPTION “Mastering Large Language Models with Python” is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects. Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation. Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of artificial intelligence. WHAT WILL YOU LEARN ● In-depth study of LLM architecture and its versatile applications across industries. ● Harness open-source and proprietary LLMs to craft innovative solutions. ● Implement LLM APIs for a wide range of tasks spanning natural language processing, audio analysis, and visual recognition. ● Optimize LLM deployment through techniques such as quantization and operational strategies like LLMOps, ensuring efficient and scalable model usage. ● Master prompt engineering techniques to fine-tune LLM outputs, enhancing quality and relevance for diverse use cases. ● Navigate the complex landscape of ethical AI development, prioritizing responsible practices to drive impactful technology adoption and advancement. WHO IS THIS BOOK FOR? This book is tailored for software engineers, data scientists, AI researchers, and technology leaders with a foundational understanding of machine learning concepts and programming. It's ideal for those looking to deepen their knowledge of Large Language Models and their practical applications in the field of AI. If you aim to explore LLMs extensively for implementing inventive solutions or spearheading AI-driven projects, this book is tailored to your needs. TABLE OF CONTENTS 1. The Basics of Large Language Models and Their Applications 2. Demystifying Open-Source Large Language Models 3. Closed-Source Large Language Models 4. LLM APIs for Various Large Language Model Tasks 5. Integrating Cohere API in Google Sheets 6. Dynamic Movie Recommendation Engine Using LLMs 7. Document-and Web-based QA Bots with Large Language Models 8. LLM Quantization Techniques and Implementation 9. Fine-tuning and Evaluation of LLMs 10. Recipes for Fine-Tuning and Evaluating LLMs 11. LLMOps - Operationalizing LLMs at Scale 12. Implementing LLMOps in Practice Using MLflow on Databricks 13. Mastering the Art of Prompt Engineering 14. Prompt Engineering Essentials and Design Patterns 15. Ethical Considerations and Regulatory Frameworks for LLMs 16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning) Index

Download or read book Mastering LLM Applications with LangChain and Hugging Face written by Hunaidkhan Pathan and published by BPB Publications. This book was released on 2024-09-21 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: DESCRIPTION The book is all about the basics of NLP, generative AI, and their specific component LLM. In this book, we have provided conceptual knowledge about different terminologies and concepts of NLP and NLG with practical hands-on. This comprehensive book offers a deep dive into the world of NLP and LLMs. Starting with the fundamentals of Python programming and code editors, the book gradually introduces NLP concepts, including text preprocessing, word embeddings, and transformer architectures. You will explore the architecture and capabilities of popular models like GPT-3 and BERT. The book also covers practical aspects of LLM usage for RAG applications using frameworks like LangChain and Hugging Face and deploying them in real world applications. With a focus on both theoretical knowledge and hands-on experience, this book is ideal for anyone looking to master the art of NLP and LLMs. The book also contains AWS Cloud deployment, which will help readers step into the world of cloud computing. As the book contains both theoretical and practical approaches, it will help the readers to gain confidence in the deployment of LLMs for any use cases, as well as get acquainted with the required generative AI knowledge to crack the interviews. KEY FEATURES ● Covers Python basics, NLP concepts, and terminologies, including LLM and RAG concepts. ● Provides exposure to LangChain, Hugging Face ecosystem, and chatbot creation using custom data. ● Guides on integrating chatbots with real-time applications and deploying them on AWS Cloud. WHAT YOU WILL LEARN ● Basics of Python, which contains Python concepts, installation, and code editors. ● Foundation of NLP and generative AI concepts and different terminologies being used in NLP and generative AI domain. ● LLMs and their importance in the cutting edge of AI. ● Creating chatbots using custom data using open source LLMs without spending a single penny. ● Integration of chatbots with real-world applications like Telegram. WHO THIS BOOK IS FOR This book is ideal for beginners and freshers entering the AI or ML field, as well as those at an intermediate level looking to deepen their understanding of generative AI, LLMs, and cloud deployment. TABLE OF CONTENTS 1. Introduction to Python and Code Editors 2. Installation of Python, Required Packages, and Code Editors 3. Ways to Run Python Scripts 4. Introduction to NLP and its Concepts 5. Introduction to Large Language Models 6. Introduction of LangChain, Usage and Importance 7. Introduction of Hugging Face, its Usage and Importance 8. Creating Chatbots Using Custom Data with LangChain and Hugging Face Hub 9. Hyperparameter Tuning and Fine Tuning Pre-Trained Models 10. Integrating LLMs into Real-World Applications–Case Studies 11. Deploying LLMs in Cloud Environments for Scalability 12. Future Directions: Advances in LLMs and Beyond Appendix A: Useful Tips for Efficient LLM Experimentation Appendix B: Resources and References

Download or read book Mastering Large Language Models written by Sanket Subhash Khandare and published by BPB Publications. This book was released on 2024-03-12 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do not just talk AI, build it: Your guide to LLM application development KEY FEATURES ● Explore NLP basics and LLM fundamentals, including essentials, challenges, and model types. ● Learn data handling and pre-processing techniques for efficient data management. ● Understand neural networks overview, including NN basics, RNNs, CNNs, and transformers. ● Strategies and examples for harnessing LLMs. DESCRIPTION Transform your business landscape with the formidable prowess of large language models (LLMs). The book provides you with practical insights, guiding you through conceiving, designing, and implementing impactful LLM-driven applications. This book explores NLP fundamentals like applications, evolution, components and language models. It teaches data pre-processing, neural networks , and specific architectures like RNNs, CNNs, and transformers. It tackles training challenges, advanced techniques such as GANs, meta-learning, and introduces top LLM models like GPT-3 and BERT. It also covers prompt engineering. Finally, it showcases LLM applications and emphasizes responsible development and deployment. With this book as your compass, you will navigate the ever-evolving landscape of LLM technology, staying ahead of the curve with the latest advancements and industry best practices. WHAT YOU WILL LEARN ● Grasp fundamentals of natural language processing (NLP) applications. ● Explore advanced architectures like transformers and their applications. ● Master techniques for training large language models effectively. ● Implement advanced strategies, such as meta-learning and self-supervised learning. ● Learn practical steps to build custom language model applications. WHO THIS BOOK IS FOR This book is tailored for those aiming to master large language models, including seasoned researchers, data scientists, developers, and practitioners in natural language processing (NLP). TABLE OF CONTENTS 1. Fundamentals of Natural Language Processing 2. Introduction to Language Models 3. Data Collection and Pre-processing for Language Modeling 4. Neural Networks in Language Modeling 5. Neural Network Architectures for Language Modeling 6. Transformer-based Models for Language Modeling 7. Training Large Language Models 8. Advanced Techniques for Language Modeling 9. Top Large Language Models 10. Building First LLM App 11. Applications of LLMs 12. Ethical Considerations 13. Prompt Engineering 14. Future of LLMs and Its Impact

Download or read book Mastering NLP from Foundations to LLMs written by Lior Gazit and published by Packt Publishing Ltd. This book was released on 2024-04-26 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Enhance your NLP proficiency with modern frameworks like LangChain, explore mathematical foundations and code samples, and gain expert insights into current and future trends Key Features Learn how to build Python-driven solutions with a focus on NLP, LLMs, RAGs, and GPT Master embedding techniques and machine learning principles for real-world applications Understand the mathematical foundations of NLP and deep learning designs Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDo you want to master Natural Language Processing (NLP) but don’t know where to begin? This book will give you the right head start. Written by leaders in machine learning and NLP, Mastering NLP from Foundations to LLMs provides an in-depth introduction to techniques. Starting with the mathematical foundations of machine learning (ML), you’ll gradually progress to advanced NLP applications such as large language models (LLMs) and AI applications. You’ll get to grips with linear algebra, optimization, probability, and statistics, which are essential for understanding and implementing machine learning and NLP algorithms. You’ll also explore general machine learning techniques and find out how they relate to NLP. Next, you’ll learn how to preprocess text data, explore methods for cleaning and preparing text for analysis, and understand how to do text classification. You’ll get all of this and more along with complete Python code samples. By the end of the book, the advanced topics of LLMs’ theory, design, and applications will be discussed along with the future trends in NLP, which will feature expert opinions. You’ll also get to strengthen your practical skills by working on sample real-world NLP business problems and solutions.What you will learn Master the mathematical foundations of machine learning and NLP Implement advanced techniques for preprocessing text data and analysis Design ML-NLP systems in Python Model and classify text using traditional machine learning and deep learning methods Understand the theory and design of LLMs and their implementation for various applications in AI Explore NLP insights, trends, and expert opinions on its future direction and potential Who this book is for This book is for deep learning and machine learning researchers, NLP practitioners, ML/NLP educators, and STEM students. Professionals working with text data as part of their projects will also find plenty of useful information in this book. Beginner-level familiarity with machine learning and a basic working knowledge of Python will help you get the best out of this book.

Download or read book Mastering LLMs and GPUs A Hands on Guide to Programming Optimization and Deployment written by Anand Vemula and published by Anand Vemula. This book was released on with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tired of slow AI? Want to build groundbreaking applications powered by language? This book is your key! Mastering LLMs and GPUs: A Hands-on Guide to Programming, Optimization, and Deployment equips you with the practical skills to leverage the revolutionary power of Large Language Models (LLMs) and Graphics Processing Units (GPUs). Inside, you'll discover: The Fundamentals: Demystify LLMs, grasp their architectures, and understand how they leverage massive data to generate human-quality text, translate languages, and answer your questions in an informative way. GPU Powerhouse: Unlock the secrets of GPUs, the processing engines that accelerate LLM training compared to traditional CPUs. Learn how to harness their parallel processing capabilities for lightning-fast results. Become an LLM Programming Pro: Code Like a Master: Dive into the world of LLM programming with essential tools and libraries like CUDA or OpenCL. Write code that effectively unleashes the parallel processing power of GPUs. Optimize for Peak Performance: Master memory management strategies to ensure data is readily available for faster processing. Explore techniques for fine-tuning pre-trained LLMs, specializing them for specific tasks and maximizing their effectiveness. Deploy Your LLM Creations: Real-World Applications: Learn to integrate your trained and optimized LLM into applications or cloud platforms, making it accessible for real-world use cases. Practical Considerations: Gain insights into resource management and performance monitoring techniques to keep your LLM running smoothly. Mastering LLMs and GPUs is your comprehensive guide to building powerful language models. With hands-on exercises, clear explanations, and practical advice, you'll be well on your way to developing groundbreaking AI applications that transform the way we interact with language.

Download or read book Mastering Prompt Engineering for LLMs Chain of Thought CoT Tree of thought ToT and Self reflection written by Anand Vemula and published by Anand Vemula. This book was released on with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mastering Prompt Engineering for LLMs: CoT, ToT, and Self-Reflection dives into the world of Large Language Models (LLMs) and explores a powerful technique called prompt engineering. This book equips you to unlock the true potential of LLMs by guiding them through complex tasks and reasoning processes. Part 1 lays the foundation by explaining how LLMs work and the importance of prompt design. It introduces Chain of Thought (CoT) prompting, a step-by-step approach for guiding LLMs through logical reasoning. Part 2 delves into Tree of Thought (ToT) prompting, enabling LLMs to explore multiple possibilities and weigh evidence before reaching a conclusion. You'll learn how to craft ToT prompts to tackle open-ended tasks and spark creative problem-solving. Part 3 introduces the concept of self-reflection in LLMs. By crafting prompts that encourage LLMs to analyze their reasoning process, you can enhance the accuracy, reliability, and trustworthiness of their outputs. Part 4 explores the exciting future of prompt engineering. It discusses emerging trends like few-shot learning prompts and interactive prompting techniques that allow for real-time adaptation during LLM interactions. You'll also delve into the ethical considerations of advanced prompt engineering, ensuring responsible use of this powerful technology. Through case studies, use cases, and clear explanations, this book empowers you to become a skilled prompt engineer, unlocking the full potential of LLMs in various fields, from scientific discovery and education to creative writing and marketing.

Download or read book Mastering Search Algorithms with Python written by Pooja Baraskar and published by BPB Publications. This book was released on 2024-07-20 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: DESCRIPTION In today's era of Artificial Intelligence and the vast expanse of big data, understanding how to effectively utilize search algorithms has become crucial. Every day, billions of searches happen online, influencing everything from social media recommendations to critical decisions in fields like finance and healthcare. Behind these seemingly straightforward searches are powerful algorithms that determine how information is discovered, organized, and applied, fundamentally shaping our digital interactions. This book covers various search algorithms, starting with linear and binary searches, analyzing their performance, and implementing them in Python. It progresses to graph traversal algorithms like DFS and BFS, including Python examples and explores the A* algorithm for optimal pathfinding. Advanced search techniques and optimization best practices are discussed, along with neural network applications like gradient descent. You will also learn to create interactive visualizations using Streamlit and explore real-world applications in gaming, logistics, and Machine Learning. By the end, readers will have a solid grasp of search algorithms, enabling them to implement them efficiently in Python and tackle complex search problems with ease. KEY FEATURES ● Comprehensive coverage of a wide range of search algorithms, from basic to advanced. ● Hands-on Python code examples for each algorithm, fostering practical learning. ● Insights into the real-world applications of each algorithm, preparing readers for real-world challenges. WHAT YOU WILL LEARN ● Understand basic to advanced search algorithms in Python that are crucial for information retrieval. ● Learn different search methods like binary search and A* search, and their pros and cons. ● Use Python’s visualization tools to see algorithms in action for better understanding. ● Enhance learning with practical examples, challenges, and solutions to boost programming skills. WHO THIS BOOK IS FOR This book is for software engineers, data scientists, and computer science students looking to master search algorithms with Python to optimize search algorithms in today's data-driven environments. TABLE OF CONTENTS 1. Introduction to Search Algorithms 2. Linear and Binary Search 3. Depth Search and Breadth First Search 4. Heuristic Search: Introducing A* Algorithm 5. Advanced Search Algorithms and Techniques 6. Optimizing and Benchmarking Search Algorithms 7. Search Algorithms for Neural Networks 8. Interactive Visualizations with Streamlit 9. Search Algorithms in Large Language Models 10. Diverse Landscape of Search Algorithms 11. Real World Applications of Search Algorithms

Download or read book Mastering Transformers written by Savaş Yıldırım and published by Packt Publishing Ltd. This book was released on 2024-06-03 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore transformer-based language models from BERT to GPT, delving into NLP and computer vision tasks, while tackling challenges effectively Key Features Understand the complexity of deep learning architecture and transformers architecture Create solutions to industrial natural language processing (NLP) and computer vision (CV) problems Explore challenges in the preparation process, such as problem and language-specific dataset transformation Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionTransformer-based language models such as BERT, T5, GPT, DALL-E, and ChatGPT have dominated NLP studies and become a new paradigm. Thanks to their accurate and fast fine-tuning capabilities, transformer-based language models have been able to outperform traditional machine learning-based approaches for many challenging natural language understanding (NLU) problems. Aside from NLP, a fast-growing area in multimodal learning and generative AI has recently been established, showing promising results. Mastering Transformers will help you understand and implement multimodal solutions, including text-to-image. Computer vision solutions that are based on transformers are also explained in the book. You’ll get started by understanding various transformer models before learning how to train different autoregressive language models such as GPT and XLNet. The book will also get you up to speed with boosting model performance, as well as tracking model training using the TensorBoard toolkit. In the later chapters, you’ll focus on using vision transformers to solve computer vision problems. Finally, you’ll discover how to harness the power of transformers to model time series data and for predicting. By the end of this transformers book, you’ll have an understanding of transformer models and how to use them to solve challenges in NLP and CV.What you will learn Focus on solving simple-to-complex NLP problems with Python Discover how to solve classification/regression problems with traditional NLP approaches Train a language model and explore how to fine-tune models to the downstream tasks Understand how to use transformers for generative AI and computer vision tasks Build transformer-based NLP apps with the Python transformers library Focus on language generation such as machine translation and conversational AI in any language Speed up transformer model inference to reduce latency Who this book is for This book is for deep learning researchers, hands-on practitioners, and ML/NLP researchers. Educators, as well as students who have a good command of programming subjects, knowledge in the field of machine learning and artificial intelligence, and who want to develop apps in the field of NLP as well as multimodal tasks will also benefit from this book’s hands-on approach. Knowledge of Python (or any programming language) and machine learning literature, as well as a basic understanding of computer science, are required.

Download or read book Mastering Data Engineering and Analytics with Databricks written by Manoj Kumar and published by Orange Education Pvt Ltd. This book was released on 2024-09-30 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: TAGLINE Master Databricks to Transform Data into Strategic Insights for Tomorrow’s Business Challenges KEY FEATURES ● Combines theory with practical steps to master Databricks, Delta Lake, and MLflow. ● Real-world examples from FMCG and CPG sectors demonstrate Databricks in action. ● Covers real-time data processing, ML integration, and CI/CD for scalable pipelines. ● Offers proven strategies to optimize workflows and avoid common pitfalls. DESCRIPTION In today’s data-driven world, mastering data engineering is crucial for driving innovation and delivering real business impact. Databricks is one of the most powerful platforms which unifies data, analytics and AI requirements of numerous organizations worldwide. Mastering Data Engineering and Analytics with Databricks goes beyond the basics, offering a hands-on, practical approach tailored for professionals eager to excel in the evolving landscape of data engineering and analytics. This book uniquely blends foundational knowledge with advanced applications, equipping readers with the expertise to build, optimize, and scale data pipelines that meet real-world business needs. With a focus on actionable learning, it delves into complex workflows, including real-time data processing, advanced optimization with Delta Lake, and seamless ML integration with MLflow—skills critical for today’s data professionals. Drawing from real-world case studies in FMCG and CPG industries, this book not only teaches you how to implement Databricks solutions but also provides strategic insights into tackling industry-specific challenges. From setting up your environment to deploying CI/CD pipelines, you'll gain a competitive edge by mastering techniques that are directly applicable to your organization’s data strategy. By the end, you’ll not just understand Databricks—you’ll command it, positioning yourself as a leader in the data engineering space. WHAT WILL YOU LEARN ● Design and implement scalable, high-performance data pipelines using Databricks for various business use cases. ● Optimize query performance and efficiently manage cloud resources for cost-effective data processing. ● Seamlessly integrate machine learning models into your data engineering workflows for smarter automation. ● Build and deploy real-time data processing solutions for timely and actionable insights. ● Develop reliable and fault-tolerant Delta Lake architectures to support efficient data lakes at scale. WHO IS THIS BOOK FOR? This book is designed for data engineering students, aspiring data engineers, experienced data professionals, cloud data architects, data scientists and analysts looking to expand their skill sets, as well as IT managers seeking to master data engineering and analytics with Databricks. A basic understanding of data engineering concepts, familiarity with data analytics, and some experience with cloud computing or programming languages such as Python or SQL will help readers fully benefit from the book’s content. TABLE OF CONTENTS SECTION 1 1. Introducing Data Engineering with Databricks 2. Setting Up a Databricks Environment for Data Engineering 3. Working with Databricks Utilities and Clusters SECTION 2 4. Extracting and Loading Data Using Databricks 5. Transforming Data with Databricks 6. Handling Streaming Data with Databricks 7. Creating Delta Live Tables 8. Data Partitioning and Shuffling 9. Performance Tuning and Best Practices 10. Workflow Management 11. Databricks SQL Warehouse 12. Data Storage and Unity Catalog 13. Monitoring Databricks Clusters and Jobs 14. Production Deployment Strategies 15. Maintaining Data Pipelines in Production 16. Managing Data Security and Governance 17. Real-World Data Engineering Use Cases with Databricks 18. AI and ML Essentials 19. Integrating Databricks with External Tools Index

Download or read book Gemini AI Prompt Engineering Mastery Playbook written by Dwayne Anderson and published by Estalontech. This book was released on 2024-07-01 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: Embark on a journey of AI mastery with "Gemini AI Prompt Engineering Mastery Playbook" by Dwayne Anderson. This comprehensive guide transcends conventional boundaries, offering not just prompts but gateways to diverse avenues of artificial intelligence application. The playbook seamlessly incorporates the latest AI technology, utilizing Gemini AI's dynamic capabilities to enhance the user experience and provide unparalleled inspiration. Inside, you'll discover a treasure trove of 400 meticulously curated prompts designed for those seeking to master AI prompt engineering. Dive into the world of AI with prompts that unlock innovative strategies, techniques, and applications across multiple domains. Explore the realms of AI with four distinct categories of prompts: Cryptocurrency Prompt Template: Navigate the ever-evolving market and unlock AI-driven investment strategies. Freelancing Prompts Template: Unveil the secrets to crafting a thriving AI-powered freelance career. Affiliate Marketing Prompt Template: Demystify the art of using AI to turn online influence into a lucrative income stream. Dropshipping Prompts Template: Guide yourself through setting up an AI-enhanced dropshipping business, selecting winning products, and navigating the intricacies of this dynamic model. Unlike traditional guides, "Gemini AI Prompt Engineering Mastery Playbook" doesn't just provide answers; it sparks curiosity, fuels creativity, and invites you to forge your path to AI mastery. Engage with Gemini AI, not merely as a tool but as a companion in your journey toward AI expertise. Each prompt is a doorway to possibilities, beckoning you to explore, adapt, and transform ideas into tangible success. Prepare to unlock your innovative spirit, as Gemini AI becomes your ally in deciphering the language of AI. This isn't just a book; it's your ticket to a world where ideas translate into action, and AI dreams materialize. Embrace the prompts, embrace the journey, and let the pages of "Gemini AI Prompt Engineering Mastery Playbook" be the catalyst for your breakthrough in artificial intelligence.

Download or read book Large Language Models written by Uday Kamath and published by Springer Nature. This book was released on 2024 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Quick Start Guide to Large Language Models written by Sinan Ozdemir and published by Addison-Wesley Professional. This book was released on 2024-09-26 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like Llama 3, Claude 3, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, and hands-on exercises. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up-to-date code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemini), X (Grok), Anthropic (the Claude family), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more Use APIs and Python to fine-tune and customize LLMs for your requirements Build a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI Agents Master advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot prompting Customize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAI Construct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasets Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5 Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind Diagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field." --Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Download or read book Training Your Own Large Language Model written by StoryBuddiesPlay and published by StoryBuddiesPlay. This book was released on 2024-04-26 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demystify the Power of Language with Large Language Models: Your Comprehensive Guide The ability to understand and generate human language is a cornerstone of human intelligence. Artificial intelligence (AI) is rapidly evolving, and Large Language Models (LLMs) are at the forefront of this revolution. These powerful AI tools can process and generate text with remarkable fluency, making them ideal for various applications. This comprehensive guide empowers you to step into the exciting world of LLMs and train your own! Whether you're a seasoned developer, an AI enthusiast, or simply curious about the future of language technology, this book equips you with the knowledge and tools to navigate the LLM landscape. Within these pages, you'll discover: The transformative potential of LLMs: Explore the various tasks LLMs can perform, from generating creative text formats to answering your questions in an informative way, and even translating languages. A step-by-step approach to LLM training: Learn how to define your project goals, identify the right data sources, and choose the optimal LLM architecture for your needs. Essential tools and techniques: Gain insights into popular frameworks like TensorFlow and PyTorch, and delve into practical aspects like data pre-processing and hyperparameter tuning. Fine-tuning and deployment strategies: Unleash the full potential of your LLM by tailoring it to specific tasks and seamlessly integrating it into your applications or workflows. The future of LLMs: Explore cutting-edge advancements like explainable AI and lifelong learning, and discover the potential impact of LLMs on various aspects of society. By the time you finish this guide, you'll be equipped to: Confidently define and plan your LLM project. Train your own LLM using powerful AI frameworks and techniques. Fine-tune your LLM for real-world applications. Deploy and integrate your LLM for seamless functionality. Contribute to the ever-evolving field of large language models. Don't wait any longer! Dive into the world of LLMs and unlock the power of language manipulation with this comprehensive guide. Get started on your LLM journey today!

Download or read book Decoding Large Language Models written by Irena Cronin and published by Packt Publishing Ltd. This book was released on 2024-10-31 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the architecture, development, and deployment strategies of large language models to unlock their full potential Key Features Gain in-depth insight into LLMs, from architecture through to deployment Learn through practical insights into real-world case studies and optimization techniques Get a detailed overview of the AI landscape to tackle a wide variety of AI and NLP challenges Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionEver wondered how large language models (LLMs) work and how they're shaping the future of artificial intelligence? Written by a renowned author and AI, AR, and data expert, Decoding Large Language Models is a combination of deep technical insights and practical use cases that not only demystifies complex AI concepts, but also guides you through the implementation and optimization of LLMs for real-world applications. You’ll learn about the structure of LLMs, how they're developed, and how to utilize them in various ways. The chapters will help you explore strategies for improving these models and testing them to ensure effective deployment. Packed with real-life examples, this book covers ethical considerations, offering a balanced perspective on their societal impact. You’ll be able to leverage and fine-tune LLMs for optimal performance with the help of detailed explanations. You’ll also master techniques for training, deploying, and scaling models to be able to overcome complex data challenges with confidence and precision. This book will prepare you for future challenges in the ever-evolving fields of AI and NLP. By the end of this book, you’ll have gained a solid understanding of the architecture, development, applications, and ethical use of LLMs and be up to date with emerging trends, such as GPT-5.What you will learn Explore the architecture and components of contemporary LLMs Examine how LLMs reach decisions and navigate their decision-making process Implement and oversee LLMs effectively within your organization Master dataset preparation and the training process for LLMs Hone your skills in fine-tuning LLMs for targeted NLP tasks Formulate strategies for the thorough testing and evaluation of LLMs Discover the challenges associated with deploying LLMs in production environments Develop effective strategies for integrating LLMs into existing systems Who this book is for If you’re a technical leader working in NLP, an AI researcher, or a software developer interested in building AI-powered applications, this book is for you. To get the most out of this book, you should have a foundational understanding of machine learning principles; proficiency in a programming language such as Python; knowledge of algebra and statistics; and familiarity with natural language processing basics.

Download or read book Fine tune Llama 3 written by StoryBuddiesPlay and published by StoryBuddiesPlay. This book was released on 2024-05-03 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash the true power of Llama 3, the revolutionary 8-billion parameter language model, by mastering the art of fine-tuning. This comprehensive guide empowers you to transform Llama 3 from a general language master into a domain-specific expert, tailored to your precise needs. Discover how to: Identify your target domain: Explore the vast potential of fine-tuning Llama 3 across diverse fields like code generation, translation, question answering, creative writing, and data analysis. Prepare your data: Learn the crucial steps for building a high-quality dataset that optimizes the fine-tuning process and ensures the model's success. Choose the right tools: Navigate the landscape of popular tools and frameworks like Hugging Face, MONSTERAPIs, and Unsloth to streamline your fine-tuning journey. Implement supervised fine-tuning: Follow a step-by-step guide to set up and execute the training process, including defining tasks, preparing data, and selecting appropriate loss functions. Refine with preference alignment: Go beyond supervised learning by incorporating human feedback through techniques like Reinforcement Learning with Human Feedback (RLHF) and Direct Preference Optimization (DPO) to further enhance accuracy and address potential biases. Evaluate and monitor performance: Employ key metrics like accuracy, precision, recall, perplexity, and human evaluation to ensure your fine-tuned model delivers exceptional results. Deploy and integrate: Learn various deployment options like cloud platforms, model serving frameworks, and custom solutions, and discover strategies for integrating your fine-tuned Llama 3 seamlessly into your workflow or application. This guide is your roadmap to unlocking the full potential of Llama 3. Take control, transform it into a powerful tool for your specific domain, and revolutionize your workflow with the power of fine-tuned language intelligence.

Download or read book A Beginner s Guide to Large Language Models written by Enamul Haque and published by Enamul Haque. This book was released on 2024-07-25 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Beginner's Guide to Large Language Models: Conversational AI for Non-Technical Enthusiasts Step into the revolutionary world of artificial intelligence with "A Beginner's Guide to Large Language Models: Conversational AI for Non-Technical Enthusiasts." Whether you're a curious individual or a professional seeking to leverage AI in your field, this book demystifies the complexities of large language models (LLMs) with engaging, easy-to-understand explanations and practical insights. Explore the fascinating journey of AI from its early roots to the cutting-edge advancements that power today's conversational AI systems. Discover how LLMs, like ChatGPT and Google's Gemini, are transforming industries, enhancing productivity, and sparking creativity across the globe. With the guidance of this comprehensive and accessible guide, you'll gain a solid understanding of how LLMs work, their real-world applications, and the ethical considerations they entail. Packed with vivid examples, hands-on exercises, and real-life scenarios, this book will empower you to harness the full potential of LLMs. Learn to generate creative content, translate languages in real-time, summarise complex information, and even develop AI-powered applications—all without needing a technical background. You'll also find valuable insights into the evolving job landscape, equipping you with the knowledge to pursue a successful career in this dynamic field. This guide ensures that AI is not just an abstract concept but a tangible tool you can use to transform your everyday life and work. Dive into the future with confidence and curiosity, and discover the incredible possibilities that large language models offer. Join the AI revolution and unlock the secrets of the technology that's reshaping our world. "A Beginner's Guide to Large Language Models" is your key to understanding and mastering the power of conversational AI. Introduction This introduction sets the stage for understanding the evolution of artificial intelligence (AI) and large language models (LLMs). It highlights the promise of making complex AI concepts accessible to non-technical readers and outlines the unique approach of this book. Chapter 1: Demystifying AI and LLMs: A Journey Through Time This chapter introduces the basics of AI, using simple analogies and real-world examples. It traces the evolution of AI, from rule-based systems to machine learning and deep learning, leading to the emergence of LLMs. Key concepts such as tokens, vocabulary, and embeddings are explained to build a solid foundation for understanding how LLMs process and generate language. Chapter 2: Mastering Large Language Models Delving deeper into the mechanics of LLMs, this chapter covers the transformer architecture, attention mechanisms, and the processes involved in training and fine-tuning LLMs. It includes hands-on exercises with prompts and discusses advanced techniques like chain-of-thought prompting and prompt chaining to optimise LLM performance. Chapter 3: The LLM Toolbox: Unleashing the Power of Language AI This chapter explores the diverse applications of LLMs in text generation, language translation, summarisation, question answering, and code generation. It also introduces multimodal LLMs that handle both text and images, showcasing their impact on various creative and professional fields. Practical examples and real-life scenarios illustrate how these tools can enhance productivity and creativity. Chapter 4: LLMs in the Real World: Transforming Industries Highlighting the transformative impact of LLMs across different industries, this chapter covers their role in healthcare, finance, education, creative industries, and business. It discusses how LLMs are revolutionising tasks such as medical diagnosis, fraud detection, personalised tutoring, and content creation, and explores the future of work in an AI-powered world. Chapter 5: The Dark Side of LLMs: Ethical Concerns and Challenges Addressing the ethical challenges of LLMs, this chapter covers bias and fairness, privacy concerns, misuse of LLMs, security threats, and the transparency of AI decision-making. It also discusses ethical frameworks for responsible AI development and presents diverse perspectives on the risks and benefits of LLMs. Chapter 6: Mastering LLMs: Advanced Techniques and Strategies This chapter focuses on advanced techniques for leveraging LLMs, such as combining transformers with other AI models, fine-tuning open-source LLMs for specific tasks, and building LLM-powered applications. It provides detailed guidance on prompt engineering for various applications and includes a step-by-step guide to creating an AI-powered chatbot. Chapter 7: LLMs and the Future: A Glimpse into Tomorrow Looking ahead, this chapter explores emerging trends and potential breakthroughs in AI and LLM research. It discusses ethical AI development, insights from leading AI experts, and visions of a future where LLMs are integrated into everyday life. The chapter highlights the importance of building responsible AI systems that address societal concerns. Chapter 8: Your LLM Career Roadmap: Navigating the AI Job Landscape Focusing on the growing demand for LLM expertise, this chapter outlines various career paths in the AI field, such as LLM scientists, engineers, and prompt engineers. It provides resources for building the necessary skillsets and discusses the evolving job market, emphasising the importance of continuous learning and adaptability in a rapidly changing industry. Thought-Provoking Questions, Simple Exercises, and Real-Life Scenarios The book concludes with practical exercises and real-life scenarios to help readers apply their knowledge of LLMs. It includes thought-provoking questions to deepen understanding and provides resources and tools for further exploration of LLM applications. Tools to Help with Your Exercises This section lists tools and platforms for engaging with LLM exercises, such as OpenAI's Playground, Google Translate, and various IDEs for coding. Links to these tools are provided to facilitate hands-on learning and experimentation.

Download or read book Hands On Large Language Models written by Jay Alammar and published by "O'Reilly Media, Inc.". This book was released on 2024-09-11 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: AI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features, products, and entire industries. With this book, Python developers will learn the practical tools and concepts they need to use these capabilities today. You'll learn how to use the power of pre-trained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; build systems that classify and cluster text to enable scalable understanding of large amounts of text documents; and use existing libraries and pre-trained models for text classification, search, and clusterings. This book also shows you how to: Build advanced LLM pipelines to cluster text documents and explore the topics they belong to Build semantic search engines that go beyond keyword search with methods like dense retrieval and rerankers Learn various use cases where these models can provide value Understand the architecture of underlying Transformer models like BERT and GPT Get a deeper understanding of how LLMs are trained Understanding how different methods of fine-tuning optimize LLMs for specific applications (generative model fine-tuning, contrastive fine-tuning, in-context learning, etc.)