How To Build Large Language Models Llms

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Machine Learning with PyTorch and Scikit-Learn

Author : Sebastian Raschka
Publisher : Packt Publishing Ltd
ISBN 13 : 1801816387
Total Pages : 775 pages
Book Rating : 4.8/5 (18 download)

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Book Synopsis Machine Learning with PyTorch and Scikit-Learn by : Sebastian Raschka

Download or read book Machine Learning with PyTorch and Scikit-Learn written by Sebastian Raschka and published by Packt Publishing Ltd. This book was released on 2022-02-25 with total page 775 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

Building Large Language Model(LLM) Applications

Author : Anand Vemula
Publisher : Anand Vemula
ISBN 13 :
Total Pages : 77 pages
Book Rating : 4./5 ( download)

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Book Synopsis Building Large Language Model(LLM) Applications by : Anand Vemula

Download or read book Building Large Language Model(LLM) Applications written by Anand Vemula and published by Anand Vemula. This book was released on with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Building LLM Apps" is a comprehensive guide that equips readers with the knowledge and practical skills needed to develop applications utilizing large language models (LLMs). The book covers various aspects of LLM application development, starting from understanding the fundamentals of LLMs to deploying scalable and efficient solutions. Beginning with an introduction to LLMs and their importance in modern applications, the book explores the history, key concepts, and popular architectures like GPT and BERT. Readers learn how to set up their development environment, including hardware and software requirements, installing necessary tools and libraries, and leveraging cloud services for efficient development and deployment. Data preparation is essential for training LLMs, and the book provides insights into gathering and cleaning data, annotating and labeling data, and handling imbalanced data to ensure high-quality training datasets. Training large language models involves understanding training basics, best practices, distributed training techniques, and fine-tuning pre-trained models for specific tasks. Developing LLM applications requires designing user interfaces, integrating LLMs into existing systems, and building interactive features such as chatbots, text generation, sentiment analysis, named entity recognition, and machine translation. Advanced LLM techniques like prompt engineering, transfer learning, multi-task learning, and zero-shot learning are explored to enhance model capabilities. Deployment and scalability strategies are discussed to ensure smooth deployment of LLM applications while managing costs effectively. Security and ethics in LLM apps are addressed, covering bias detection, fairness, privacy, security, and ethical considerations to build responsible AI solutions. Real-world case studies illustrate the practical applications of LLMs in various domains, including customer service, healthcare, and finance. Troubleshooting and optimization techniques help readers address common issues and optimize model performance. Looking towards the future, the book highlights emerging trends and developments in LLM technology, emphasizing the importance of staying updated with advancements and adhering to ethical AI practices. "Building LLM Apps" serves as a comprehensive resource for developers, data scientists, and business professionals seeking to harness the power of large language models in their applications.

Build a Large Language Model (From Scratch)

Author : Sebastian Raschka
Publisher : Simon and Schuster
ISBN 13 : 1633437167
Total Pages : 366 pages
Book Rating : 4.6/5 (334 download)

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Book Synopsis Build a Large Language Model (From Scratch) by : Sebastian Raschka

Download or read book Build a Large Language Model (From Scratch) written by Sebastian Raschka and published by Simon and Schuster. This book was released on 2024-10-29 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. 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

Introduction to Python and Large Language Models

Author : Dilyan Grigorov
Publisher : Springer Nature
ISBN 13 :
Total Pages : 395 pages
Book Rating : 4.8/5 (688 download)

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Book Synopsis Introduction to Python and Large Language Models by : Dilyan Grigorov

Download or read book Introduction to Python and Large Language Models written by Dilyan Grigorov and published by Springer Nature. This book was released on with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Building LLM Powered Applications

Author : Valentina Alto
Publisher : Packt Publishing Ltd
ISBN 13 : 1835462634
Total Pages : 343 pages
Book Rating : 4.8/5 (354 download)

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Book Synopsis Building LLM Powered Applications by : Valentina Alto

Download or read book Building LLM Powered Applications written by Valentina Alto and published by Packt Publishing Ltd. This book was released on 2024-05-22 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get hands-on with GPT 3.5, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applications Key Features Embed LLMs into real-world applications Use LangChain to orchestrate LLMs and their components within applications Grasp basic and advanced techniques of prompt engineering Book 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 learn Explore the core components of LLM architecture, including encoder-decoder blocks and embeddings Understand the unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLM Use AI orchestrators like LangChain, with Streamlit for the frontend Get familiar with LLM components such as memory, prompts, and tools Learn how to use non-parametric knowledge and vector databases Understand the implications of LFMs for AI research and industry applications Customize your LLMs with fine tuning Learn about the ethical implications of LLM-powered applications Who 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.

Quick Start Guide to Large Language Models

Author : Sinan Ozdemir
Publisher : Addison-Wesley Professional
ISBN 13 : 0138199337
Total Pages : 429 pages
Book Rating : 4.1/5 (381 download)

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Book Synopsis Quick Start Guide to Large Language Models by : Sinan Ozdemir

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 2023-09-20 with total page 429 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 ChatGPT are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, 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, hands-on exercises, and more. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, parameters, and performance. You'll find even more resources on the companion website, including sample datasets and code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and ChatGPT), Google (BERT, T5, and Bard), EleutherAI (GPT-J and GPT-Neo), 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 retrieval-augmented generation Master advanced prompt engineering techniques like output structuring, chain-ofthought, and semantic few-shot prompting Customize LLM embeddings to build a complete recommendation engine from scratch with user data Construct and fine-tune multimodal Transformer architectures using opensource LLMs Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind "By balancing the potential of both open- and closed-source models, Quick Start Guide to Large Language Models stands as a comprehensive guide to understanding and using LLMs, bridging the gap between theoretical concepts and practical application." --Giada Pistilli, Principal Ethicist at HuggingFace "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.

Mastering Large Language Models

Author : Sanket Subhash Khandare
Publisher : BPB Publications
ISBN 13 : 9355519656
Total Pages : 465 pages
Book Rating : 4.3/5 (555 download)

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Book Synopsis Mastering Large Language Models by : Sanket Subhash Khandare

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

Hands-On Large Language Models

Author : Jay Alammar
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098150929
Total Pages : 449 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Hands-On Large Language Models by : Jay Alammar

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.)

Demystifying Large Language Models

Author : James Chen
Publisher : James Chen
ISBN 13 : 1738908461
Total Pages : 300 pages
Book Rating : 4.7/5 (389 download)

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Book Synopsis Demystifying Large Language Models by : James Chen

Download or read book Demystifying Large Language Models written by James Chen and published by James Chen. This book was released on 2024-04-25 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a comprehensive guide aiming to demystify the world of transformers -- the architecture that powers Large Language Models (LLMs) like GPT and BERT. From PyTorch basics and mathematical foundations to implementing a Transformer from scratch, you'll gain a deep understanding of the inner workings of these models. That's just the beginning. Get ready to dive into the realm of pre-training your own Transformer from scratch, unlocking the power of transfer learning to fine-tune LLMs for your specific use cases, exploring advanced techniques like PEFT (Prompting for Efficient Fine-Tuning) and LoRA (Low-Rank Adaptation) for fine-tuning, as well as RLHF (Reinforcement Learning with Human Feedback) for detoxifying LLMs to make them aligned with human values and ethical norms. Step into the deployment of LLMs, delivering these state-of-the-art language models into the real-world, whether integrating them into cloud platforms or optimizing them for edge devices, this section ensures you're equipped with the know-how to bring your AI solutions to life. Whether you're a seasoned AI practitioner, a data scientist, or a curious developer eager to advance your knowledge on the powerful LLMs, this book is your ultimate guide to mastering these cutting-edge models. By translating convoluted concepts into understandable explanations and offering a practical hands-on approach, this treasure trove of knowledge is invaluable to both aspiring beginners and seasoned professionals. Table of Contents 1. INTRODUCTION 1.1 What is AI, ML, DL, Generative AI and Large Language Model 1.2 Lifecycle of Large Language Models 1.3 Whom This Book Is For 1.4 How This Book Is Organized 1.5 Source Code and Resources 2. PYTORCH BASICS AND MATH FUNDAMENTALS 2.1 Tensor and Vector 2.2 Tensor and Matrix 2.3 Dot Product 2.4 Softmax 2.5 Cross Entropy 2.6 GPU Support 2.7 Linear Transformation 2.8 Embedding 2.9 Neural Network 2.10 Bigram and N-gram Models 2.11 Greedy, Random Sampling and Beam 2.12 Rank of Matrices 2.13 Singular Value Decomposition (SVD) 2.14 Conclusion 3. TRANSFORMER 3.1 Dataset and Tokenization 3.2 Embedding 3.3 Positional Encoding 3.4 Layer Normalization 3.5 Feed Forward 3.6 Scaled Dot-Product Attention 3.7 Mask 3.8 Multi-Head Attention 3.9 Encoder Layer and Encoder 3.10 Decoder Layer and Decoder 3.11 Transformer 3.12 Training 3.13 Inference 3.14 Conclusion 4. PRE-TRAINING 4.1 Machine Translation 4.2 Dataset and Tokenization 4.3 Load Data in Batch 4.4 Pre-Training nn.Transformer Model 4.5 Inference 4.6 Popular Large Language Models 4.7 Computational Resources 4.8 Prompt Engineering and In-context Learning (ICL) 4.9 Prompt Engineering on FLAN-T5 4.10 Pipelines 4.11 Conclusion 5. FINE-TUNING 5.1 Fine-Tuning 5.2 Parameter Efficient Fine-tuning (PEFT) 5.3 Low-Rank Adaptation (LoRA) 5.4 Adapter 5.5 Prompt Tuning 5.6 Evaluation 5.7 Reinforcement Learning 5.8 Reinforcement Learning Human Feedback (RLHF) 5.9 Implementation of RLHF 5.10 Conclusion 6. DEPLOYMENT OF LLMS 6.1 Challenges and Considerations 6.2 Pre-Deployment Optimization 6.3 Security and Privacy 6.4 Deployment Architectures 6.5 Scalability and Load Balancing 6.6 Compliance and Ethics Review 6.7 Model Versioning and Updates 6.8 LLM-Powered Applications 6.9 Vector Database 6.10 LangChain 6.11 Chatbot, Example of LLM-Powered Application 6.12 WebUI, Example of LLM-Power Application 6.13 Future Trends and Challenges 6.14 Conclusion REFERENCES ABOUT THE AUTHOR

A Beginner's Guide to Large Language Models

Author : Enamul Haque
Publisher : Enamul Haque
ISBN 13 : 1445263289
Total Pages : 259 pages
Book Rating : 4.4/5 (452 download)

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Book Synopsis A Beginner's Guide to Large Language Models by : Enamul Haque

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.

The Language Architect: Building the Future with Mistral LLM

Author : Anand Vemula
Publisher : Anand Vemula
ISBN 13 :
Total Pages : 25 pages
Book Rating : 4./5 ( download)

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Book Synopsis The Language Architect: Building the Future with Mistral LLM by : Anand Vemula

Download or read book The Language Architect: Building the Future with Mistral LLM written by Anand Vemula and published by Anand Vemula. This book was released on with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Language Architect: Building the Future with Mistral LLM Unlock the potential of language and co-create the future with Mistral LLM, a revolutionary large language model. In "The Language Architect: Building the Future with Mistral LLM," you'll embark on a journey into the exciting world of large language models (LLMs) and delve into the capabilities of Mistral LLM, a powerful AI tool that's shaping the future of communication. This book is your comprehensive guide to understanding Mistral LLM. You'll explore its inner workings, from its innovative architecture to its impressive multilingual abilities. Master the Fundamentals: Gain a solid understanding of LLMs and how they revolutionize human-computer interaction. Dive into Mistral LLM: Explore the technical aspects of Mistral, including its decoder-only transformer model, efficiency techniques, and training processes. Unleash the Power of Words: Discover how Mistral LLM can generate creative text formats, translate languages with accuracy, and answer your questions in informative ways. Become a Language Architect: Learn how to leverage Mistral LLM for various applications, from crafting compelling content to creating chatbots and virtual assistants. But "The Language Architect" goes beyond just technical understanding. It emphasizes the responsible development and use of LLMs. Navigate Ethical Considerations: Explore the potential biases and limitations of LLMs and how Mistral prioritizes safety and ethical AI practices. Forge a Human-Machine Partnership: Discover how to collaborate with Mistral LLM to achieve exceptional results while ensuring responsible use of this powerful technology. This book is for anyone interested in the future of language and AI. Whether you're a writer, programmer, entrepreneur, or simply curious about technological advancements, "The Language Architect" equips you with the knowledge and insights to become a co-architect of the future, working alongside Mistral LLM to unlock its potential for positive change.

Mastering Large Language Models with Python

Author : Raj Arun R
Publisher : Orange Education Pvt Ltd
ISBN 13 : 8197081824
Total Pages : 547 pages
Book Rating : 4.1/5 (97 download)

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Book Synopsis Mastering Large Language Models with Python by : Raj Arun R

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

Building and Fine Tuning LLMs from Scratch

Author : StoryBuddiesPlay
Publisher : StoryBuddiesPlay
ISBN 13 :
Total Pages : 84 pages
Book Rating : 4./5 ( download)

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Book Synopsis Building and Fine Tuning LLMs from Scratch by : StoryBuddiesPlay

Download or read book Building and Fine Tuning LLMs from Scratch written by StoryBuddiesPlay and published by StoryBuddiesPlay. This book was released on 2024-09-10 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Building and Fine-Tuning LLMs from Scratch" is an essential guide for AI practitioners, researchers, and enthusiasts looking to master the art of creating and optimizing large language models. This comprehensive resource covers everything from fundamental concepts to cutting-edge techniques, providing readers with the knowledge and skills needed to develop state-of-the-art language AI systems. With practical examples, in-depth explanations, and expert insights, this book is your roadmap to becoming proficient in LLM architecture, training, fine-tuning, and deployment. Whether you're a seasoned professional or an ambitious newcomer, this guide will empower you to push the boundaries of what's possible in natural language processing and AI. Large Language Models, AI development, Natural Language Processing, Machine Learning, Deep Learning, Transformer Architecture, Fine-tuning techniques, Neural Networks, Text Generation, Language AI

Building Transformer Models with PyTorch 2.0

Author : Prem Timsina
Publisher : BPB Publications
ISBN 13 : 9355517491
Total Pages : 355 pages
Book Rating : 4.3/5 (555 download)

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Book Synopsis Building Transformer Models with PyTorch 2.0 by : Prem Timsina

Download or read book Building Transformer Models with PyTorch 2.0 written by Prem Timsina and published by BPB Publications. This book was released on 2024-03-08 with total page 355 pages. Available in PDF, EPUB and Kindle. Book excerpt: Your key to transformer based NLP, vision, speech, and multimodalities KEY FEATURES ● Transformer architecture for different modalities and multimodalities. ● Practical guidelines to build and fine-tune transformer models. ● Comprehensive code samples with detailed documentation. DESCRIPTION This book covers transformer architecture for various applications including NLP, computer vision, speech processing, and predictive modeling with tabular data. It is a valuable resource for anyone looking to harness the power of transformer architecture in their machine learning projects. The book provides a step-by-step guide to building transformer models from scratch and fine-tuning pre-trained open-source models. It explores foundational model architecture, including GPT, VIT, Whisper, TabTransformer, Stable Diffusion, and the core principles for solving various problems with transformers. The book also covers transfer learning, model training, and fine-tuning, and discusses how to utilize recent models from Hugging Face. Additionally, the book explores advanced topics such as model benchmarking, multimodal learning, reinforcement learning, and deploying and serving transformer models. In conclusion, this book offers a comprehensive and thorough guide to transformer models and their various applications. WHAT YOU WILL LEARN ● Understand the core architecture of various foundational models, including single and multimodalities. ● Step-by-step approach to developing transformer-based Machine Learning models. ● Utilize various open-source models to solve your business problems. ● Train and fine-tune various open-source models using PyTorch 2.0 and the Hugging Face ecosystem. ● Deploy and serve transformer models. ● Best practices and guidelines for building transformer-based models. WHO THIS BOOK IS FOR This book caters to data scientists, Machine Learning engineers, developers, and software architects interested in the world of generative AI. TABLE OF CONTENTS 1. Transformer Architecture 2. Hugging Face Ecosystem 3. Transformer Model in PyTorch 4. Transfer Learning with PyTorch and Hugging Face 5. Large Language Models: BERT, GPT-3, and BART 6. NLP Tasks with Transformers 7. CV Model Anatomy: ViT, DETR, and DeiT 8. Computer Vision Tasks with Transformers 9. Speech Processing Model Anatomy: Whisper, SpeechT5, and Wav2Vec 10. Speech Tasks with Transformers 11. Transformer Architecture for Tabular Data Processing 12. Transformers for Tabular Data Regression and Classification 13. Multimodal Transformers, Architectures and Applications 14. Explore Reinforcement Learning for Transformer 15. Model Export, Serving, and Deployment 16. Transformer Model Interpretability, and Experimental Visualization 17. PyTorch Models: Best Practices and Debugging

The LLM Security Handbook: Building Trustworthy AI Applications

Author : Anand Vemula
Publisher : Anand Vemula
ISBN 13 :
Total Pages : 68 pages
Book Rating : 4./5 ( download)

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Book Synopsis The LLM Security Handbook: Building Trustworthy AI Applications by : Anand Vemula

Download or read book The LLM Security Handbook: Building Trustworthy AI Applications written by Anand Vemula and published by Anand Vemula. This book was released on with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a world increasingly powered by artificial intelligence, Large Language Models (LLMs) are emerging as powerful tools capable of generating human-quality text, translating languages, and writing different creative content. However, this power comes with hidden risks. This book dives deep into the world of LLM security, providing a comprehensive guide for developers, security professionals, and anyone interested in harnessing the potential of LLMs responsibly. Part 1: Understanding the Landscape The book starts by unpacking the inner workings of LLMs and explores how these models can be misused to generate harmful content or leak sensitive data. We delve into the concept of LLM bias, highlighting how the data used to train these models can influence their outputs. Through real-world scenarios and case studies, the book emphasizes the importance of proactive security measures to mitigate these risks. Part 2: Building Secure LLM Applications The core of the book focuses on securing LLM applications throughout their development lifecycle. We explore the Secure Development Lifecycle (SDLC) for LLMs, emphasizing secure data acquisition, robust model testing techniques, and continuous monitoring strategies. The book delves into MLOps security practices, highlighting techniques for securing model repositories, implementing anomaly detection, and ensuring the trustworthiness of LLM models. Part 3: Governance and the Future of LLM Security With the rise of LLMs, legal and ethical considerations come to the forefront. The book explores data privacy regulations and how to ensure responsible AI development practices. We discuss the importance of explainability and transparency in LLM decision-making for building trust and addressing potential biases. Looking ahead, the book explores emerging security threats and emphasizes the importance of continuous improvement and collaboration within the LLM security community. By proactively addressing these challenges, we can ensure a secure future for LLM applications.

Generative AI with LangChain

Author : Ben Auffarth
Publisher : Packt Publishing Ltd
ISBN 13 : 1835088368
Total Pages : 369 pages
Book Rating : 4.8/5 (35 download)

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Book Synopsis Generative AI with LangChain by : Ben Auffarth

Download or read book Generative AI with LangChain written by Ben Auffarth and published by Packt Publishing Ltd. This book was released on 2023-12-22 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Features Learn how to leverage LangChain to work around LLMs’ inherent weaknesses Delve into LLMs with LangChain and explore their fundamentals, ethical dimensions, and application challenges Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality Book 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 learn Create LLM apps with LangChain, like question-answering systems and chatbots Understand transformer models and attention mechanisms Automate data analysis and visualization using pandas and Python Grasp prompt engineering to improve performance Fine-tune LLMs and get to know the tools to unleash their power Deploy LLMs as a service with LangChain and apply evaluation strategies Privately interact with documents using open-source LLMs to prevent data leaks Who this book is for The 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.

AgentScope A Guide to Building Multi-Agent LLM Applications

Author : StoryBuddiesPlay
Publisher : StoryBuddiesPlay
ISBN 13 :
Total Pages : 99 pages
Book Rating : 4./5 ( download)

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Book Synopsis AgentScope A Guide to Building Multi-Agent LLM Applications by : StoryBuddiesPlay

Download or read book AgentScope A Guide to Building Multi-Agent LLM Applications written by StoryBuddiesPlay and published by StoryBuddiesPlay. This book was released on 2024-05-14 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash the power of collaboration with AgentScope, a comprehensive platform designed to streamline the development of multi-agent Large Language Model (LLM) applications. This in-depth guide equips you with everything you need to know to leverage AgentScope's functionalities and build intelligent, scalable AI systems. Embrace the Future of AI: Multi-Agent Collaboration Made Easy AgentScope empowers you to construct a team of specialized LLMs, each with its own strengths and expertise. Imagine a system where one agent analyzes customer reviews for sentiment, another identifies key themes, and a third generates a comprehensive report – all working together seamlessly. This is the power of multi-agent LLMs, and AgentScope simplifies the process of bringing it to life. Dive Deep into AgentScope: From Agent Definition to Orchestrated Workflows This comprehensive guide takes you on a journey through the functionalities of AgentScope. Learn how to define and configure your agents, specifying their roles, LLM models, and communication protocols. Explore how to orchestrate tasks, ensuring a smooth workflow where subtasks are completed in the correct order and dependencies are managed effectively. Conquer Challenges: Error Handling, Security, and Explainability The guide doesn't shy away from the real-world considerations of multi-agent systems. Address potential errors and exceptions with AgentScope's robust error handling mechanisms. Safeguard your LLM application with built-in security features like authentication and data encryption. Foster trust and transparency by incorporating Explainable AI (XAI) techniques to understand the decision-making processes within your multi-agent system. Scale to New Heights: Optimizing Performance for Large Tasks As your LLM application tackles more complex tasks and works with ever-growing datasets, AgentScope provides the tools you need to maintain optimal performance. Discover strategies for resource allocation, communication optimization, and utilizing scalable LLM architectures. Employ monitoring and analytics to identify bottlenecks and ensure your multi-agent system continues to function efficiently. A Glimpse into the Future: Pioneering Applications with AgentScope Look ahead and explore the exciting potential of multi-agent LLM systems. Imagine AI-powered scientific discovery, personalized education, intelligent content creation, and advanced conversational AI for businesses – these are just a few possibilities on the horizon. AgentScope equips you to be a part of this revolution, empowering you to build groundbreaking applications that leverage the power of collaborative intelligence. Start Building Today: Unleash the Potential of Multi-Agent LLMs with AgentScope This guide provides a roadmap for your journey into the world of multi-agent LLM development with AgentScope. With its user-friendly interface, comprehensive documentation, and expansive capabilities, AgentScope makes complex AI development accessible. So, what are you waiting for? Start building the future of AI today!