Download or read book LLM from Scratch written by Anand Vemula and published by Independently Published. This book was released on 2024-06-07 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "LLM from Scratch" is an extensive guide designed to take readers from the basics to advanced concepts of large language models (LLMs). It provides a thorough understanding of the theoretical foundations, practical implementation, and real-world applications of LLMs, catering to both beginners and experienced practitioners. Part I: Foundations The book begins with an introduction to language models, detailing their history, evolution, and wide-ranging applications. It covers essential mathematical and theoretical concepts, including probability, statistics, information theory, and linear algebra. Fundamental machine learning principles are also discussed, setting the stage for more complex topics. The basics of Natural Language Processing (NLP) are introduced, covering text preprocessing, tokenization, embeddings, and common NLP tasks. Part II: Building Blocks This section delves into the core components of deep learning and neural networks. It explains various architectures, such as Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data, including Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs). The concept of attention mechanisms, especially self-attention and scaled dot-product attention, is explored, highlighting their importance in modern NLP models. Part III: Transformer Models The book provides a detailed examination of the Transformer architecture, which has revolutionized NLP. It covers the encoder-decoder framework, multi-head attention, and the building blocks of transformers. Practical aspects of training transformers, including data preparation, training techniques, and evaluation metrics, are discussed. Advanced transformer variants like BERT, GPT, and others are also reviewed, showcasing their unique features and applications. Part IV: Practical Implementation Readers are guided through setting up their development environment, including the necessary tools and libraries. Detailed instructions for implementing a simple language model, along with a step-by-step code walkthrough, are provided. Techniques for fine-tuning pre-trained models using transfer learning are explained, supported by case studies and practical examples. Part V: Applications and Future Directions The book concludes with real-world applications of LLMs across various industries, including healthcare, finance, and retail. Ethical considerations and challenges in deploying LLMs are addressed. Advanced topics such as model compression, zero-shot learning, and future research trends are explored, offering insights into the ongoing evolution of language models. "LLM from Scratch" is an indispensable resource for anyone looking to master the intricacies of large language models and leverage their power in practical applications.
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.
Download or read book Build a Large Language Model From Scratch written by Sebastian Raschka and published by Manning. This book was released on 2024-08-27 with total page 0 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), you’ll discover how LLMs work from the inside out. In this insightful book, bestselling author Sebastian Raschka guides you step by step through creating your own LLM, explaining each stage with clear text, diagrams, and examples. You’ll go from the initial design and creation to pretraining on a general corpus, all the way to finetuning 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 Finetune LLMs for text classification and with your own data Use human feedback to ensure your LLM follows instructions Load pretrained weights into an LLM The large language models (LLMs) that power cutting-edge AI tools like ChatGPT, Bard, and Copilot seem like a miracle, but they’re not magic. This book demystifies LLMs by helping you build your own from scratch. You’ll get a unique and valuable insight into how LLMs work, learn how to evaluate their quality, and pick up concrete techniques to finetune and improve them. The process you use to train and develop your own small-but-functional model in this book follows the same steps used to deliver huge-scale foundation models like GPT-4. Your small-scale LLM can be developed on an ordinary laptop, and you’ll be able to use it as your own personal assistant. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the book Build a Large Language Model (from Scratch) is a one-of-a-kind guide to building your own working LLM. In it, machine learning expert and author Sebastian Raschka reveals how LLMs work under the hood, tearing the lid off the Generative AI black box. The book is filled with practical insights into constructing LLMs, including building a data loading pipeline, assembling their internal building blocks, and finetuning techniques. As you go, you’ll gradually turn your base model into a text classifier tool, and a chatbot that follows your conversational instructions. About the reader For readers who know Python. Experience developing machine learning models is useful but not essential. About the author Sebastian Raschka has been working on machine learning and AI for more than a decade. Sebastian joined Lightning AI in 2022, where he now focuses on AI and LLM research, developing open-source software, and creating educational material. Prior to that, Sebastian worked at the University of Wisconsin-Madison as an assistant professor in the Department of Statistics, focusing on deep learning and machine learning research. He has a strong passion for education and is best known for his bestselling books on machine learning using open-source software.
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 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 Inside LLMs Unraveling the Architecture Training and Real World Use of Large Language Models written by Anand Vemula and published by Anand Vemula. This book was released on with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed for readers who wish to gain a thorough grasp of how LLMs operate, from their foundational architecture to advanced training techniques and real-world applications. The book begins by exploring the fundamental concepts behind LLMs, including their architectural components, such as transformers and attention mechanisms. It delves into the intricacies of self-attention, positional encoding, and multi-head attention, highlighting how these elements work together to create powerful language models. In the training section, the book covers essential strategies for pre-training and fine-tuning LLMs, including various paradigms like masked language modeling and next sentence prediction. It also addresses advanced topics such as domain-specific fine-tuning, transfer learning, and continual adaptation, providing practical insights into optimizing model performance for specialized tasks.
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
Download or read book Prompt Engineering for Large Language Models written by Nimrita Koul and published by Nimrita Koul. This book was released on with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: This eBook ‘Prompt Engineering for Large Language Models’ is meant to be a concise and practical guide for the reader. It teaches you to write better prompts for generative artificial intelligence models like Google’s BARD and OpenAI’s ChatGPT. These models have been trained on huge volumes of data to generate text and provide a free of cost, web-based interface to the underlying models as of 11 Nov. 2023. These models are fine tuned for conversational AI applications. All the prompts used in the eBook have been tested on the web interface of BARD and ChatGPT-3.5.
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.
Download or read book Optimizing Large Language Models Practical Approaches and Applications of Quantization Technique written by Anand Vemula and published by Anand Vemula. This book was released on 2024-08-19 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book provides an in-depth understanding of quantization techniques and their impact on model efficiency, performance, and deployment. The book starts with a foundational overview of quantization, explaining its significance in reducing the computational and memory requirements of LLMs. It delves into various quantization methods, including uniform and non-uniform quantization, per-layer and per-channel quantization, and hybrid approaches. Each technique is examined for its applicability and trade-offs, helping readers select the best method for their specific needs. The guide further explores advanced topics such as quantization for edge devices and multi-lingual models. It contrasts dynamic and static quantization strategies and discusses emerging trends in the field. Practical examples, use cases, and case studies are provided to illustrate how these techniques are applied in real-world scenarios, including the quantization of popular models like GPT and BERT.
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
Download or read book Large Language Model Based Solutions written by Shreyas Subramanian and published by John Wiley & Sons. This book was released on 2024-04-02 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to build cost-effective apps using Large Language Models In Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications, Principal Data Scientist at Amazon Web Services, Shreyas Subramanian, delivers a practical guide for developers and data scientists who wish to build and deploy cost-effective large language model (LLM)-based solutions. In the book, you'll find coverage of a wide range of key topics, including how to select a model, pre- and post-processing of data, prompt engineering, and instruction fine tuning. The author sheds light on techniques for optimizing inference, like model quantization and pruning, as well as different and affordable architectures for typical generative AI (GenAI) applications, including search systems, agent assists, and autonomous agents. You'll also find: Effective strategies to address the challenge of the high computational cost associated with LLMs Assistance with the complexities of building and deploying affordable generative AI apps, including tuning and inference techniques Selection criteria for choosing a model, with particular consideration given to compact, nimble, and domain-specific models Perfect for developers and data scientists interested in deploying foundational models, or business leaders planning to scale out their use of GenAI, Large Language Model-Based Solutions will also benefit project leaders and managers, technical support staff, and administrators with an interest or stake in the subject.
Download or read book From Data Selection To Fine Tuning written by Richard Anthony Aragon and published by Independently Published. This book was released on 2023-09-04 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: From Data Selection To Fine Tuning: The Technical Guide To Constructing LLM Models Learn how to build and train large language models (LLMs) from scratch Large language models (LLMs) are a type of artificial intelligence (AI) that are trained on massive datasets of text and code. This allows them to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. This book provides a technical guide to constructing LLM models. It covers everything from data selection to fine tuning, including: How to select the right data for your model How to preprocess your data How to choose the right architecture for your model How to train your model How to evaluate your model How to fine tune your model The book also covers the ethical considerations involved in building and using LLM models. If you are interested in learning how to build and train LLM models, then this book is for you. It is a comprehensive and up-to-date resource that will teach you everything you need to know. Key Features: Covers all aspects of LLM model construction, from data selection to fine tuning Provides a hands-on approach with practical examples Discusses the ethical considerations involved in building and using LLM models Written by an expert in the field of AI Audience: This book is for anyone who wants to learn how to build and train LLM models. It is suitable for both beginners and experienced professionals. Here are some additional points that you could include in the description: The book is written in a clear and concise style, making it easy to follow. The book is full of practical examples, so you can learn by doing. The book is up-to-date with the latest research in the field of LLMs. The book is written by an expert in the field of AI, so you can be confident that you are learning from the best.
Download or read book Advanced Intelligent Computing Technology and Applications written by De-Shuang Huang and published by Springer Nature. This book was released on with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The LLM Toolkit Fine Tuning Hyperparameter Tuning and Building Hierarchical Classifiers written by Anand Vemula and published by Anand Vemula. This book was released on with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the age of artificial intelligence, large language models (LLMs) have become powerful tools for understanding and manipulating language. However, unlocking their full potential requires a deeper understanding of fine-tuning, hyperparameter optimization, and hierarchical classification techniques. The LLM Toolkit equips you with a comprehensive guide to take your LLMs to the next level. This book delves into the concept of fine-tuning, explaining how to adapt pre-trained LLMs to specific tasks, such as text classification or question answering. You'll explore various techniques for fine-tuning, including freezing and unfreezing layers, along with strategies for selecting and augmenting task-specific training data. Next, the book tackles the crucial topic of hyperparameter optimization. LLMs have numerous parameters that can significantly impact their performance. This section guides you through the challenges of optimizing these hyperparameters, including the high computational cost and vast search space. You'll discover common techniques like grid search, random search, and Bayesian optimization, along with their strengths and limitations. The book also explores the potential of using LLMs themselves to streamline hyperparameter optimization, paving the way for more efficient fine-tuning processes. Finally, the book dives into hierarchical classification, a powerful approach for categorizing data with inherent hierarchical structures. You'll learn how to leverage LLMs to build hierarchical classifiers, exploring both multi-stage and tree-based approaches. The book delves into the benefits of hierarchical classification for LLMs, including improved accuracy and better handling of ambiguous or noisy data. The LLM Toolkit is your one-stop shop for mastering these advanced LLM techniques. Whether you're a researcher, developer, or simply interested in pushing the boundaries of language models, this book equips you with the practical knowledge and tools to unlock the full potential of LLMs and achieve cutting-edge results in your field.
Download or read book Transfer Diffusion and Adoption of Next Generation Digital Technologies written by Sujeet K. Sharma and published by Springer Nature. This book was released on 2023-12-12 with total page 479 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the IFIP WG 8.6 International Working Conference on Transfer and Diffusion of IT, TDIT 2023, which took place in Nagpur, India, in December 2023. The 87 full papers and 23 short papers presented in these proceedings were carefully reviewed and selected from 209 submissions. The papers are organized in the following topical sections: Volume I: Digital technologies (artificial intelligence) adoption; digital platforms and applications; digital technologies in e-governance; metaverse and marketing. Volume II: Emerging technologies adoption; general IT adoption; healthcare IT adoption. Volume III: Industry 4.0; transfer, diffusion and adoption of next-generation digital technologies; diffusion and adoption of information technology.
