Название: Mastering Large Language Models: Advanced techniques, applications, cutting-edge methods, and top LLMs Автор: Sanket Subhash Khandare Издательство: BPB Publications Год: 2024 Страниц: 903 Язык: английский Формат: pdf, epub Размер: 13.2 MB
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.
Chapter 1: Fundamentals of Natural Language Processing – It introduces the basics of Natural Language Processing (NLP), including its applications and challenges. It also covers the different components of NLP, such as morphological analysis, syntax, semantics, and pragmatics. The chapter provides an overview of the historical evolution of NLP and explains the importance of language data in NLP research.
Chapter 2: Introduction to Language Models – It introduces Language Models (LMs), which are computational models that learn to predict the probability of a sequence of words. The chapter explains the concept of probability in language modeling and how it is calculated. It also covers the different types of LMs, such as n-gram models, feedforward neural networks, and recurrent neural networks. This chapter also explores the different types of LMs in more detail. It covers statistical language models, which are based on the frequency of word co-occurrences, and neural language models, which use neural networks to model the probability distribution of words. The chapter also discusses the differences between autoregressive and autoencoding LMs and how they are trained.
Chapter 3: Data Collection and Pre-processing for Language Modeling – It explores the essential steps in transforming raw data into valuable insights. We will cover strategies for acquiring diverse datasets, techniques for cleaning noisy data, and methods for preprocessing text to prepare it for modeling. We will delve into exploratory data analysis, address challenges like handling unstructured data, discuss building a representative text corpus, and explore data privacy considerations. You will be equipped to develop accurate and robust language models by mastering these techniques. ... Chapter 13: Prompt Engineering – It explores the vital role of prompt engineering in the evolving field of Natural Language Processing (NLP). Language Models (LLMs) such as GPT-3 and BERT have significantly transformed text generation and comprehension in AI. This chapter delves into the intricacies of prompt engineering, from understanding different prompt types to crafting tailored prompts for specific NLP tasks. By mastering the art and techniques of prompt engineering, readers will be equipped to harness the full potential of these powerful LLMs.
Chapter 14: Future of LLMs and Its Impact – We embark on a journey to explore the future of Large Language Models (LLMs) and their profound impact on society. From advancements in model capabilities like the Program-Aided Language Model (PAL) and ReAct to considerations of their influence on the job market and ethical implications, we delve into the transformative potential and ethical responsibilities associated with these linguistic powerhouses. As we navigate this dynamic landscape, we envision a future where human-AI collaboration fosters innovation and societal well-being, shaping a world where the mastery of LLMs resonates across industries and professions.
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).
Навигация
Вход на сайт
Реклама
Mastering Large Language Models 
