Mastering Large Language Models

✍ Scribed by Sanket Subhash Khandare

• Publisher: BPB Publications
• Year: 2024
• Tongue: English
• Leaves: 903
• Category: Library

✦ Synopsis

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

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