Demystifying Large Language Models: Unraveling the Mysteries of Language Transformer Models, Build from...

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James Chen

Demystifying Large Language Models

Unraveling the Mysteries of Language Transformer Models, Build from Ground up, Pre-train, Fine-tune...

James Chen

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Hardback

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English

James Chen
26 April 2024

Natural language & machine translation

Summary
Details

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.

By: James Chen
Imprint: James Chen
Dimensions: Height: 229mm, Width: 152mm, Spine: 27mm
Weight: 635g
ISBN: 9781738908479
ISBN 10: 173890847X
Pages: 346
Publication Date: 26 April 2024
Audience: General/trade , ELT Advanced
Format: Hardback
Publisher's Status: Active