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Demystifying Large Language Models

Jezik AngleščinaAngleščina
Knjiga Trda
Knjiga Demystifying Large Language Models James Chen
Koda Libristo: 45980029
Založba Aldys Books, april 2024
This book is a comprehensive guide aiming to demystify the world of transformers -- the architecture... Celoten opis
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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.

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O knjigi

Polni naslov Demystifying Large Language Models
Avtor James Chen
Jezik Angleščina
Vezava Knjiga - Trda
Datum izida 2024
Število strani 346
EAN 9781738908479
ISBN 173890847X
Koda Libristo 45980029
Založba Aldys Books
Teža 635
Mere 152 x 229 x 27
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