Название: Large Language Model Using Tensorflow: A Complete TensorFlow Implementation Guide for Modern AI Development
Автор: Aarav Joshi
Издательство: 101 Books
Год: 2025
Страниц: 1470
Язык: английский
Формат: epub (true)
Размер: 12.2 MB

Large Language Model Using Tensorflow: A Complete TensorFlow Implementation Guide for Modern AI Development is the definitive hands-on guide for building state-of-the-art language models from the ground up using TensorFlow. This comprehensive resource takes you through every stage of LLM development, from understanding transformer architecture fundamentals to deploying production-ready models at scale.

Unlike other theoretical treatments, this book provides practical, code-first instruction using TensorFlow's latest features and best practices. You'll master essential concepts including attention mechanisms, positional encoding, tokenization strategies, and advanced training techniques like mixed precision and distributed computing. The book covers critical topics such as instruction tuning, reinforcement learning from human feedback (RLHF), and parameter-efficient fine-tuning methods like LoRA.

Each chapter builds systematically on previous concepts, featuring detailed code implementations, optimization strategies, and real-world deployment considerations. You'll learn to handle large-scale datasets, implement efficient training pipelines, and navigate the complexities of model scaling and production deployment using TensorFlow Serving and cloud platforms.

The evolution of language models represents one of the most significant advancements in artificial intelligence research. This remarkable journey from simple statistical approaches to sophisticated neural architectures has revolutionized how machines understand and generate human language. The transformation didn’t happen overnight but evolved through decades of research, breakthrough moments, and computational innovations. Understanding this progression is essential for anyone working with large language models today, as it provides crucial context for current architectures and reveals the foundational principles that continue to guide development. What’s particularly fascinating is how each limitation in earlier approaches sparked innovations that eventually led to the powerful language models we see today.

Whether you're a Machine Learning engineer, AI researcher, or data scientist, this book provides the practical expertise needed to build, train, and deploy your own large language models. By the end, you'll have created a fully functional LLM comparable to GPT-style models, complete with the knowledge to customize and scale it for specific applications.