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Large Language Model Using Tensorflow: A Complete TensorFlow Implementation Guide for Modern AI Development
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- FormatePub
- ISBN8231157372
- EAN9798231157372
- Date de parution25/05/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurWalzone Press
Résumé
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. 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.
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. 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.
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. 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.
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. 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.