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Build a Large Language Model (From Scratch)
物品 #: 127561311

Build a Large Language Model (From Scratch)

物品 #: 127561311

HKD 523

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What Stands Out

Comprehensive Guide
This book offers an in-depth, step-by-step approach to building a large language model from scratch, making complex concepts accessible for both beginners and professionals.
Practical Examples
Includes hands-on examples and coding exercises that enhance learning, enabling readers to directly apply concepts and solidify their understanding of language model development.
Expert Insights
Authored by industry experts, it provides insider knowledge and best practices that keep readers ahead in the rapidly evolving field of AI and machine learning.

產品詳情

Shop Build a Large Language Model (From Scratch) online at a best price in 香港. 1633437167
  • Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up! In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You'll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to: Plan and code all the parts of an LLMPrepare a dataset suitable for LLM trainingFine-tune LLMs for text classification and with your own dataUse human feedback to ensure your LLM follows instructionsLoad pretrained weights into an LLM Build a Large Language Model (from Scratch) takes you inside the AI black box to tinker with the internal systems that power generative AI. As you work through each key stage of LLM creation, you'll develop an in-depth understanding of how LLMs work, their limitations, and their customization methods. Your LLM can be developed on an ordinary laptop, and used as your own personal assistant. About the technology Physicist Richard P. Feynman reportedly said, “I don't understand anything I can't build.” Based on this same powerful principle, bestselling author Sebastian Raschka guides you step by step as you build a GPT-style LLM that you can run on your laptop. This is an engaging book that covers each stage of the process, from planning and coding to training and fine-tuning.
Publisher Manning Publications
Publication date 1 Nov. 2024
Edition 1st
Language English
Print length 368 pages
ISBN-10 1633437167
ISBN-13 978-1633437166
Item weight 612 g
Dimensions 18.73 x 1.78 x 23.5 cm

Who Should Buy?

Suitable For
  • AI Enthusiasts

    This product is ideal for individuals keen on understanding and building language models from foundational principles.

  • Students in AI

    Perfect for students pursuing degrees related to AI who want practical experience in language model development.

  • Industry Professionals

    Structured for tech professionals wanting to deepen their knowledge in natural language processing and AI applications.

Not Suitable For
  • Casual Learners

    Individuals looking for light reading or general knowledge won’t find this technical guide engaging or suitable.

產品敘述

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AI & Machine Learning Editorial Review

Build a Large Language Model (From Scratch) is a foundational guide for anyone looking to grasp the mechanics behind large language models (LLMs). The book, published by Manning Publications, emphasizes a hands-on approach with practical code examples that can be executed successfully, making it suitable for those with basic Python skills. Readers appreciate the clarity and friendly tone of the writing, which breaks down complex concepts like tensors and the attention mechanism. The book is praised for its ability to provide a deep understanding without delving into heavy mathematics, making it accessible for engineers and hobbyists alike who wish to build their own models.

Customer Reviews & Ratings

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優點

  • Clear and friendly writing style
  • Hands-on coding examples
  • Suitable for intermediate Python users
  • Explains complex concepts simply
  • Great for understanding NLP applications

缺點

  • Not for those wanting to go too deep into theory

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