제품번호 #:
221979662
Build a Large Language Model (From Scratch)
제품번호 #:
221979662
KRW 136508
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This is an engaging book that covers each stage of the process, from planning and coding to training and fine-tuning.
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What Stands Out
Comprehensive Guide
This book offers an in-depth exploration of building large language models from scratch, making it suitable for both beginners and experienced developers seeking a thorough understanding of the subject.
Practical Examples
Featuring practical examples and step-by-step instructions, readers can implement their own models, ensuring that theoretical knowledge translates into actionable skills for real-world applications.
Latest Techniques
Updated with the latest advancements in natural language processing, this guide equips readers with current methodologies and best practices, providing a competitive edge in the rapidly evolving field of AI.
제품 세부 정보
| Publishers | Manning publications |
| Date of publication | November 1, 2024 |
| Language | English |
| Print Length | 368 pages |
| ISBN-10 | 1633437167 |
| ISBN-13 | 978-1633437166 |
| Dimensions | 18.73 x 1.78 x 23.5 cm |
제품 설명
Build a Large Language Model (From Scratch)
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고객 질문 및 답변
-
의문:
What prior knowledge do I need to get started?
답변: You need intermediate Python skills and some knowledge of machine learning. -
의문:
Can I use this book on any laptop?
답변: Yes, the LLM you create will run on any modern laptop and can optionally utilize GPUs. -
의문:
Does the book provide any additional resources?
답변: Yes, purchasing the print book includes a free eBook in PDF and ePub formats.
İngilizce Baskı Sebastian Raschka Format: Kağıt Kapak Editorial Review
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- Some concepts may require prior knowledge.
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KRW 136508
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특징 및 장점
- Learn to build large language models (LLMs) from scratch.
- Step-by-step guidance from renowned author Sebastian Raschka.
- Hands-on approach covering planning, coding, training, and fine-tuning.
- Develop your own LLM that runs on an ordinary laptop.
- Includes a free eBook with the print purchase for easy access.
- Gain a deep understanding of LLMs and their customization methods.
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