Item #:
142530159
Build a Large Language Model (From Scratch)
90% of respondents would recommend this to a friend
Item #:
142530159
XCD 202
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What Stands Out
Comprehensive Guide
Offers a step-by-step approach to building large language models from scratch, ideal for developers and researchers alike looking to deepen their understanding of AI and machine learning.
Practical Examples
Includes numerous real-world examples that help readers apply theoretical concepts, facilitating hands-on learning and ensuring readers gain practical skills in model development.
Expert Insights
Written by industry experts, the book provides unique perspectives and advanced tips, making it a valuable resource for both beginners and practitioners aiming to enhance their knowledge of AI technology.
Product Details
| Publisher | Manning Publications |
| Publication date | 29 Oct. 2024 |
| Edition | 1st |
| Language | English |
| Print length | 368 pages |
| ISBN-10 | 1633437167 |
| ISBN-13 | 978-1633437166 |
| Dimensions | 18.73 x 1.78 x 23.5 cm |
Who Should Buy?
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AI Enthusiasts
Individuals curious about artificial intelligence can learn from detailed insights and methodologies described in the book.
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Developers & Data Scientists
Professionals looking to implement and customize language models in their projects will find practical guidance invaluable.
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Students & Researchers
Academics wanting a thorough understanding of language models will benefit from comprehensive theories and hands-on approaches.
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Casual Readers
Those uninterested in technical details may find the content too complex and dense to engage with effectively.
Product Description
Build a Large Language Model (From Scratch)
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English edition Sebastian Raschka Format: Paperback Editorial Review
Build A Large Language Model (From Scratch) is a comprehensive guide to understanding the intricacies of AI and large language models (LLMs). The book, published by Manning Publications, is structured to suit readers with a basic grasp of neural networks and relevant mathematics, making it accessible yet informative. Reviewers commend its clear explanations and practical examples using PyTorch, including fine-tuning techniques like LoRA. It offers extra resources, including instructional videos and additional materials on GitHub, enhancing the learning experience. However, some find certain chapters challenging, though the appendix provides further clarification, ensuring readers can catch up if needed.
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Pros
- Clear explanations of LLM components and workings
- Practical examples using PyTorch provided
- Covers fine-tuning techniques like LoRA
- Includes additional resources and videos
- Accessible for those with basic neural network knowledge
Cons
- Some concepts may be challenging for newcomers
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XCD 202
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