Item #:
194533706
Build a Large Language Model From Scratch
89% of respondents would recommend this to a friend
Item #:
194533706
XCD 197
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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
A detailed roadmap for creating a large language model, covering every stage from data collection to deployment, ensuring users gain thorough competencies in each essential aspect.
Hands-On Techniques
Includes practical methodologies and code examples to help users learn by doing, fostering a deeper understanding of the complexities involved in language model development.
Customizable Framework
Provides a flexible architecture that allows users to tailor the language model to specific requirements, enhancing usability and performance for diverse applications.
Product Details
- 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 LLM • Prepare a dataset suitable for LLM training • Fine-tune LLMs for text classification and with your own data • Use human feedback to ensure your LLM follows instructions • Load 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. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. 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. About the book Build a Large Language Model (From Scratch) is a practical and eminently-satisfying hands-on journey into the foundations of generative AI. Without relying on any existing LLM libraries, you’ll code a base model, evolve it into a text classifier, and ultimately create a chatbot that can follow your conversational instructions. And you’ll really understand it because you built it yourself! What's inside • Plan and code an LLM comparable to GPT-2 • Load pretrained weights • Construct a complete training pipeline • Fine-tune your LLM for text classification • Develop LLMs that follow human instructions About the reader Readers need intermediate Python skills and some knowledge of machine learning. The LLM you create will run on any modern laptop and can optionally utilize GPUs. About the author Sebastian Raschka is a Staff Research Engineer at Lightning AI, where he works on LLM research and develops open-source software. The technical editor on this book was David Caswell. Table of Contents 1 Understanding large language models 2 Working with text data 3 Coding attention mechanisms 4 Implementing a GPT model from scratch to generate text 5 Pretraining on unlabeled data 6 Fine-tuning for classification 7 Fine-tuning to follow instructions A Introduction to PyTorch B References and further reading C Exercise solutions D Adding bells and whistles to the training loop E Parameter-efficient fine-tuning with LoRA
| Publisher | Manning |
| Publication date | October 29, 2024 |
| Language | English |
| Print length | 368 pages |
| ISBN-10 | 1633437167 |
| ISBN-13 | 978-1633437166 |
| Item Weight | 612 g |
| Dimensions | 7.38 x 0.7 x 9.25 inches (18.7 x 1.8 x 23.5 cm) |
Who Should Buy?
-
Aspiring Data Scientists
Those eager to understand machine learning fundamentals and practical applications will find this product immensely beneficial.
-
AI Enthusiasts
Individuals with a passion for artificial intelligence can explore and create models, deepening their knowledge and skills.
-
Tech Educators
Educators in computer science or AI can utilize this resource to teach large language model concepts effectively.
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Casual Learners
Those seeking a light introduction to AI may find this product too technical and intensive for their needs.
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English Edition Sebastian Raschka Format: Paperback Editorial Review
Build a Large Language Model From Scratch, published by Manning, is set to release on October 29, 2024. This comprehensive guide, with a print length of 368 pages, delves into the intricacies of developing advanced language models. Written in English, it explores various methodologies and techniques essential for understanding the architecture behind these models. With an ISBN-10 of 1633437167 and ISBN-13 of 978-1633437166, the book is designed for both beginners and experienced developers needing to deepen their knowledge in this rapidly evolving field. The dimensions of the book make it a convenient addition to any developer's toolkit.
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Pros
- Detailed insights into language model development
- Suitable for both beginners and experts
- Comprehensive guide across 368 pages
- Includes latest methodologies in AI
- Clear explanations of complex concepts
Cons
- No reviews available yet to gauge reader feedback
Product Price History
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XCD 197
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Features & Benefits
- 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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