Build a Large Language Model (From Scratch)
Comprehensive guide to understanding and building large language models from first principles.
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How the 7.3 was reached
- Quality of output8/10
Companion repository has 105,731 stars and only 3 open issues, showing very heavy adoption; content descriptions are vendor-only. Source: github.com
- Ease of use7/10
Official code repository has download options and setup guidance to follow the book step by step. Source: github.com
- Pricing value6/10
Paperback $38 to $49.99, Kindle $49.99; in line with technical books, and the code is free on GitHub. Source: github.com
- API and integration qualityn/a
No evidence found, so this criterion is left out of the score rather than counted as zero.
- Solves the problem it claims to solve8/10
Code and text build a GPT-like model as promised; 105,731 stars show wide independent adoption by learners. Source: github.com
7.3/10 is the average of the 4 criterion scores above. One criterion had no evidence and is left out, so this score rests on 4 of the five. A tool needs at least three to get a score. The evidence comes from one source.Scores come from public evidence (the tool's own pages and documentation, and independent reviews); we do not test every tool hands-on. Scored 29 September 2026, on version 1 of our five-criteria scoring method.
Pricing
Billing period not listed
- Physical book
- Complete implementation guide
- Step-by-step tutorials
Billing period not listed
- Digital format
- Searchable content
- Instant download
Billing period not listed
- Durable binding
- Premium format
- Reference quality
For the full list of what each plan includes, and current prices, see github.com.
Key Features
- Step-by-step LLM construction
- Python implementation examples
- Mathematical foundations explained
- Practical coding exercises
- Architecture deep-dive
Pros & Cons
Pros
- Comprehensive technical depth
- Hands-on implementation approach
- Clear explanations of complex concepts
- Written by ML expert Sebastian Raschka
Cons
- Requires strong programming background
- May become outdated quickly
- Time-intensive learning curve
- Limited to educational content only
An excellent educational resource for developers and researchers wanting to understand LLM internals. Best suited for those with solid Python and ML foundations who want practical implementation knowledge.
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