The CS degree you never got
A complete introduction to computer science for self-taught developers.
Written by Tom Johnson
Sound familiar?
- You can ship features, but you don’t know how far to trust what your AI assistant is telling you.
- You only find out about an important concept after an embarrassing code review or causing a production incident.
- You know you have gaps, but you don’t have time to read a dozen giant computer science textbooks.
The problem is that you don’t know what you don’t know.
This book fixes that. It gives you the fundamental building blocks you need to succeed.
I know the feeling. I once brought down a system during a busy sales period because I didn’t know what a file handle was. Turns out you can run out of them and crash the system!
I spent five years reading textbooks and watching lectures to fill my own knowledge gaps, then turned the useful parts into a practical map for working developers.
“Incredibly helpful for building the missing foundations behind highly abstracted systems.”
Kelven Opoku, backend engineer in Big Tech
Buy the ebook - $29- ✅ 28-day money-back guarantee
- ⭐ 4.5 stars on Amazon
The Computer Science Book: For Self-Taught Developers
Learn the computer science fundamentals from logic gates to LLMs
The building blocks you need
The Computer Science Book is a guided, opinionated walk through the core areas of a computer science degree. It’s aimed at anyone who needs a practical understanding of computer science fundamentals.
This is not a thousand-page reference manual. It is a curated introduction to the parts of computer science that working developers actually need to know.
You’ll build intuition across thirteen chapters ranging from the logical foundations to the bleeding edge of AI research:
- Machine learning – the essential primer on the techniques of the AI revolution
- Deep learning – how neural networks function and how to use them well
- Large language models & AI – how LLMs and AI agents actually work
- Computer architecture – how your computer works from logic gates up
- Operating systems – processes, virtual memory and filesystems in depth
- Theory of computation – what computers can and can’t do
- Algorithms and data structures – how to reason about data and performance
- Networking – what actually happens between your browser and the server
- Concurrent programming – modern programming for maximum performance
- Distributed systems – resiliency and coherency at web scale
- Programming languages – how syntax and semantics determine paradigms
- Databases – how to store data securely and query efficiently
- Compilers – how your source code actually turns into a program
Each chapter builds up an intuitive understanding of the most important topics. You’ll map out the territory and understand how everything fits together.
When you want to go deeper, comprehensive Further Reading sections signpost more advanced topics and resources.
An essential read for anyone who felt they missed out on a CS education. The further reading sections are full of curated resources to explore each topic more deeply. Tom distils each topic beautifully and succinctly. A joy to read.
Adrian Booth
Software engineer at Syft
Covers all the topics I lacked confidence in. Now I feel like I have a solid base to start from if I need to dive deeper. I’ve been able to apply the content to my day-to-day work. I really recommend this book.
John Whiles
Software engineer at Contentful
Three new chapters for the age of machine learning
The second edition extends the systems foundation with a practical map of modern AI.
You still need the old fundamentals. But now you also need enough context to place machine learning and LLMs inside the rest of computer science.
Machine Learning
From linear regression to unsupervised learning, building the intuition you need to understand the statistical, data-driven approach to programming.
Deep Learning
A deep dive into how modern neural networks learn from images, text, and sequences, and where their power actually comes from. Includes generative techniques like Stable Diffusion.
Large Language Models and AI
Goes from tokenisation and transformer architectures, through reinforcement learning at mid-training right up to the bleeding edge of interpretability, world models and robotic agents.
Paperback and hardback are available on Amazon.
About the author
Tom Johnson is a senior software engineer who has worked across startups, investment banks, and now Big Tech.
He took the self-taught path: Founders & Coders bootcamp in London, then years of learning on the job, plugging his knowledge gaps the hard way through textbooks, lectures, and the occasional production mistake.
The Computer Science Book is the map he wishes he’d had when he was already writing software, but still missing the deeper models underneath it.
Free Articles & Tutorials
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What is an API?
One of the joys of studying computer science is spotting a familiar concept in new surroundings. It’s a wonderful “aha!” moment as …
If you have any questions, please feel free to get in touch: tom@thecomputersciencebook.com.