Books

This repository is a curated bookshelf for software engineers and lifelong learners.

If you’re looking for a high-signal reading list (and want the actual files in one place), this repo is for you.

Why this repo

Reading random blog posts is useful, but books build depth.

This collection is focused on:

• Writing better code (clean design, refactoring, maintainability)
• Growing as an engineer (interviews, systems, data-intensive apps)
• ML/AI foundations (deep learning + the math/stats behind it)
• Markets/quant context (for those exploring finance)

Who this is for

• Beginners who want a guided path to fundamentals
• Working engineers who want to level up (code quality + architecture)
• ML/AI learners who want both practical and foundational books
• Curious builders exploring trading/markets

How to use this repo

• Pick a goal from “Suggested learning paths” below.
• Read consistently (even 20–30 minutes/day compounds fast).
• Use the repo as:
  • a library (download/open PDFs)
  • a reference (come back to chapters when you hit real problems)

Suggested learning paths

Path A: Become a stronger software engineer

1. Clean Code
2. Working Effectively with Legacy Code
3. Designing Data-Intensive Applications
4. Cracking the Coding Interview (when preparing for interviews)

Path B: Python mastery

1. Fluent Python
2. Python Cookbook (use as a reference alongside projects)

Path C: ML/AI foundations

1. The Elements of Statistical Learning (reference + foundations)
2. Deep Learning with Python (hands-on)
3. Deep Learning (Goodfellow/Bengio/Courville) (deeper theory/reference)

Path D: Markets/Quant curiosity

1. Trading and Exchanges
2. Algorithmic Trading with Interactive Brokers

Contributing

PRs are welcome.

• Add books that are widely respected and high-signal
• Prefer adding a short reason (1–2 lines) for why it’s worth reading
• Keep links working (relative links for repo files)

Note

If any file should not be in this repository due to licensing, please open an issue and it will be reviewed.

Books in this repo

1. Clean Code by Robert C Martin
2. Working Effectively with Legacy Code by Michael C. Feathers
3. Cracking the Coding Interview
4. Designing Data Intensive Applications by Martin Kleppmann
5. David Beazley Python Cookbook
6. Fluent Python 2015
7. Deep Learning with Python
8. Deep Learning by I. Goodfellow, Y. Bengio, A. Courville (MIT)
9. The Elements of Statistical Learning
10. Trading and Exchanges
11. Algorithmic Trading with Interactive Brokers (Python and C++)

External links

1. Alex Xu System Design (ByteByteGo PDF)

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