Tech Books
Networks, Crowds, and Markets: Reasoning about a Highly Connected World
- David Easley
- Jon Kleinberg
Tech Books
- Algorithms
- Beej's Guide to Network Programming https://beej.us/guide/bgnet/html//index.html
- Building secure and reliable systems Building Secure and Reliable Systems: Best Practices for Designing, Implementing, and Maintaining Systems 1492083127, 9781492083122 - DOKUMEN.PUB
- Clean Code
- Database Internals - https://www.databass.dev
- Design and Build Great Web APIs - Mike Amundsen
- Designing Data-Intensive Applications by Martin Kleppmann
- Designing Distributed systems - Brendan Burns
- Distributed Programming - http://www.distributedprogramming.net/index.shtml
- Distributed systems for fun and profit
- Code Complete by Steve McConnell
- Coders at work
- Communicating Sequential Processes - C.A.R. Hoare
- Cracking the coding interview
- Closure - https://www.braveclojure.com
- Elasticsearch the definitive guide
- Google SRE - https://landing.google.com/sre/sre-book/toc/index.html
- Grokking Algorithms - https://www.manning.com/books/grokking-algorithms
- Hacker's Delight by Henry S. Waren
- High performance mysql - https://www.highperfmysql.com
- https://www.immutablearchitecture.com
- Inside the machines by Jon Stokes
- Introduction to Reliable and Secure Distributed Programming by Christian Cachin
- Kubernetes in action
- Kubernetes up and running
- Practical Grpc
- Pragmatic Programmer
- SciPy Lectures
- Site Reliability Engineering
- Site Reliability Workbook
- The Clean Coder
- The Mythical Man-Month: Essays on Software Engineering
- The self taught programmer
- Wireless Networking http://wndw.net/book.html
- Windows Internals Book - Sysinternals | Microsoft Learn
- Writing an Interpreter/Compiler in Go
- You don't know js https://github.com/getify/You-Dont-Know-JS
- A book of Abstract Algebra
- Introduction to Statistical Learning With Application in R, by Gareth James
- Deep Learning with Python by Francois Chollet
- SciPy Lectures
- Master Machine Learning Algorithms
- Statistics in Plain English
- https://leanpub.com/insidethepythonvirtualmachine/read
- https://leanpub.com/clean-architectures-in-python
- Don't Make Me Think, Revisited: A Common Sense Approach to Web Usability (Voices That Matter)
- Lean UX: Applying Lean Principles to Improve User Experience by Jeff Gothelf & Josh Seiden
- Building Microservices: Defining Fine-Grained Systems
- Head first Design pattern
- The C++ Programming Language 4th edition
- Accelerated C++
- Scaling-python https://scaling-python.com
- The Programmer's Guide To Theory: Great ideas explained by Dr. Mike James
- Zookeeper - Distributed Process Coordination by Benjamin Reed
32 Book Recommendations for the Holidays - Various Speakers - GOTO 2021
📕 Statistics Think Stats --
Probability and Statistics https://lnkd.in/gjAs_s9
Statistical Inference for Data Science https://lnkd.in/grU8ep7
Think Bayes -- Bayesian Statistics Made Simple https://lnkd.in/gW_ebEa
📗 Machine Learning
An Introduction to Statistical Learning https://lnkd.in/gqQkbcn The Elements of Statistical Learning https://lnkd.in/g78kwBp
Machine Learning Yearning http://www.mlyearning.org/
Deep Learning https://lnkd.in/g6HDwN5
📘 Data Science
Data Jujitsu by DJ Patil https://lnkd.in/gMS2tyA
Data Science for Business https://lnkd.in/g5H7G2b
R for Data Science http://r4ds.had.co.nz/
Python Data Science Handbook https://lnkd.in/gHWXixJ
📙 Programming
Automate the Boring Stuff With Python https://lnkd.in/gzNdUAb
R For Beginners https://lnkd.in/gzz-niK
📒 Other
Natural Language Processing with Python https://lnkd.in/gmuQcmt
The Data Science Handbook -- Advice & Insights from Data Scientists https://lnkd.in/g8t7hk9
AI Engineer
1. Hands-On Large Language Models
A practical toolkit for building and fine-tuning LLMs, from transformer basics to semantic search, RAG, and deployment. Packed with clear visuals and code examples to take you from zero to expert.
2. Designing Machine Learning Systems
A practical guide to building ML systems that are reliable, scalable, and easy to maintain. It covers the full ML lifecycle, from data and modeling to deployment and monitoring.
3. Practical MLOps: Operationalizing Machine Learning Models
A hands-on guide to moving models from development to production. Covers CI/CD, monitoring, testing, and choosing the right tools for MLOps on cloud platforms.
4. AI Engineering: Building Applications with Foundation Models
Teaches how to build real-world applications using foundation models. Covers prompt design, fine-tuning, retrieval-augmented generation, evaluation, and optimization.
5. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
A classic guide that walks you through ML and deep learning with real-world examples. Covers everything from regression to computer vision with beginner-friendly explanations and code.
Mathematics
https://www.freecodecamp.org/news/learn-the-history-of-the-internet-in-dr-chucks