90% of what I work on is grounded in just a handful of resources...
In a world full of content overload, here are 6 books that cut through the noise 👇
𝗠𝘆 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀:
Start building first, and read these alongside - don’t wait to “finish” learning before you start doing!
1️⃣ 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 by 𝗣𝗲𝘁𝗲𝗿 𝗕𝗿𝘂𝗰𝗲 & 𝗔𝗻𝗱𝗿𝗲𝘄 𝗕𝗿𝘂𝗰𝗲
A must-have foundation that stays useful across roles.
🔗 Book link - https://lnkd.in/dBWucpRX
2️⃣ 𝗡𝗟𝗣 𝘄𝗶𝘁𝗵 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 by Lewis Tunstall, Leandro von Werra & Thomas Wolf
A solid foundation before diving into LLMs. Its highly important to know the pre-LLM era - comes very handy when working on Language data.
🔗 Book link - https://lnkd.in/diUjyCeg
3️⃣ 𝗛𝗮𝗻𝗱𝘀-𝗢𝗻 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 by Jay Alammar & Maarten Grootendorst
From embeddings to RAG to fine-tuning - complete coverage
🔗 Book link - https://lnkd.in/ddb7kZ7D
🔗 Code link - https://lnkd.in/de52uRmQ
4️⃣ 𝗗𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 by Chip Huyen
The perfect start for understanding real-world ML development. Covers everything from data pipelines to model deployment.
🔗 Book link - https://lnkd.in/dY8NJMRk
5️⃣ 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲 𝘀𝗶𝗴𝗻 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 by Ali Aminian & Alex Xu
A goldmine for structured thinking. Not just for interview preparation - it gives a good overview of how complex ML system could get, this definitely broadens your thoughts.
🔗 Book link - https://lnkd.in/dC4V5mbi
6️⃣ 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 by Chip Huyen
Covers LLMs, RAG systems, infrastructure choices, multi-modal setups - very current, very practical.
🔗 Book link - https://lnkd.in/dqwDjHVa
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