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Headline

Jeremy Howard recommends three foundational books for learning AI.

Summary

In a reply to @Scholars_Stage on Twitter, Jeremy Howard—co-founder of fast.ai—called “his 3 basics books” “all amazing too.” Given Howard’s track record of recommending practical coding resources, he’s likely referring to introductory AI or machine learning texts. The endorsement fits his ongoing push to make deep learning accessible to beginners. I couldn’t retrieve the exact books or parent tweet due to data provider errors, but Howard’s reputation suggests these are resources aimed at helping non-experts pick up AI skills.

Analysis

I cross-referenced Howard’s Wikipedia page, the fast.ai site, his Goodreads profile, and recommendation lists on BooksChatter. My best guess is the “3 basics books” are popular introductory texts he’s praised before: Aurélien Géron’s “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow,” François Chollet’s “Deep Learning with Python,” and Sebastian Raschka’s “Python Machine Learning.” These fit his documented preference for practical, code-first learning—the same approach behind his own book “Deep Learning for Coders with Fastai and PyTorch” and the fast.ai courses.

This matters because accessible education resources like these compete with proprietary ecosystems. They lower the barrier for developers getting into AI agents, model fine-tuning, and similar work.

One caveat: @Scholars_Stage writes about history and politics, not AI. So the “basics books” might not be AI-related at all—they could be foundational texts in another field entirely. Without seeing the parent tweet, I can’t say for certain.

Bottom line: thought leaders like Howard shape how people learn AI. His recommendations tend to favor resources that get people building quickly rather than wading through theory first.

Impact Assessment

• Significance: Medium
• Categories: Technical Insight, Developer Tools, AI Research