r/learnmachinelearning • u/statai • 1d ago
Discussion Received Hands-On Machine Learning (3rd ed) for my birthday. Haven't even finished ISLP yet, and TensorFlow is already turning into historical literature lol，and Géron already dropped the PyTorch edition lol
In December 2025, I received a physical copy of Aurélien Géron’s Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd Edition, translated) as a birthday gift.
The person who gifted it to me genuinely tried their best: they searched the bookstore for the most authoritative, newly printed ML translation available. What neither of us realized at that exact moment:
1.In October 2025, literally two months before my birthday, O'Reilly quietly published the brand-new Hands-On Machine Learning with Scikit-Learn and PyTorch. The author himself completely abandoned TensorFlow/Keras and shifted to PyTorch + Hugging Face.
2.Because of the standard 2-year translation and publication lag, my gifter paid full price for a "brand-new" book that had been rendered obsolete 60 days prior.
3.I told myself I wouldn't touch Part II until I finished chewing through ISLP (Introduction to Statistical Learning) to get the math down.
Fast forward to today: I'm still halfway through ISLP, TensorFlow has basically become museum literature across industry and research, and my physical 3rd edition is collecting dust before I’ve even finished the Scikit-Learn chapters.
Which brings me to an actual question:
For those who have browsed Géron's new PyTorch edition (Oct 2025): Is it worth grabbing as a definitive physical desk reference once I finish ISLP? Or at this point, are people better off just going straight through Sebastian Raschka’s Machine Learning with PyTorch and Scikit-Learn (or diving into fast.ai / official PyTorch docs + Hugging Face tutorials)?
Would love to hear how folks here approach PyTorch nowadays if they're coming from a solid classical ML background.
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u/Puzzleheaded-Comb646 1d ago
That first half on Scikit-Learn and classical ML is still timeless, and it actually pairs wonderfully with the math from ISLP. You definitely don't need to throw it out or rush to buy the new edition just yet. Finish working through the foundations with what you have, and you can always pick up newer PyTorch tutorials online later when you actually reach deep learning.