Pattern Recognition and Machine Learning

After that, I'm afraid I think you do need to go "academic", if by that you mean learning some of the underlying math to approach AI / ML from a more rigorous probabilistic perspective. I'd recommend studying probability theory and then working your way through Bishop's Pattern Recognition and Machine Learning.

on HN, 2017

Chris Bishop's Pattern Recognition book is also very good, but it's not the same sort of book. Bishop is exhaustively deep on the narrower range of ML that he covers, but you won't get the same sort of coverage of the wider view of the field.

on HN, 2011

If you just want Deep Learning and statistical methods, then Bishop's Pattern Recognition and Machine Learning is a good start.

on HN, 2017

I liked the first two chapters of Bishop's PRML for that.

on HN, 2018

For a more fundamental take on that stuff (but less robotics specific), "Pattern Recognition and Machine Learning" by Bishop is my favorite.

on HN, 2023

Once you get your feet wet, the first year PHD course book for good theory is Pattern Recognition and Machine Learning by Bishop. But it could be a bit too theoretical - it provides a foundational mathematical framework and got me thinking about problems in a better way.

on HN, 2017