(Redacted from a AIDL's discussion.)
Q (First asked by Shezad Rayhan, Redacted): "I bought some books on ML,Deep Learning ,RL seeing the reviews on amazon and quora.
[Arthur: the OP then listed out ~10 books on different subjects such as DL, ML, RL.]" .....
"I saw few lectures of Geoffrey Hinton's Neural networks course but not sure which text book has similarity with his lectures."
A: "Good question. Thanks for writing it up. My 2cts:
You bought too many books. Here are the few books to focus on
- Python Machine Learning (Sebastian Raschka )
- The Elements of Statistical Learning (Trevor Hastie,Robert Tibshirani,Jerome Friedman )
- Machine Learning A probabilistic perspective (K Murphy)
- Pattern Recognition and Machine learning (Bishop)
- Deep learning (Ian Goodfellow,Yoshua ,Aaron Courville)
- Neural networks and learning machines(Simon Haykin)
I never read Raschka's and Murphy's book but there are many good comments. Raschka's books is more for practical use of machine learning so that's probably the best place to start. For other 5, if you can read through one of them with ease, you should already be able to get a job or do research somewhere.
To your question about Hinton's: not every lectures come with a textbook. Hinton's class is unique in the sense that *he* represents a point of view, so you have to delve into his or his students' paper.