| I recommend against DL by Goodfellow. At this point it is pretty much outdated. Actually, anything specific to NNs is already outdated by release. You'd need the following background: - Linear Algebra - Multivariate Calculus - Probability theory && Statistics Then you need a decent ML book to get the foundations of ML, you can't go wrong with either of these: - Bishop's Pattern Recognition - Murphy's Probabilistic ML - Elements of statistical learning - Learning from data You can supplement Murphy's with the advanced book. Elements is a pretty tough book, consider going through "Introduction to statistical learning"[1]. Bishop and Murphy include foundational topics in mathematics. LfD is a great introductory book and covers one of the most important aspects of ML, that is, model complexity and families of models. It can be supplemented with any of the other books. I'd also recommend doing some abstract algebra, but it's not a prerequisite. If you would like a top-down approach, I recommend getting the book "Mathematics of Machine Learning" and learning as needed. For NN methods, some recommendations: - https://paperswithcode.com/methods/category/regularization - https://paperswithcode.com/methods/category/stochastic-optim... - https://paperswithcode.com/methods/category/attention-mechan... - https://paperswithcode.com/paper/auto-encoding-variational-b... For something a little bit different but worth reading given that you have the prerequisite mathematical maturity - Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges | https://arxiv.org/abs/2104.13478 [1] https://www.statlearning.com/ Many thanks to the user "mindcrime" for catching my error with Introduction to statistical learning. |
Was that supposed to be An Introduction to Statistical Learning[1] or maybe Introduction to Statistical Relational Learning[2]? I don't think there is a book titled Introduction to Elements of Statistical Learning?
[1]: https://www.statlearning.com/
[2]: https://www.cs.umd.edu/srl-book/