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I think to be successful at machine learning you also need a good understanding of calculus, besides probability and linear algebra.
by flor1s 10y ago
I think to be successful at machine learning you also need a good understanding of calculus, besides probability and linear algebra.
- gugagore 10y agobut perhaps not too much: https://news.ycombinator.com/item?id=9189553 https://news.ycombinator.com/item?id=9189553
- curuinor 10y agoStrang's complaint is that there's too little linear algebra. This is true. This doesn't overshadow the fact that you're not going to get out of using some partial derivatives in neural net land (and many other AI subfields).
- flor1s 10y agoWhat is too much though? Backpropagation uses derivatives, some filters in Computer Vision use multivariate calculus. If you want to have a thorough understanding then calculus is necessary. That said, Andrew Ng was quite good at avoiding calculus in his Machine Learning MOOC, and for applied machine learning I guess calculus is not that important. A great place to study about math is www.khanacademy.org, they have courses on calculus, probability/statistics and linear algebra.