Get some basics of linear algebra down. Eigenvectors, Eigenvalues. Nail down Matrix Factorization, Principal Components, and the relationship between the two.
Learn softmax, logit function, different activation functions. When to use them. Difference between classification, binary classification, multi label prediction etc. Theyre all similar, just use a few different functions in the neural net
After this, go through some optimization theory and learn the different algorithms for optimizing neural nets, i.e. Adam vs RMSProp.
Then I would just get a list of all the top network architectures, then go through their white papers. Do this chronologically. Start at ~2012. Basically all the network architectures build on each other. So take the first good working deep CNN (alexnet), find out why it worked. Then move to VGG, why did that one work? What problems were solved? then move onwards.
^Do this for computer vision, then again for NLP (Word Vectors) and transformers (BERT, XLNet, etc).
Then youre done.
Theres also GANs etc, but that stuff is extra.
From there, choose whatever specialty you wanna research, and just grab the state of the art.
Learn softmax, logit function, different activation functions. When to use them. Difference between classification, binary classification, multi label prediction etc. Theyre all similar, just use a few different functions in the neural net
After this, go through some optimization theory and learn the different algorithms for optimizing neural nets, i.e. Adam vs RMSProp.
Then I would just get a list of all the top network architectures, then go through their white papers. Do this chronologically. Start at ~2012. Basically all the network architectures build on each other. So take the first good working deep CNN (alexnet), find out why it worked. Then move to VGG, why did that one work? What problems were solved? then move onwards.
^Do this for computer vision, then again for NLP (Word Vectors) and transformers (BERT, XLNet, etc).
Then youre done.
Theres also GANs etc, but that stuff is extra.
From there, choose whatever specialty you wanna research, and just grab the state of the art.