So I wanted to get into ML using Python recently and I was wondering about which ML library I should learn as a ML beginner first. I’ve been using Python for a few years now.
Linear and logistic regression are much easier (and less error prone) to implement from scratch than neural network training with backpropagation.
That way you can still follow the progression I suggested: implement those regressions by hand using numpy -> compare against (and appreciate) sklearn -> implement SVMs by hand using cvxpy -> appreciate sklearn again.
If you get the hang of “classical” ML, then deep learning becomes easy as it’s still machine learning, just with more complicated models and no closed-form solutions.
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So I should learn sklearn first before pytorch to understand the basics?
Linear and logistic regression are much easier (and less error prone) to implement from scratch than neural network training with backpropagation.
That way you can still follow the progression I suggested: implement those regressions by hand using numpy -> compare against (and appreciate) sklearn -> implement SVMs by hand using cvxpy -> appreciate sklearn again.
If you get the hang of “classical” ML, then deep learning becomes easy as it’s still machine learning, just with more complicated models and no closed-form solutions.
Aight thanks.