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.
Maybe find some code to look at on the HuggingFace hub page? HuggingFace libraries or PyTorch are likely to give you really good learning opportunities and examples. Just keep an eye out for timestamps of articles or version numbers. And of course use venv/conda/… to not mess up your version when trying out different things 😉
AlmightySnoo@lemmy.world 1 year ago
I’d say since you’re a beginner, it’s much better to try to implement your regression functions and any necessary helper functions (train/test split etc…) yourself in the beginning. Learn the necessary linear algebra and quadratic programming and try to implement linear regression, logistic regression and SVMs using only
numpy
andcvxpy
.Once you get the hang of it, you can jump straight into
sklearn
and be confident that you understand sort of what those “blackboxes” really do and that will also help you with troubleshooting.Asudox@lemmy.world 1 year ago
So I should learn sklearn first before pytorch to understand the basics?
AlmightySnoo@lemmy.world 1 year ago
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.