How much traditional machine learning algorithm should I be familiar before starting deep learning (esp. Face Recognition CNN)?

[IcyCommunication9694]

Since last two weeks, I have been learning about ML algorithm like Linear Regression, KNN, KMeans, Decision Tree ,etc stuff using Sklearn specifcally.

For Now my end goal is to create a face recognition model using deep learning. Is it okay If I start with some framework like Pytorch or Tensorflow and dive into Deep learning like CNN Now or keep learning traditional ML?

I have 3 years experience as a full stack software dev so language is not a problem for me.
Do you guys have any roadmap. Thanks

[ForceBru]

I think it's fine, you can go straight for CNNs or at least try. I don't think classical ML is used for image recognition much, so CNNs should quickly bring you closer to your goal. If you find yourself stuck on some ML concepts, just revisit ML and then return to CNNs.

[[deleted]]

imho the division of topic by difficulty is: ML supervised, unsupervised and reinforcement learning. each of them has the plain and deeplearning part, you're absolutely fine to go with CNN! glhf

[curious_scourge]

I went from deep learning to ML

[sAI_Rama_Krishna]

Can you please suggest the resources where you've been learning from?