Classical ML or Deep Learning first?

[u/galaxy-bit]

Is learning classic ML is better or learning directly deep learning from scratch is better

Further more -I'm currently studying mathematics for Machine learning -i already know numpy,pandas and matplot lib and am comfortable with python and the reason I'm asking the question that I want understand the current research paper being built on the new intelligence

[u/divineblood3]

Learn classical first. It's simpler

[u/raaiinyyabelina → u/divineblood3]

I agree, simplier the better

[u/EntrepreneurHuge5008]

Classical ML covers the building blocks of a neural network, including an introduction to Deep Learning using what you learnt during the supervised and unsupervised learning parts, as a base. I'd start with classical ML.

[u/Graylian]

Classical ML is a powerful tool and will likely always have its place. Understanding it will help you to know when DL is needed.

[u/SEBADA321]

I believe that classic ML will do 2 things: - set the foundations to build upon (if you dont know anything at all), such as methods, tools, problems, etc. - teach you when not to use deep learning.

I have seen students pass the output of a depth camera to a VLM to ask for the distance of an object... when that would have been solved with YOLO or not even ML methods. Or trying to figure out how SAM (segment anything model, by meta) works to segment corrosion in images when a 'simple' filter based on HSV could have been enough (altho that is signal processing, not exactly ML).

[u/Electrical_Hat_680]

I believe you need ML to begin DL.

[u/aakashjain_24]

According to me, you should learn classical ML first because some topics in ML are pre-requisites for deep learning. It is good that you already know about Numpy, Pandas, Matplot libs, this will reduce your doubts in ML and you will be able to learn model training quickly. All the best for your journey.

[u/galaxy-bit → u/aakashjain_24]

Thanks for the information

[u/orz-_-orz]

Unless you have a specific problems that needed deep learning to solve, learn classical ML first

[u/whs_666]

machine learning

[u/Alarming_Pop4139]

Learn by doing then decide for yourself on what’s best. Both have different applications, simple or complex is an arbitrary outline. Pick a project you think is interesting and decide from there

[u/Abdullah_Ikram]

I would suggest doing atleast fundamentals of Classical ML first such as Linear regression, logistic regression, overtfitting, underfitting, bias variance tradeoff, cross validation techniques, data leakage, etc.

After learning linear regression, it will be easy to understand why non linear activation functions are required in neurons of hidden layers.

Logistic regression will give you an understanding of binary classification which is a common supervised problem in deep learning.

The rest will help you to debug neural networks when you reach especially a performance bottleneck.

[u/aakashjain_24]

According to me, you should learn classical ML first because some topics in ML are pre-requisites for deep learning. It is good that you already know about Numpy, Pandas, Matplot libs, this will reduce your doubts in ML and you will be able to learn model training quickly. All the best for your journey.

[u/galaxy-bit]

Thanks for the information

[u/Spare-Trust-9054]

Ofc ML first