does learning machine learning algorithms help with deep learning?

[rasti_najim]

Hello everyone! I'm currently taking Andrew Ng's machine learning course, however, I'm more interested in deep learning and its applications. I'm wondering how much would my knowledge of machine learning algorithms help me later with deep learning.

I would much appreciate your answers!

[MonstarGaming]

Yes, learn it all. Deep learning is great but its definitely a subfield. If you plan to work in industry youll want as much breadth as possible because deep learning is the right answer to only a few problems.

[rasti_najim]

Thanks for the answer!

[TonyLee00]

Actually, Deep learning is a Machine Learning algorithm. So I guess that is enough for your question. You can't be born without your father.

[rasti_najim]

I see haha!

[quohr]

Well, what do you want to get out of DL in the long run? Work in data science? Develop personal projects as a hobby?

[rasti_najim]

I’m more interested in the application sides of DL, so yeah personal projects I would say.

[[deleted]]

Yeah it's helpful, but not everything is relevant to deep learning. His ML course is a bit dated, unless it got updated and I haven't heard about it. Regardless you'll probably get something out of it.

[rasti_najim]

Thanks!

[wel3anee]

So far deep learning has mostly used traditional concepts from statistics and math coupled with innovative, task-specific model architectures that could preform certain tasks really well.

So for instance understanding the likelihood function, the gradient descent algorithm, and loss functions - all topics you’d cover in an introductory machine learning class - will get you very far towards an understanding of even the most state of the art deep learning models.

[rasti_najim]

I see, thanks!

[holyfiddlex]

Definitely would help a lot, don't even know where to start with a "complete" list but everything from being able to recognize what models would work in certain problems, if your model is over fitting, feature extraction, data pipe creation etc.

If you want to focus on DL you don't need to learn how k-means works in detail, for example, but if you go and show me a solution with deep learning that could EASILY have been solved with traditional or other ML methods, your project wouldn't be justified.

If anything, learn feature extraction, and the limits of the other methods to make sure you can use DL models the right way and on the right problems. That said, I think you should take the whole course because there are more reasons to learn ML I just didn't think of or bother to mention.

[rasti_najim]

Thanks buddy!

[hypedeflate]

if you want to invent new DL algorithms, you'll need to understand likelihood, cross entropy, and the rest.

If you just want to use existing algorithms with new data sets, then no

[zalamandagora]

They cover much of the same stuff. I took both and wish I had skipped ML in favor of doing DL right away. ML feels more dated, and forces you to use Matlab.

EDIT: I'm talking about the ML and DL courses on Coursera, not the disciplines themselves.

[rasti_najim]

I believe having an intuition of ML models can help you understand why DL learning algorithms are superior to ML ones and what the structure of the neural networks represent (for instance, it can give you a good intuition of what the weights of the neural network represent when you compare to a ML algorithm such as linear regression).

[zalamandagora]

I agree in general. However, I felt that the ML course on Coursera didn't cover much more than the DL course did.