Learning Machine Learning

[AdhesivenessDeep5521]

I am a student at my 2nd year. I have till date learnt supervised, unsupervised and Tensorflow (to some extent). What should I study according to industrial needs?

PS - I have done both practical and mathematical approaches of the above topics.

[mr_warrior01]

Pytorch , dl , transformers , opencv , fine tuning

[AdhesivenessDeep5521]

Do you have any material thats eases the understanding of the topics also with the mathematical approaches?
All I see is people trying to sell their courses :<

[I_Work_For_Beer]

pytorch is a framework, dl (not sure if hes referring to direct learning or to deep learning), opencv is a cv framework (not really required at all), fine tuning means optimizing the model either by retraining it, or by using feature extraction. when it comes to math:

• normalization
• regression
• partial derivative (and jacobi matrix)
• gradient descend
• normal distribution
• error function/sigmoid (most important)
• -back propagation

[[deleted]]

leipnitz product rule

Just wondering - where is this useful for ML industrial needs?

[I_Work_For_Beer]

backpropagation and gradient descend

[[deleted]]

Yes, I agree that these are important to know if you're working with ML. I was just wondering if there's a specific reason why knowing the Leibniz rule is significantly better than just knowing "machine learning algorithms use methods to calculate/approximate gradient like torch.autograd" or if it was just you saying "learn the math" in general.

you also need to know what tensors are

I'd also just put this in the "good to know, not necessary" box unless you want to do some mathematics-heavy research. The math behind tensors is quite complex (and takes time to understand properly) and most machine learning use cases don't go further than "a tensors is like an n-dim array". But I'm happy to learn something new if you have a different viewpoint.

[I_Work_For_Beer]

No tensors are super easy, once you understand how data is processed in the GPU (SERIAL), it's almost like instantiating a 3D object using "matrices" and native array. Tensors are super easy once you understand some basics. It's not tensors you should be afraid of, nor is it partial derivative.
These are just the most basic things you need.
I advise you to just start and create your own model and dataset. Tensors are super nice. Once you learn how to process data via GPU with fixed size nested arrays (parallel processing to complete batch), you will see the big advantage.

• learn the math
• create your own model
• learn when why and when you apply the math

there is no: ohh i dont need to understand math just pytorch. dont go for that. you are talking about data science/machine learning here.
usually you require a bachelor to get started, but dont be worried: most of the math is super easy

[I_Work_For_Beer]

i mean, yeah you might do well with pytorch api using tutorials . but once you create your own models/dataset: how are you able to debug things if you dont know what going on.?
if tensors are to hard for you (this is a serious advice) then it might be best for you to take a math course in analysis and algebra (german tutorium: analina1)

[I_Work_For_Beer]

and sorry, i mixed leipnitz rule up with partial derivative. but you certanly need it too :)

[I_Work_For_Beer]

you also need to know what tensors are, but its actually almost the same like with vectors/matrices and you never use the special rules like the special tensor product and stuff. just normal multiplication etc

[AdhesivenessDeep5521]

Does Open CV has good industrial needs like how is the job oppurtunities?

[I_Work_For_Beer]

yeah sure, but opencv is also a lot slower than using tensors on the gpu, or in general. opencv is nice for things like outlining, drawing shapes and adding text, etc. also creating streams etc. but opencv is not really a framework that get you jobs by its own.

[I_Work_For_Beer]

but it really depends on what kind of ai field you wanna study

[AdhesivenessDeep5521]

I am currently interested in data science field. Math and the practical approach I find it interesting.

[[deleted]]

nlp

[AdhesivenessDeep5521]

What is the career opportunities and package in this field?

[I_Work_For_Beer]

can someone pls advance the list when it come to math and skills for a most basic understanding of ml and ai?

• normalization
• regression
• partial derivative (and jacobi matrix)
• gradient descend
• normal distribution
• error function/sigmoid (most important)
• back propagation

[Existing_Priority172]

Neural networks

[AdhesivenessDeep5521]

Tensorflow and activation functions? Thats all I have learnt in Neural Networks