Guidance for ML breadth/Fundamentals
I have had my fair share of playing around with different Kinda off generic Ml models and projects but It had been mostly random and not organised.
So now i want to dive deep into some niche in this field maybe even understand some relatively easy research paper and work on it too.
Before doing all that I still want a very good base in the fundamentals. So as an experienced person in this field or a skilled student or a tutor do you have any suggestions for me maybe some resources which i can use . Also i must say that i am not planning to spend a lot of time(at most a month) for clearing the fundamentals as i am aware of most of the stuffs and wish to focus more on a specific concept as quickly as possible.
A fast paced onto the point approach will be really helpful. Are statquest videos good enough for this ?
Also i will really appreciate if you have anything else to tell me beyond what i asked for.
Thank you.
Talking product sense with Ridhi
9 min AI interview5 questions
Did you just invoke those models using python and see the results?
Or
Are you able to work out the iterations of a xor function using Neural Networks using a piece of paper. If yes, can you do the same for CNN. Can you explain why Fourier transformation is done? If the answer is no, start with your graduation books coupled with online resources. That should form a base for further understanding
Well yes i have had completed a graduation course involving perceptron learning, mp network ,backpropagation to update weights , numericals on competitive learning models etc usual course work.
Not the CNN and fourier one .
If you are in Bangalore, I suggest you look up for JedI institute (if they are still operating that is). Prof Vinay is a rockstar in every possible way and a great tutor for the subject.
Can you direct me to some link from where i can identify the person and reach to them . Thanks.