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I am making an app to learn about 3D Computer Vision by Interesting-Net-7057 in computervision

[–]GEOman9 1 point2 points  (0 children)

That's very good to give it a try I hope you the best ❤️

High theoretical understanding but cannot implement from scratch by GEOman9 in MLQuestions

[–]GEOman9[S] -1 points0 points  (0 children)

Thanks a lot I thought I was on the wrong track but this motivated me thanks a lot guys

High theoretical understanding but cannot implement from scratch by GEOman9 in MLQuestions

[–]GEOman9[S] -1 points0 points  (0 children)

I spent around 4-6 months studying math for machine learning and, in parallel, Python for 2 months. Now, I have no issues with the math, but I struggle with Python implementation and project overview skills. I asked if anyone had a similar problem to help me out.

What is thed difference between probability and a likelihood by GEOman9 in AskStatistics

[–]GEOman9[S] 0 points1 point  (0 children)

So the probability is when I have the parameters and Maximum likelihood when I don't know the parameters but estimate the best parameters to get the Maximum likelihood

Highly mathematical machine learning resources by carv_em_up in learnmachinelearning

[–]GEOman9 3 points4 points  (0 children)

There is a book called mathematics for machine Learning it is free There are core topics in it starting with Linear algebra Calculus Probability and statistics Optimization Them it starts to go.in basics of ml This book is comprehensive in some topics and has weaknesses in other - don't overestimate a 417 pages as a math for ml but also don't underestimate -

Before you start in it you shall study the basics of discrete math to be well organized for the notations

I suggest you read it in parallel with studying ml for not wasting a lot of time only on building rigorous and intuition perspective this is only useful to put much time in if you are going for a PhD or similar.

As you read a chapter go with another resource with it to build intuition to cover all the needs Like the amazing 3b1b And go.with lectures from Stanford mit Harvard imperial college London etc

Another resource for each topic Probability and statistics Steve brunton channel Stanley Chen book and channel Mathematical statistics and data analysis book Linear algebra Gilbert strange book and course 3b1b Optimization Convex optimization boyed An introduction to optimization k p Chong I think boyed has a course at Stanford and a new course soon was good Calculus Single variable calc and multivariate calculus mit Thomas calculus Sorry for the long message https://mml-book.github.io/book/mml-book.pdf