Bitcoin is gearing up for a monumental leap to new all- time highs.🚨

太棒了先生🍅

great ..thanks

Hilbert has Norm expressed as Inner products.

y would be x - (1/alpha)grad f(x) at 11:44

if you understand one word in the whole fking video, then you have no life! Go make better use of your time.

Great job ! I am impressed !

in these two videos of RKHS you mentioned your videos in which you have talked about MMD and KL, how can I find them in your channel. what is the titles of the videos or playlist ? thanks.

so happy i find your channel. Good video!

HI , thanks for such good work , but what if I want to let the HMM learns directly distribution from data instead of me imposing the "gaussian" in probabilityType ? is this possible ? your input is highly appreciated

hello sir Farshad thanks about this clip. very good

are these types of models applicable / implementable on a cloud infrastructure like IBM Watson

Pretty impressive!!! I love the approach, can't wait to see an update

Thanks a lot for this great series . is there a prerequisite for this course or any text book

Thank you so much for your lecture!

Excellent explanation! For what purpose did you create these lectures?

Thanks!

It's great! Can you share this document in pdf, sir? Thank you very much.

These videos don't get the views they deserve. I do my best to share them wherever possible!

Some brilliant videos you have on your channel where can beginners start off , could you make a playlist that we can walk through to learn steb by step.

Will the optimization channel continue to be updated？

Hi! do you know how to use a predictor based on dissimilarity functions?

Can you provide an explanation on Truncated rayleigh flow method (rifle) please

awesome thanks!!

you should explain it with practical explanation so we could understand it better

Thank you!

Keep up the good work !

Brilliant

thats brilliant how have you measured the regime ? or can you just use volatility

For me selective inference is a new science which applies topology to data analysis. So it covers all bayesians systematically !!! It also covers information theory. So strange that we are talking about these important intuitions in 2020 and not in 2000.

The content is fairly technical. It would be nice if you could break it down/ give links/ state prerequisites

Maybe you should publish the code, because certain parts are impossible to read. Greetings!

سلام چطور میتونم با شما در ارتباط باشم؟

Farshad, good job, I have same idea to use Deep Reinforcement Learning for trading, but I am behind you and study RL now.