Cross-Entropy Loss Log-likelihood Perspective
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- Опубліковано 3 лют 2020
- This is a video that covers Cross-Entropy Loss Log-likelihood Perspective
Attribution-NonCommercial-ShareAlike CC BY-NC-SA
Authors: Matthew Yedlin, Mohammad Jafari
Department of Computer and Electrical Engineering, University of British Columbia. - Наука та технологія
Great lectures. Thank you Matt Yedlin.
I really like that you guys hand clean the clear board. Feels like back in the classroom + math teather's small math talk!
love your video hope to see more of it.
you just cleared my doubt about likelihooh and log likelihood which I had for past yaer and half.
Thank you so much
Well explained using human language.
thanks!
Muhammad is a good student
Hi, thank you so much! I am self lerner, with no much of formal background. Can you please explain how SUM p_i log q_i is entropy, because it does not have minus sign. If it would be log (1/q_i) we would get minus sign out of it but its not. I'm stuck there...
Nice
I want to know how you can write on the mirror and record it.
The camera sees the writing flipped through a mirror.
Can the old guy.
*(EDIT: Solved it, see comment reply)*
I don't follow how you go from the case of numerical example, where the likelihood is a product of predicted and observed probabilities p_i and q_i each raised to the number of times they occur, to the algebraic expression of the likelihood where you take the product of q_i raised to N * p_i (or is that N_p_i? I'm a little unsure if the p_i is a subscript of the N or multiplied by it).
I worked it out. The answer is to remember that the number of times the outcome i, with probability p_i occurs can be expressed by rearranging the definition p_i = N_p_i / N and substituting this into the expression for the likelihood in the general form that follows from the numerical example:
L = Π q_i ^ N_p_i ,
giving
L = Π q_i ^ N*p_i