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King Greens

Thanks for your efforts, very well explained

Waelchi Light

Excellent; thank you!

Great explanation, finally wrapped my head around the topic. Thanks!

5:24 MLE vs LS

clear explanation, really appreciated.

great video, thanks for sharing!

This is the best explanation about qunatile loss function. Thank you!

Would absolutely love to be able to get a high res full picture of this to hang it on my dorm's wall and gradually cross/map it out as I progress through it : D

Wish you would have described Y better in the video … eventually figured it out had to go back and forth, but so far this makes a lot of sense, thanks.

Cool vid. But using = instead of <- in R is ick brah

My question is why is it necessary though? The prediction from an OLS give you the conditional mean of the normal distribution, we also already have a constant variance, from there we can then calculate any quantile we need.

This is the best VI tutorials I have ever seen. Great job and keep going!

מלך! הסברים ממש טובים, עזר לי מאוד

You're awesome !

very nice video!

Thanks so much for this great video. It helped a lot

Thanks so much brother! I was struggling with this so much. I am following NNs from scratch book by Sentdex and I was stuck at the derivative of softmax because I was not able to understand the notations. Now, I understand that the j=k is referring to the diagonal elements of the gradient matrix :) Thanks

Dear David. I want to create generator that will give me the random value - lifespan of healthy human. So we can call it “Gompertz distribution” generator. I need it for learning purpose. So the random lifespan =e^(b0+E). b0= const, E -random value of extremal distribution. Which value of b0 you can recommend? Can you also advise the way for creating extremal distribution generator? I have a ready-made normal distribution generator. It can be used to make an extreme distribution generator. You need to take a sample of size n and take a smaller number from it. Am I right? Which mean and sigma of the normal distribution generator should I take? And what is the sample size to take?

You are the best. Thank you very much for all your course I really enjoy watching each of them.

for question2, if a1=Wa0, the columns of W can learn something during training and become different but the rows of W are the same to each other

Pandas should be included in base Python. Any language that wants to call itself worthy of data analytics has to have a built-in dataframe data structure.

Bro you’re so good, thanks man!

Ah! Finally a good and to the point explanation after searching so much on RUclips. Thankyou! You have a new subscriber now.

Beautiful presentation. So clear and informative!

There are some mistakes in your notes

I love this, thank you! Very clear explanation.

Thanks for introducing GAM, need you watch several times to understand

Gauss didn't invent the linear model; he just claimed to a decade after someone else had. The same is true for Gaussian elimination. Newton invented it, and then Gauss decided to name it after himself.

Loving the spline / GAM series, thank you!

First, the conflict did not begin on October 7th. You have been oppressing Palestinians since your illegal occupation, starting with the massacre of Karbala. You take pride in the killing of thousands of children since then. You even bombed a civilian camp where you told people to go to suffer less, only to slaughter them in the middle of the night. How dare you, coward.

Very clear and concise! Looking forward for the follow up video

So much information compiled together wonderfully in this video.Hope you come up with more such videos on individual methods with some examples.

IMHO, symmetrical or not, the mean is still your expected value. If you want to minimise error, you can only work with the expectation, since that's what you can expect. If you optimised for something else, you may use the mode or whatever else that fits the optimization.

What is the difference between denoting p_theta (x|z) vs p(x|z,theta) ?

Just wanted to say Thank You !!! Very well explained & so much intuitive 👍

at 3:30 , i dont understand why ez1 + ez2 + ez 3 = 1 can someone please explain? thanks

Great vid. Can you please share that notebook ?

for question 1 : it won't be a problem and the network will learn normally because the inputs to the neurons will not be zero or constant since we initialized the weights to be random so the biases will get updated normally. for question 2 : since the inputs to the network are different the deriviative of the output with respect to the wight will not be constant or zero so this weights in the first layer(input) will be updated normally.

Thnak you so much , very good explanation

This video is everything I asked for! Thank you so much, Meerkat!

thank u so much brother

thanka man

do you have any more tips and tricks for regular linear models?

very useful video for practitioners

many thanks for the lecture. we all the time need to know how the factors are calculated from the variables. but how come the factors and their loading can be calculated given that the only given data is the variables? x1=l1f1 + l2f2 + l3f3

Islam always brings hate and war!!

Wow, will you cover nested logit model? And relationship with gev or vglm

Thank u so much!