Відео

image for undersamping and over sampling makes no sense. Can you explain what is the orange part and what is the blue part???so confusing...

Where or how do you get the initial 50 data points?

For those who want to understand more about how we get Lagrangian and Dual Formulation - Appendix C called SVM Dual Problem from "Hands On Machine Learning" book might be helpful. Explains Lagrangian and some maths

So much value. Thank you

"Would you be able to provide the references for this material, please? Thank you very much!"

Tutorial en castellano de optimizacion bayesiana, por si a alguien le interesa: ua-cam.com/video/nNRGOfneMdA/v-deo.html

Excellent way to teach❤

man, your videos are amazing. i literally just remembered how great this video is but couldn't find your channel for almost an hour. glad i did. honestly one of my favourites about KFold

Best explanation on GMM algorithm, thanks !

Awesome and well explained

Best explained... millions of thanks

can i get the code ?

Very well explained. I got the complete intuition of GMMs in a go.

😀

Very well simplified explanation. Thank you

best ever explanation for GMM. Thanks a lot for such a helpful video.

Hello, I’ve been watching your videos. Thank you! They are great. Is there a way to contact you directly?

Which reference book do you use?

Hello there, Can you give the material notes

Nice sir

well done in <15 min!!! highly recommended

is the end of the video missing?

This seems to be correct if and only if you assume that your three models are independant. This is fine, but I think this does not say much in practical cases, as it is very unlikely that you will have 3 base learners that are not correlated. In general, it seems pretty complicated to come up with a "comprehensive" formula that takes into account the respective covariances of these three models with each others and expresses the probabilistic advantage ensembling has.

More on Statistics.

very clear explanation for decision tree features importance, thanks

You should mention the normalized method. I kill myself to find out how to normalize those numbers

can you provide these slides

@machinelearningmaster Great video, thanks! Could you please share the dataset used so that we can replicate this?

Thanks a lot for the video

by far the clearest explanation of bayesian optimization, great work, thanks man!

Wonderful explaination! Thanks professor.

is there a mistake in 9:10 ? there is 1 f(x) too much i think. Has to be N(f(x_1), ... (x_n) l o, C*)) / N(f(x_1), ... (x_n) l o, C)). Can anyone confirm this? ty

what a video!!! simple and straight forward

Very intuitive explanation!! Thank you so much! I found this gem of a channel today!

thanks but there is a typo in y=-1 wtx+b<= -1 not wtx+b<= 1

This entire series is one of the most comprehensive explanations I have found for SVMs. Extremely grateful for it

Wonderful video, many thanks. Perhaps it would be nice if you made the code available for us learners to play with. Thanks.

Very clear and informative. Thanks!

is there any repo containing the codes for the entire playlist

Why don't we inherit from the keras.model class? Thanks alot for your tutorials

extremely not good explanations

Thanks

Best explanation find the most, thank-you

You taught better than my instructor 🙂

Thanks so much, the best explanation of xgBoost I´ve seen so far, most people doesnt matter about the math intuition!

Good stuff but consider sharing the Notebooks in the future

Can you please tell me if I have 10 classes of training data, what number of epochs should I use?

I am a silent internet user, what I usually do when I like a content is just by pressing the like button and save the link on the txt file, but this time is an exception, your content is very simple and completely what I'm looking for. I write you a comment, as the first commentator in this video 😀 You have a new subscriber now. Keep up the good work

Wow!!! Excellent lecture!!

Why do you add the mean of the predicted points back to the predicted points?