Loss Functions - EXPLAINED!

Many animations used in this video came from Jonathan Barron [1, 2]. Give this researcher a like for his hard work!

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[sephchan]

'check it out when you want to lower your self esteem' damn that sentence caught me off guard

[BB-rhbp]

Nice voice, tones, speed, and graphics! Perfect for studying anytime. Thanks!

[geeky_explorer]

you nailed it, thanks a lot for this video, great informative lecture, keep making this contents and keep inspiring us

[oscarsal]

Wow impresive more value than dozens of study hours!!! Love it!

[MarcelloNesca]

This was an incredibly accessible video! You explained complex subjects simply enough to make me curious about going deeper in loss functions. Very awesome, thank you

[kayingtang]

Finally find a video that explains loss function clearly. Thanks for your effort!!

[vivek]

Thanks for this video, nicely explained.

[arkasaha]

Nice video! How about doing a mathematical video related to eigenvalues/vectors in ML applications?

[david-vrty]

Great video. Is the code for the graphics somewhere available? I would like to use and adapt it for my purposes

[gregorygreif]

I love your animations! How did you make them?

[hardikvegad]

One question how do we apply/implement adaptive loss? do we have to create a list of random values of alpha; like we do in randomizedsearchcv or grid search. Or is there any other way?.... Btw your explanation was amazing.

[bhargav]

Welsch loss is what I needed for my problem, thanks

[KaranKinariwala]

Hey, firstly great explanation! Thanks! Secondly, I have a minor clarification. Can we then, in conclusion, say that finding the best loss function for a particular task also depends on the second derivative of the loss function? Since the final loss depends on the convex nature of the loss function.

[ethiopiansickness]

Great video! But I really need help understanding something. I thought the purpose of calculating MSE, SSE etc. Was to get a sense of accurate your regression model is. 0:55 I noticed how when you introduced the outliers you said that the model tries to change to also incorporate the outliers. So does that also change the linear regression model too? I'm also confused because I thought linear regression (OLS) already computes the minimum of the sum of squared errors.

[templetonpeck]

This video was the was freaking awesome!

[hariharans.j]

Great as always man!
Make videos about reward modelling and neural ODEs

[codingwithnipu]

what tool you used for the visulization graph

[emmanuelgoldstein]

Hello, not sure if you will reply to this older video but at 7:01 you choose the function with the greatest amount of loss and say that it fits the data the best. Was this an error? If not, can you explain why?

[hillosand]

Absolute loss does not 'ignore outliers altogether'. Outliers still have more effect on the absolute loss function than non-outliers do, it's just not as extreme an effect as MSE. The right-hand graphic at about 2:35 is wrong, the fitted line would be shifted much more upwards for low values of x.

[GauravSharma-uiyd]

Hey ajay this is gaurav, I always loved to watch your in depth videos. Please make indepth videos on various optimizers like adam, adagrad, ftrl and all other