RoBERTa: A Robustly Optimized BERT Pretraining Approach

[@ursinbrunner2461]

I'm doing my masters in ML and your videos have the perfect level of detail for me. Watched a few of them now and they always get the core of a paper in detail without taking more time than necessary. Thanks a lot for your efforts!

[@sasna8800]

same as me one day of watching his videos made me understand things that I don't understand for weeks

[@michaelcarlon1831]

These videos are a real service; thank you!

[@hihiendru]

Thank you! I am also working on a masters in ML and this is incredibly helpful.

[@陳翰儒-d5m]

Man, you just help me very much.
As a sophomore computer science student, I learn some tips when reading paper, thank you.

[@laurynasgrusas8755]

I didn't understand the paper clearly after reading it at first, but your video explains things in a simple and understandable manner. Thank you!

[@danielmichelin4832]

I'm preparing for a PhD in machine learning and these videos are indispensable. Thank you!

[@gergerger53]

At 14:47 how can you highlight the number "256" and say out loud "five hundred and twelve" ? That's some weird stuff going on there! It's almost like for computer science folk, the powers of two have become their own separate mental entries, beyond the standard numerical quantities, where one can prime another and lead to slips like this. But perfect summary video (like your usual standard). We can always rely on you to give us the main points.

[@thepresistence5935]

For those NLP partitioner, it's very helpful.

[@soumyasarkar4100]

In Full sentences and Doc sentences variant , they only use MLM loss is it ?

[@PotatoKaboom]

Your videos are great! Do you think you can do one about ALBERT?

[@YannicKilcher]

If I ever get around to read the paper :) but it's quite far down the stack right now

[@darullshifa4870]

Thank you for clear details

[@xuian1107]

Great thanks!! Very clear.

[@niduttbhuptani9301]

Thanks a lot man!

[@masoudparpanchi505]

nice video

[@AlphaMoury]

You are the best