Thank you so much for the insights & tips on mastering ML Paper! I love your workstation setup, can you please share the details about your setup dual screen, camera, table, laptop, etc.
would you like to explore physics-informed machine learning in your future videos? For example, physics informed neural networks PINN, data assimilation or system identification. I am saying this because you won't find many videos on PINNs on UA-cam.
@@NicholasRenotte I really appreciate the way you reply every single chat. This revolutionary paper started the trend of physics informed neural network in 2017.. github.com/maziarraissi/PINNs Ofcourse there are many more papers, but this is where it all started.
@@NicholasRenotte Also, if you try to find some videos on youtube, you would find literally nothing useful at all. When I learnt PINN I had to go to stackoverflow everytime I'd problem.
Thanks Nicholas for your recommendation. You are doing impressive and outstanding job! I do believe that your great community will be also impressed and apply your recommendations accordingly. We are very lucky the "paperwithcode" has been launched. All the papers are impressive but I do believe with quantum computers which probably will change a bit the "concept" of ML, AI and DL. We know you work in IBM and I think it will be awesome if your future videos will depict some ML project based on Qiskit. IBM makes remarkable work in quantum computer technology and probably it will be great also to approach this concept to others. IBM website is a rich source of information (outstanding) . Thank you for your effort. Have a nice day!
@@NicholasRenotte Awesome. I do believe you once come in and will never go out. QC technology is not easy and as you know some part are still unknown but it is very exciting. For sure your contribution is essential.
I feel like I understand what I read. But it's always written in verbose and obtuse ways. Almost like they are trying to hide the pointlessness of their work or something. Mathematical proofs are hard when working ..
The ones referencing what’s app? If those ones then think someone was impersonating me, reported them 🙃 others I haven’t deleted, might be getting stopped by YT’s comment filter.
100% increase my level on reading ML papers 🧙🏼♂️
You're a wizard Jonnny! Thanks so much guys!
thank you, Nicholas! U just saved me from the pain of reading deep learning papers. Pls keep going on your AMAZING jobs.
Thanks, Professor Renotte. Your effective but simple thinking astounds me.
Great tips, Nicholas! Love how you broke down the steps and clearly explained how to do it.
LILLIAN!!! Thanks sooo much!! You've got some awesome vids on your channel!!
@@NicholasRenotte Thank you! Glad you appreciate it!
please do a video on ML papers recommended for beginners
this was a much needed video , thanks Nick!!
This came just in time. Thanks!
Yo! This video was a NEED!
I recently tried reading an ML paper but I found myself lost half way in 🤦♂️😂
Thanks! 🤘🔥
Mannn, I started getting back into them hardcore over the last month and it was brutal!! Thanks for checking it out Vik!!!
Thanks for the great videos dude!
This was so much needed.
OMG, it´s awsome ! will help me a lot with my researchs ! Thank you so much !!!!
Thank you so much for the insights & tips on mastering ML Paper! I love your workstation setup, can you please share the details about your setup dual screen, camera, table, laptop, etc.
Again best ever …. It’s really amazing seems to be same as before coding more focus on theory ….cheers ….
Agreed, implementation and testing makes it a ton easier!
Your content really helps thanks.
Heya @Brendan! Thanks so much man!
speaking of facenet paper, please make a video about triplet loss/siamese net/one shot learning!
Happening as we speak, it's my stretch project for the week!
that’s awesome. It’s part of my project so I would love to see your insights too!
What brand is your incense stick?
Hahahah, I have no idea 😂
Wonderful video❤️. Will definitely try and follow 👍🏻
Awesome work @Sharan!
@Nicholas , scikit-learn more useful than tensorflow or
Scikit-learn is better for ML and tensorflow for DL?
They both have their use cases, for simple ML problems I use sklearn most of the time for deep learning I use TF!
This one is great mate!
Thanks 🙏 a bunch @Namindu!!
hey man... I have become big fan of yours. great video!!
Can you also make a video on how to fine tune Bert to make model like keybert for keyword extraction?
You got it!
Thanks a lot)
Respect ✊ from Russia)
Please make a video about facial recognition in Python🙏
That would be openCV.
This was A1. Thanks
Thanks a bunch @Trevor!
would you like to explore physics-informed machine learning in your future videos? For example, physics informed neural networks PINN, data assimilation or system identification. I am saying this because you won't find many videos on PINNs on UA-cam.
Got a link to some research? Would love to take a look!
@@NicholasRenotte I really appreciate the way you reply every single chat. This revolutionary paper started the trend of physics informed neural network in 2017.. github.com/maziarraissi/PINNs Ofcourse there are many more papers, but this is where it all started.
@@NicholasRenotte Also, if you try to find some videos on youtube, you would find literally nothing useful at all. When I learnt PINN I had to go to stackoverflow everytime I'd problem.
Sir pls create a video on mask detector using react js
You got it man!
Great content mate
Thanks so much @Umar!!
Thanks a lot man!
very helpful.
thank you
Thanks Nicholas for your recommendation. You are doing impressive and outstanding job! I do believe that your great community will be also impressed and apply your recommendations accordingly. We are very lucky the "paperwithcode" has been launched. All the papers are impressive but I do believe with quantum computers which probably will change a bit the "concept" of ML, AI and DL. We know you work in IBM and I think it will be awesome if your future videos will depict some ML project based on Qiskit. IBM makes remarkable work in quantum computer technology and probably it will be great also to approach this concept to others. IBM website is a rich source of information (outstanding) . Thank you for your effort. Have a nice day!
Oh Markus, you are always on the ball. This a screenshot of my content list: imgur.com/29Jtd9o I've been talking to the Qiskit team :)
@@NicholasRenotte Awesome. I do believe you once come in and will never go out. QC technology is not easy and as you know some part are still unknown but it is very exciting. For sure your contribution is essential.
I feel like I understand what I read. But it's always written in verbose and obtuse ways. Almost like they are trying to hide the pointlessness of their work or something.
Mathematical proofs are hard when working ..
Best title ever hahaha
Why you delete the commentaires about trading forex and crypto??
The ones referencing what’s app? If those ones then think someone was impersonating me, reported them 🙃 others I haven’t deleted, might be getting stopped by YT’s comment filter.