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One of the best explanations I have been able to find. Thank you for your time and effort!
This seems to be an incredible tool for ML practitioners. I can't wait to start using it!!!
Amazing video , very clearly explained optuna !
Glad it was helpful!
Yeah! We are no longer need to do fine-tuning.I used an Adma with 1e-3 with the same setting but I was beaten by an SGD 1e-4 with the same setting.That notebook was the Pytorch official fast R-CNN fine-tuning notebook.
This tool seems to be incredible, i will be sure to include it in my next project, thanks :)
want to know how to implement the progress bar at 12:45, it looks pretty cool
use library called tqdm and then set progress bar mode to ascii
thank you for the presentation!
This is great, a lot easier to use than hyperopt.
Where to find gaussian sampler?
thank you, very nice presentation
Where can i find quality code examples?
The provided link to the source code is not available.
Great tool ! Thank you for sharing
Great explanation
Awesome video and for thx to show this tool!
Excellent video!
Thank you for the video!
a great useful framework thank u
Windows pip build seems to be broken, I had to install with conda
that is very common unfortunately, do you have C++ compilers installed on Win?
could you share the slides?
Appreciated
14:37 Never though learning rate is so important
would be awesome to have this integrated with MLFlow!
Bravo !!!
11:10 twice as fast WITH tuning.
Amazing!
Brilliant.
One of the best explanations I have been able to find. Thank you for your time and effort!
This seems to be an incredible tool for ML practitioners. I can't wait to start using it!!!
Amazing video , very clearly explained optuna !
Glad it was helpful!
Yeah! We are no longer need to do fine-tuning.
I used an Adma with 1e-3 with the same setting but I was beaten by an SGD 1e-4 with the same setting.
That notebook was the Pytorch official fast R-CNN fine-tuning notebook.
This tool seems to be incredible, i will be sure to include it in my next project, thanks :)
want to know how to implement the progress bar at 12:45, it looks pretty cool
use library called tqdm and then set progress bar mode to ascii
thank you for the presentation!
This is great, a lot easier to use than hyperopt.
Where to find gaussian sampler?
thank you, very nice presentation
Where can i find quality code examples?
The provided link to the source code is not available.
Great tool ! Thank you for sharing
Great explanation
Awesome video and for thx to show this tool!
Excellent video!
Thank you for the video!
a great useful framework thank u
Windows pip build seems to be broken, I had to install with conda
that is very common unfortunately, do you have C++ compilers installed on Win?
could you share the slides?
Appreciated
14:37 Never though learning rate is so important
would be awesome to have this integrated with MLFlow!
Bravo !!!
11:10 twice as fast WITH tuning.
Amazing!
Brilliant.