Explore >> Select a destination
|
You are here 
|
bartwronski.com | ||
| | | | |
mirawelner.com
|
|
| | | | | A straightforward explanation of the spectroscopy work I did with sparse sensing at Purdue. | |
| | | | |
thenumb.at
|
|
| | | | | ||
| | | | |
grigory.github.io
|
|
| | | | | Discussion of the class on Foundations of Data Science that I am teaching at IU this Fall. | |
| | | | |
www.paepper.com
|
|
| | | When you have a big data set and a complicated machine learning problem, chances are that training your model takes a couple of days even on a modern GPU. However, it is well-known that the cycle of having a new idea, implementing it and then verifying it should be as quick as possible. This is to ensure that you can efficiently test out new ideas. If you need to wait for a whole week for your training run, this becomes very inefficient. | ||
