IEEE Fraud @Kaggle Challenge 1st Place Solution with @rapidsai library:https://t.co/x125dun7kl#ml #ai #ds #machinelearning
— Bojan Tunguz (@tunguz) February 4, 2020
IEEE Fraud @Kaggle Challenge 1st Place Solution with @rapidsai library:https://t.co/x125dun7kl#ml #ai #ds #machinelearning
— Bojan Tunguz (@tunguz) February 4, 2020
See how Kaggler Chris Deotte uses @rapidsai #cuML to accelerate knn 600x in @kaggle #GPU cloud compute environment and augments data for higher accuracy on MNIST - https://t.co/H9aPHCMCsP
— RAPIDS AI (@rapidsai) February 5, 2020
I just compared t-SNE algorithm on #MNIST dataset in @kaggle kernels between #sklearn and @rapidsai. We are getting a 2000X speedup!https://t.co/pbFgCbQ7jZ
— Bojan Tunguz (@tunguz) February 6, 2020
And today I've tried UMAP with @nvidia @rapidsai in @kaggle kernels. A speedup of 120 x is nothing to sneeze at, even though it's not as dramatic as the 2000 x speedup for t-SNE.https://t.co/iIA8UZekH5 pic.twitter.com/QVRdqJQWag
— Bojan Tunguz (@tunguz) February 6, 2020