Cool assortment of genetics algorithms in Python: https://t.co/ce0hjysphj pic.twitter.com/dUmrOgZHxD
— Kaggle (@kaggle) September 12, 2018
Cool assortment of genetics algorithms in Python: https://t.co/ce0hjysphj pic.twitter.com/dUmrOgZHxD
— Kaggle (@kaggle) September 12, 2018
Machine learning models inherit biases present in the source data by default. If this isn't carefully considered, it means they can propagate discrimination instead of improving fairness. We just launched a competition around one facet of this challenge https://t.co/cpISwHFc53
— Ben Hamner (@benhamner) September 7, 2018
Hungry for a new dataset? 😋Check out the classification of recipes based off more than 400,000 food images from social media. #deeplearning #machinelearning #deepchef https://t.co/V5XQv4PeEZ pic.twitter.com/dvkBlZoX2C
— Kaggle (@kaggle) September 7, 2018
Frustrated with the difficulty of versioning Jupyter notebooks and the confusion from out-of-order execution? We address this in @Kaggle Kernels with a Commit, which saves a code-only version for diffs, and creates a linear top-to-bottom run of the notebook in a fresh environment pic.twitter.com/WlaFpR4BLm
— Ben Hamner (@benhamner) September 7, 2018
We launched an 'Inclusive Images Challenge' to promote inclusive development of image recognition models across geographically diverse datasets.
— Jeff Dean (@JeffDean) September 6, 2018
Results will be presented at #NIPS2018.
More info in the Google AI blog: https://t.co/oWezKUa0NR https://t.co/DAsgd0YntI
Should you Kaggle? Data scientist and Kaggler, @A_K_Nain provides a great overview and a compelling argument for getting started! And looks pretty great in his #KaggleSwag, by the way 😉 https://t.co/LebSxymOgS pic.twitter.com/RnbTpBQEK4
— Kaggle (@kaggle) September 6, 2018
1st place solution for @Kaggle Home Credit Default Risk Competition: https://t.co/8nstdRWGcX
— Bojan Tunguz (@tunguz) September 2, 2018
This week's #KernelAwards winner uses Local Interpretable Model-Agnostic Explanations (LIME) to better understand the predictions of an ML model: https://t.co/FSvY4WtYqp pic.twitter.com/AdQIMzKX9h
— Kaggle (@kaggle) August 31, 2018
Ethereum blockchain data now available on Google Cloud! A live updating public dataset for smart contract analytics by @allenday @fkn1088 and @MeganRisdal cc @ethereum #datascience #blockchain https://t.co/pOk9nKdJuU pic.twitter.com/E1MObI6x6D
— Kaggle (@kaggle) August 30, 2018
Using Kaggle to start and guide your ML/data science journey https://t.co/4emQs0Nq2T
— Ben Hamner (@benhamner) August 27, 2018
The ultimate peer-to-peer guide on getting started with our #machinelearning competitions, written by Kaggler, @koehrsen_will. https://t.co/6R4INgkQLd
— Kaggle (@kaggle) August 24, 2018
Extensive example using Prophet to forecast time series (in this case, item-level demand forecasting, which has complex seasonal and holiday effects) https://t.co/E4hV9UL6UM
— Ben Hamner (@benhamner) August 14, 2018