We're excited to launch the Human Protein Atlas Image Classification challenge w/@HumanProteome! Help the #CellAtlas team and @Prof_Lundberg to classify subcellular protein patterns and win great prizes, provided by @LeicaMicro and @nvidia. Details: https://t.co/HBP1wYiAsy pic.twitter.com/aoknb4dxhy
— Kaggle (@kaggle) October 4, 2018
Have you ever considered using Kaggle Kernels to teach coding, but held off because you don't want to confuse your students with a new platform? I got you covered:
— Rachael Tatman (@rctatman) October 3, 2018
* lesson plans (various lengths)
* video demos
* student FAQs
* 👩🏫👩💻👨💻👩💻👨💻😁https://t.co/ERL2f2kxtM
Ready to make sense of the Universe? If so, we've got a new challenge for you! The @LSST Project seeks your help with this "Astronomical Classification Challenge" ✨ Scope it out and get started now: https://t.co/8y0lTK1m4h pic.twitter.com/EAzvN8vKUK
— Kaggle (@kaggle) October 1, 2018
Awesome #dataset alert — 1.4 Million self-driving car images by nuScences. 🚘Check it out: https://t.co/nXRetTdoRp pic.twitter.com/EK9WyUxiiB
— Kaggle (@kaggle) October 1, 2018
Build a classifier for the world’s largest dataset of doodles! 🖍😺 https://t.co/uOBxnfJYPD
— hardmaru (@hardmaru) September 28, 2018
One common misconception on Kaggle: we don't have "real" data or support "real" work. Not true!
— Ben Hamner (@benhamner) September 27, 2018
1. Public comps & datasets include medical, financial, etc.
2. Range from unstructured/real-world/dirty to cleaned/anonymized
3. You can run private code on your own private data
Thrilled to launch @kaggle’s second kernels competition with @twosigma: predict stock price performance based on news articles. Models need to be submitted through kernels, so limited compute will be a regularizer on model complexity https://t.co/gztRAzJCyD
— Ben Hamner (@benhamner) September 25, 2018
Working on a deep learning predictive modeling project? Check out @JakobAungiers updated article: "Time series prediction using LSTM deep neural networks" https://t.co/Olzb3JQjZ4 #deeplearning pic.twitter.com/hMO0AZiNju
— Kaggle (@kaggle) September 25, 2018
How toxic are @HillaryClinton and @raelDonaldTromp tweets? A #MachineLearning approach with the help of @kaggle: https://t.co/X6DoiO6cwp
— Bojan Tunguz (@tunguz) September 24, 2018
“Help! I can’t reproduce a machine learning project!” - nice blog post from @rctatman https://t.co/eTCEmW2Qao
— Ben Hamner (@benhamner) September 19, 2018
This week's #KernelAwards winner uses the Fashion MNIST dataset to demonstrate the use of autoencoders for generating new and denoised images: https://t.co/F49sLYV0YY pic.twitter.com/hZm1sQjWWA
— Kaggle (@kaggle) September 14, 2018
Competition launch! Can you predict how much GStore customers will spend? $45,000 in prize money is up for grabs. Check out the Google Analytics Customer Revenue Challenge today: https://t.co/Xs8ce2nhqZ pic.twitter.com/WikLyKlJWR
— Kaggle (@kaggle) September 14, 2018
