nharbour + kaggle   70

deeplearning/DAE.py at master · KeremTurgutlu/deeplearning
GitHub is where people build software. More than 27 million people use GitHub to discover, fork, and contribute to over 80 million projects.
denoising  dae  denoising-autoencoder  regularisation  deep-learning  fastai  kaggle 
march 2018 by nharbour
dark-channel-prior-dehazing/dehaze.py at master · joyeecheung/dark-channel-prior-dehazing
dark-channel-prior-dehazing - Python implementation of "Single Image Haze Removal Using Dark Channel Prior"
haze  haze-removal  planet  kaggle  deep-learning 
february 2018 by nharbour
image_dehaze/dehaze.py at master · He-Zhang/image_dehaze
image_dehaze - Single Image Haze Removal Using Dark Channel Prior
haze  haze-removal  planet  kaggle  deep-learning 
february 2018 by nharbour
Kaggle-Ensemble-Guide/src at master · MLWave/Kaggle-Ensemble-Guide
Kaggle-Ensemble-Guide - Code for the Kaggle Ensembling Guide Article on MLWave
ensemble  code  github  kaggle  deep-learning 
december 2017 by nharbour
Statefarm kaggle comp - Part 1 - Deep Learning Course Forums
Ask questions or add your thoughts about the statefarm competition here!
kaggle  fast.ai  state-farm  winner  winners  deep-learning  state  farm 
november 2017 by nharbour
Why Kagglers Love XGBoost - Long Short-Term Memories
One of the more delightfully named theorems in data science is called “The No Free Lunch Theorem.” It states “any two algorithms are equivalent when their performance is averaged across all possible problems.”(4) If that’s true, why did over half of the winning solutions for the data science competition website Kaggle in 2015 contain XGBoost?(1) How does XGBoost even work? What does it look like in action?
xgboost  deep-learning  kaggle 
september 2017 by nharbour

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