Online ad demand prediction #kaggle competition 1st place summary:
https://www.kaggle.com/c/avito-demand-prediction/discussion/59880
Winner explains how to combine categorical, numerical, image and text features into a single #NN that gets you into top 10 without stacking.
https://www.kaggle.com/c/avito-demand-prediction/discussion/59880
Winner explains how to combine categorical, numerical, image and text features into a single #NN that gets you into top 10 without stacking.
1st place solution in the recent Home Credit Default Risk #Kaggle competition
- extensive feature engineering, with ~700 of features total used
- XGBoost, LightGBM, CatBoost, FastRGF, DAE+NN, Lin Reg
- 3-level ensembling (stacking x2 + blending)
Link: https://www.kaggle.com/c/home-credit-default-risk/discussion/64821
- extensive feature engineering, with ~700 of features total used
- XGBoost, LightGBM, CatBoost, FastRGF, DAE+NN, Lin Reg
- 3-level ensembling (stacking x2 + blending)
Link: https://www.kaggle.com/c/home-credit-default-risk/discussion/64821
#OpenDataScience community (ods.ai) recently released Open Machine Learning Course. This is a community-driven course, covering #production, #Kaggle (actually #CompetitiveDataScience, but we use this hashtag for the first time), #DL, #RL, #ML and validated on the russian-speaking DS community, which was translated into english.
There are lots of assignments and some competitions during the course. Interactive rating highly motivates and makes it fun to participate.
Next session starts on October 1. Welcome!
Link: https://mlcourse.ai?utm_source=telegram&utm_medium=opendatascience
There are lots of assignments and some competitions during the course. Interactive rating highly motivates and makes it fun to participate.
Next session starts on October 1. Welcome!
Link: https://mlcourse.ai?utm_source=telegram&utm_medium=opendatascience
Data Science by ODS.ai 🦜
New library for #DataAugmentation: SOLT. Supports various transformations for images, masks, targets and landmarks. Fast and easy-to-use library useful in #ComputerVision and #DeepLearning Link: https://github.com/MIPT-Oulu/solt .
Top #Kaggle masters are releasing image augmentation library v0.1.1 with an extended bounding box support.
Guthub: https://github.com/albu/albumentations/releases/tag/v0.1.1
#cv #dl #dataaugmentation
Guthub: https://github.com/albu/albumentations/releases/tag/v0.1.1
#cv #dl #dataaugmentation
GitHub
Release Extended bounding boxes support. New transformations. New notebooks with examples. A lot of bugfixes. · albumentations…
Bounding boxes support
Transformations that support bounding boxes
The main change in this release is the addition of the operations on bounding boxes to the
Flip
VerticalFlip
HorizontalFlip
Trans...
Transformations that support bounding boxes
The main change in this release is the addition of the operations on bounding boxes to the
Flip
VerticalFlip
HorizontalFlip
Trans...
Great example on how different approach to feature encoding can influence the results.
Mean (likelihood) encoding for categorical variables with high cardinality and feature interactions: a comprehensive study with Python
Link: https://www.kaggle.com/vprokopev/mean-likelihood-encodings-a-comprehensive-study
#FeatureEngineering #FeactureEncoding #Kaggle
Mean (likelihood) encoding for categorical variables with high cardinality and feature interactions: a comprehensive study with Python
Link: https://www.kaggle.com/vprokopev/mean-likelihood-encodings-a-comprehensive-study
#FeatureEngineering #FeactureEncoding #Kaggle
Sptoify announced its new Data Science Challenge
Spotify Sequential Skip Prediction Challenge is a part of #WSDM Cup 2019. The dataset comprises 130M Spotify listening sessions, and the task is to predict if a track is skipped. The challenge is live today, and runs until Jan 4.
Link: https://www.crowdai.org/challenges/spotify-sequential-skip-prediction-challenge
#kaggle #CompetitiveDataScience #Spotify
Spotify Sequential Skip Prediction Challenge is a part of #WSDM Cup 2019. The dataset comprises 130M Spotify listening sessions, and the task is to predict if a track is skipped. The challenge is live today, and runs until Jan 4.
Link: https://www.crowdai.org/challenges/spotify-sequential-skip-prediction-challenge
#kaggle #CompetitiveDataScience #Spotify
Forensic Deep Learning: Kaggle Camera Model Identification Challenge
Report on Kaggle solution on camera model identification.
Link: https://towardsdatascience.com/forensic-deep-learning-kaggle-camera-model-identification-challenge-f6a3892561bd
#CV #Kaggle
Report on Kaggle solution on camera model identification.
Link: https://towardsdatascience.com/forensic-deep-learning-kaggle-camera-model-identification-challenge-f6a3892561bd
#CV #Kaggle
Medium
Forensic Deep Learning: Kaggle Camera Model Identification Challenge
There was a computer vision challenge that was hosted at kaggle.com about a year ago named IEEE’s Signal Processing Society — Camera Model…
Free online ODS.AI course on ML
Another great free course will start on February 11. Taught through #Kaggle notebooks and competitions.
Link: https://www.kaggle.com/general/77771
#entrylevel #novice #beginner
Another great free course will start on February 11. Taught through #Kaggle notebooks and competitions.
Link: https://www.kaggle.com/general/77771
#entrylevel #novice #beginner
Google’s progress on AutoML
Hint: it’s beating some old competition solutions.
Link: https://cloud.google.com/blog/products/ai-machine-learning/expanding-google-cloud-ai-to-make-it-easier-for-developers-to-build-and-deploy-ai
#AutoML #Kaggle
Hint: it’s beating some old competition solutions.
Link: https://cloud.google.com/blog/products/ai-machine-learning/expanding-google-cloud-ai-to-make-it-easier-for-developers-to-build-and-deploy-ai
#AutoML #Kaggle
Critics: AI competitions don’t produce useful models
Post, suggesting a viewpoint that AI competitions never seem to lead to products, how the one can overfit on a hold out test set, and why #Imagenet results since the mid-2010s are suspect.
Link: https://lukeoakdenrayner.wordpress.com/2019/09/19/ai-competitions-dont-produce-useful-models/
#critics #meta #AI #kaggle #imagenet #lenet
Post, suggesting a viewpoint that AI competitions never seem to lead to products, how the one can overfit on a hold out test set, and why #Imagenet results since the mid-2010s are suspect.
Link: https://lukeoakdenrayner.wordpress.com/2019/09/19/ai-competitions-dont-produce-useful-models/
#critics #meta #AI #kaggle #imagenet #lenet
Luke Oakden-Rayner
AI competitions don’t produce useful models
Ai competitions are fun, community building, talent scouting, brand promoting, and attention grabbing. But competitions are not intended to develop useful models.
Abstraction and Reasoning Challenge winners
There is a very interesting challenge by #Francois Chollet about can a computer learn complex abstract tasks through maybe reasoning from a few examples?
And here is the first place with descriptions!
https://www.kaggle.com/c/abstraction-and-reasoning-challenge/discussion/154597
But author doubts about his solution brings us to AGI, but it's interesting to look through :)
"This DSL is solved by enumeration (exploiting duplicates) + a greedy stacking combiner. Everything is implemented efficiently in C++ (with no dependencies) and running in parallel."
There are 10k lines of code and a bunch of tricks that you can read about on the link.
Though second and third place also interesting – you can find it in discussion section here https://www.kaggle.com/c/abstraction-and-reasoning-challenge/discussion
The 3d place even almost don't use ML :)
So, nothing close to general reasoning here : )
#kaggle #chollet #AGI #stacking
There is a very interesting challenge by #Francois Chollet about can a computer learn complex abstract tasks through maybe reasoning from a few examples?
And here is the first place with descriptions!
https://www.kaggle.com/c/abstraction-and-reasoning-challenge/discussion/154597
But author doubts about his solution brings us to AGI, but it's interesting to look through :)
"This DSL is solved by enumeration (exploiting duplicates) + a greedy stacking combiner. Everything is implemented efficiently in C++ (with no dependencies) and running in parallel."
There are 10k lines of code and a bunch of tricks that you can read about on the link.
Though second and third place also interesting – you can find it in discussion section here https://www.kaggle.com/c/abstraction-and-reasoning-challenge/discussion
The 3d place even almost don't use ML :)
So, nothing close to general reasoning here : )
#kaggle #chollet #AGI #stacking