The State of Data Science & Machine Learning 2017 by Kaggle.

Very informative article about age, job titles, most popular languages and everything related to DS / ML.

Not to mention that source data is included.

https://www.kaggle.com/surveys/2017

#kaggle #statistics

OpenAI launched Retro Contest — a contest where agents use their past experience to adapt to new environments. In this contest agents have to play previously unseen levels.
https://blog.openai.com/retro-contest/

#kaggle #rl #openai

Post on the estimation of the likelihood of the ads closing due to actually selling the good, which was the goal of Avito’s #Kaggle contest. Author built a #neuralnetwork solution and had 18th place.

https://towardsdatascience.com/kaggle-avito-demand-challenge-18th-place-solution-neural-network-ac19efd6e183

Series of articles «Machine Learning Kaggle Competition» in three parts:

1. Part I: https://towardsdatascience.com/machine-learning-kaggle-competition-part-one-getting-started-32fb9ff47426
2. Part II: https://towardsdatascience.com/machine-learning-kaggle-competition-part-two-improving-e5b4d61ab4b8
3. Part III: https://towardsdatascience.com/machine-learning-kaggle-competition-part-three-optimization-db04ea415507

#kaggle #guide

Kaggle kernel on classifing Russian Troll tweets.

https://www.kaggle.com/kmader/from-hate-speech-to-russian-bot-tweets

#kaggle #kernel #nlp

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.

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

#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

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

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

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

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

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

​​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

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

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