kaggle-for-fun - All my submissions for Kaggle contests that I have been, and going to be participating

  •        28

All my submissions for Kaggle contests that I have been, and going to be participating. I will probably have everything written in Python (utilizing scikit-learn or similar libraries), but occasionally I might also use R or Haskell if I can.




Related Projects

mlcourse_open - OpenDataScience Machine Learning course. Both in English and Russian

  •    Python

This is the list of published articles on medium.com 🇬🇧, habr.com 🇷🇺, and jqr.com 🇨🇳. Icons are clickable. Also, links to Kaggle Kernels (in English) are given. This way one can reproduce everything without installing a single package. Assignments will be announced each week. Meanwhile, you can pratice with demo versions. Solutions will be discussed in the upcoming run of the course.

painters - :art: Winning solution for the Painter by Numbers competition on Kaggle

  •    Python

This repository contains a 1st place solution for the Painter by Numbers competition on Kaggle. Below is a brief description of the dataset and approaches I've used to build and validate a predictive model. The challenge of the competition was to examine pairs of paintings and determine whether they were painted by the same artist. The training set consists of artwork images and their corresponding class labels (painters). Examples in the test set were split into 13 groups and all possible pairs within each group needed to be examined for the submission. The evaluation metric for the leaderboard was AUC (area under the curve).

kaggle - A collection of Kaggle solutions. Not very polished.

  •    Python

I've been using Kaggle as an excuse to learn techniques in machine learning/artificial intelligence. Here are some primary resources I've been learning from (in rough chronological order). For reference, I started from an extensive programming background, a decent but rusty math background, and a rudimentary background in machine learning.

u-net - U-Net: Convolutional Networks for Biomedical Image Segmentation

  •    Python

This tutorial shows how to use Keras library to build deep neural network for ultrasound image nerve segmentation. More info on this Kaggle competition can be found on https://www.kaggle.com/c/ultrasound-nerve-segmentation. This deep neural network achieves ~0.57 score on the leaderboard based on test images, and can be a good staring point for further, more serious approaches.

web-traffic-forecasting - Kaggle | Web Traffic Forecasting 📈

  •    Python

My solution for the Web Traffic Forecasting competition hosted on Kaggle. The training dataset consists of approximately 145k time series. Each of these time series represents a number of daily views of a different Wikipedia article, starting from July 1st, 2015 up until September 10th, 2017. The goal is to forecast the daily views between September 13th, 2017 and November 13th, 2017 for each article in the dataset. The name of the article as well as the type of traffic (all, mobile, desktop, spider) is given for each article.

satellite-image-deep-learning - Resources for performing deep learning on satellite imagery

  •    Jupyter

This document primarily lists resources for performing deep learning (DL) on satellite imagery. To a lesser extent Machine learning (ML, e.g. random forests, stochastic gradient descent) are also discussed, as are classical image processing techniques. Kaggle hosts several large satellite image datasets (> 1 GB). A list if general image datasets is here. A list of land-use datasets is here. The kaggle blog is an interesting read.

kaggle-galaxies - Winning solution for the Galaxy Challenge on Kaggle (http://www

  •    Python

Winning solution for the Galaxy Challenge on Kaggle (http://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge). Documentation about the method and the code is available in doc/documentation.pdf. Information on how to generate the solution file can also be found below.

docker-python - Kaggle Python docker image

  •    Dockerfile

This is the Dockerfile (etc.) used for building the image that runs python scripts on Kaggle. Here's the Docker image on Dockerhub. To get started with this image, read our guide to using it yourself, or browse Kaggle Kernels for ideas.

Kaggle-Ensemble-Guide - Code for the Kaggle Ensembling Guide Article on MLWave

  •    Python

Code for the Kaggle Ensembling Guide Article on MLWave

Kaggle-Solution - Solution Code for Kaggle Competition

  •    Python

This project contain my code for competition in kaggle.

Deep-Learning-Boot-Camp - A community run, 5-day PyTorch Deep Learning Bootcamp

  •    Jupyter

Tel-Aviv Deep Learning Bootcamp is an intensive (and free!) 5-day program intended to teach you all about deep learning. It is nonprofit focused on advancing data science education and fostering entrepreneurship. The Bootcamp is a prominent venue for graduate students, researchers, and data science professionals. It offers a chance to study the essential and innovative aspects of deep learning. Participation is via a donation to the A.L.S ASSOCIATION for promoting research of the Amyotrophic Lateral Sclerosis (ALS) disease.

kaggle-cifar10-torch7 - Code for Kaggle-CIFAR10 competition. 5th place.

  •    Lua

Please check your Torch7/CUDA environment when this code fails. Place the data files into a subfolder ./data.

kaggle-api - Official Kaggle API

  •    Python

Official API for https://www.kaggle.com, accessible using a command line tool implemented in Python. Beta release - Kaggle reserves the right to modify the API functionality currently offered.

kaggle - Kaggle 项目实战(教程) = 文档 + 代码 + 视频

  •    Jupyter

Kaggle 项目实战(教程) = 文档 + 代码 + 视频

scikit-neuralnetwork - Deep neural networks without the learning cliff! Classifiers and regressors compatible with scikit-learn

  •    Python

Deep neural network implementation without the learning cliff! This library implements multi-layer perceptrons, auto-encoders and (soon) recurrent neural networks with a stable Future Proof™ interface that's compatible with scikit-learn for a more user-friendly and Pythonic interface. It's a wrapper for powerful existing libraries such as lasagne currently, with plans for blocks. By importing the sknn package provided by this library, you can easily train deep neural networks as regressors (to estimate continuous outputs from inputs) and classifiers (to predict discrete labels from features).