Displaying 1 to 20 from 27 results

LightGBM - A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks

  •    C++

For more details, please refer to Features.Experiments on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, the experiments show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.




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.

open-solution-home-credit - Open solution to the Home Credit Default Risk challenge :house_with_garden:

  •    Python

This is an open solution to the Home Credit Default Risk challenge 🏡. In this open source solution you will find references to the neptune.ml. It is free platform for community Users, which we use daily to keep track of our experiments. Please note that using neptune.ml is not necessary to proceed with this solution. You may run it as plain Python script 🐍.

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).


fast_retraining - Show how to perform fast retraining with LightGBM in different business cases

  •    Jupyter

In this repo we compare two of the fastest boosted decision tree libraries: XGBoost and LightGBM. We will evaluate them across datasets of several domains and different sizes.On July 25, 2017, we published a blog post evaluating both libraries and discussing the benchmark results. The post is Lessons Learned From Benchmarking Fast Machine Learning Algorithms.

minimal-datascience - This repository contains all the code and dataset used in my blog series: Minimal Data Science

  •    Python

My goal for this minimal data science blog series is not only sharing, tutorializing, but also, making personal notes while learning and working as a Data Scientist. I’m looking forward to receiving any feedback from you. Chapter-1: Classify StarCraft 2 players with Python Pandas and Scikit-learn.

kaggle-malware-classification - Kaggle "Microsoft Malware Classification Challenge"

  •    Python

Kaggle "Microsoft Malware Classification Challenge". 6th place solution

Apartment-Interest-Prediction - Predict people interest in renting specific NYC apartments

  •    Jupyter

Predict people interest in renting specific apartments. The challenge combines structured data, geolocalization, time data, free text and images. This solution features Gradient Boosted Trees (XGBoost and LightGBM) and does not use stacking, due to lack of time.

March-Madness-2017 - Kaggle Competition for Predicting NCAA Basketball Tourney Games

  •    Jupyter

Kaggle Competition for Predicting NCAA Basketball Tourney Games. Link to the associated blog post I wrote.

kaggle-carvana - Solution for the Carvana Image Masking Challenge on Kaggle

  •    Python

The solution for the Carvana Image Masking Challenge on Kaggle. It uses a custom version of RefineNet with Squeeze-and-Excitation modules implemented in PyTorch. It was a part of the final ensemble that was ranked 23 out of 735 teams (top 4%). The goal of the Carvana Image Masking Challenge was to develop an algorithm that removes a background from a wide variety of car photos. Here you can see predictions from a trained neural network for 16 images of a single car.

open-solution-avito-demand-prediction - Open solution to the Avito Demand Prediction Challenge

  •    Jupyter

This is an open solution to the Avito Demand Prediction Challenge. In this open source solution you will find references to the neptune.ml. It is free platform for community Users, which we use daily to keep track of our experiments. Please note that using neptune.ml is not necessary to proceed with this solution. You may run it as plain Python script 😉.

open-solution-cdiscount-starter - Open solution to the Cdiscount’s Image Classification Challenge

  •    Python

This is ready to use, end-to-end sample solution for the currently running Kaggle Cdiscount challenge. It involves data loading and augmentation, model training (many different architectures), ensembling and submit generator.

open-solution-data-science-bowl-2018 - Open solution to the Data Science Bowl 2018

  •    Python

This is an open solution to the Data Science Bowl 2018 based on the topcoders winning solution from ods.ai. In this open source solution you will find references to the neptune.ml. It is free platform for community Users, which we use daily to keep track of our experiments. Please note that using neptune.ml is not necessary to proceed with this solution. You may run it as plain Python script 😉.

open-solution-ship-detection - Open solution to the Airbus Ship Detection Challenge

  •    Python

This is an open solution to the Airbus Ship Detection Challenge. In this open source solution you will find references to the neptune.ml. It is free platform for community Users, which we use daily to keep track of our experiments. Please note that using neptune.ml is not necessary to proceed with this solution. You may run it as plain Python script 🐍.