Displaying 1 to 20 from 21 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.

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.




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.

kaggle-airbnb-recruiting-new-user-bookings - 2nd Place Solution in Kaggle Airbnb New User Bookings competition

  •    R

2nd place solution for Airbnb New User Bookings Competition. Note: This code should be differ from my submitted solution(Public:0.88209/Private:0.88682) because of the seed settings. if you select a model of more than 5 fold-CV 0.833600, you can get about 0.88682(Private).


kaggle-lshtc - Code for Large Scale Hierarchical Text Classification competition. Final place: 3rd

  •    C++

Code for Large Scale Hierarchical Text Classification competition. a centroid-based flat classifier.

kaggle-ndsb - Code for National Data Science Bowl. 10th place.

  •    Lua

Code for National Data Science Bowl at Kaggle. Ranked 10th/1049. Ensemble Deep CNNs trained with real-time data augmentation.

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 🐍.

open-solution-talking-data - Open solution to the TalkingData AdTracking Fraud Detection Challenge

  •    Jupyter

This is an open solution to the TalkingData Challenge. Deliver open source, ready-to-use and extendable solution to this competition. This solution should - by itself - establish solid benchmark, as well as provide good base for your custom ideas and experiments.

open-solution-toxic-comments - Open solution to the Toxic Comment Classification Challenge

  •    Python

Here, at Neptune we enjoy participating in the Kaggle competitions. Toxic Comment Classification Challenge is especially interesting because it touches important issue of online harassment. You need to be registered to neptune.ml to be able to use our predictions for your ensemble models.

Credit-Card-Fraud - A Very Deep Neural Network that can classify Credit Card Fraudulent Transaction with 99

  •    Jupyter

In the world of Technology, Credit Card Fraudulent Transactions are fairly Common and this is a Deep Neural Network (Algorithm) that can classify these transactions just by looking at the data with 99.92% Accuracy which is likely to be very accurate. This Neural Network is based of the Credit-Card-Fraud Data available on Kaggle, It contains a whooping 248,407 Transactions which occurred in September 2013 by European Card Holders.