Displaying 1 to 13 from 13 results

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-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).

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.

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-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.

xgboost-node - Run XGBoost model and make predictions in Node.js

  •    Cuda

XGBoost-Node is a Node.js interface of XGBoost. XGBoost is a library from DMLC. It is designed and optimized for boosted trees. The underlying algorithm of XGBoost is an extension of the classic gbm algorithm. With multi-threads and regularization, XGBoost is able to utilize more computational power and get a more accurate prediction. The package is made to run existing XGBoost model with Node.js easily.

NYISOToolkit - Access data, statistics, and visualizations for New York's electricity grid.

  •    Python

A package for accessing power system data (NYISOData), generating statistics (NYISOStat), and creating visualizations (NYISOVis) from the New York Independent System Operator (NYISO). There are several visualizations currently supported - browse them on the NYISOToolkit Web App or in the nyisotoolkit/nyisovis/visualizations folder. The visualizations are focused on communicating New York's status toward achieving the power sector decarbonization goals outlined by the Climate Leadership and Community Protection Act (CLCPA).

ashrae-great-energy-predictor-3-solution-analysis - Analysis of top give winning solutions of the ASHRAE Great Energy Predictor III competition

  •    Jupyter

This repository contains the code and documentation of top-5 winning solutions from the ASHRAE - Great Energy Predictor III cometition that was held in late 2019 on the Kaggle platform. It also contains comparative analysis of these solutions with respect to their characteristics such as workflow, computation time, and score distributation with respect to meter type, site, and primary space usage, etc. In late 2019, ASHRAE hosted the Great Energy Predictor III (GEPIII) machine learning competition on the Kaggle platform. This launch marked the third energy prediction competition from ASHRAE and the first since the mid-1990s. In this updated version, the competitors were provided with over 20 million points of training data from 2,380 energy meters collected for 1,448 buildings from 16 sources. This competition’s overall objective was to find the most accurate modeling solutions for the prediction of over 41 million private and public test data points. The competition had 4,370 participants, split across 3,614 teams from 94 countries who submitted 39,403 predictions. In addition to the top five winning workflows, the competitors publicly shared 415 reproducible online machine learning workflow examples (notebooks), including over 40 additional, full solutions. This paper gives a high-level overview of the competition preparation and dataset, competitors and their discussions, machine learning workflows and models generated, winners and their submissions, discussion of lessons learned, and competition outputs and next steps. The most popular and accurate machine learning workflows used large ensembles of mostly gradient boosting tree models, such as LightGBM. Similar to the first predictor competition, preprocessing of the data sets emerged as a key differentiator.

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