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

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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.

gbdt gbm machine-learning data-mining kaggle efficiency distributed lightgbm gbrt

data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines

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IPython Notebook(s) demonstrating deep learning functionality.IPython Notebook(s) demonstrating scikit-learn functionality.

machine-learning deep-learning data-science big-data aws tensorflow theano caffe scikit-learn kaggle spark mapreduce hadoop matplotlib pandas numpy scipy keras

catboost - CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R

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CatBoost is a machine learning method based on gradient boosting over decision trees. All CatBoost documentation is available here.

machine-learning decision-trees gradient-boosting gbm gbdt r kaggle gpu-computing catboost tutorial categorical-features distributed gpu coreml opensource data-science big-data

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

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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.

gpu nvidia docker-image machine-learning deep-learning data-science cuda-kernels kaggle-competition cuda pytorch pytorch-tutorials pytorch-tutorial bootcamp meetup kaggle kaggle-scripts pycuda

kaggle-web-traffic - 1st place solution

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1st place solution

kaggle-web-traffic kaggle time-series timeseries rnn-encoder-decoder rnn tensorflow cudnn cocob seq2seq

Machine-Learning / Deep-Learning / AI + Web3 -Tutorials

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A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

machine-learning deep-learning tensorflow pytorch keras matplotlib aws kaggle pandas scikit-learn torch artificial-intelligence neural-network convolutional-neural-networks tensorflow-tutorials python-data ipython-notebook capsule-network

dogs_vs_cats - 猫狗大战

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猫狗大战

kaggle image-classification keras-tutorials

machineJS - Automated machine learning- just give it a data file! Check out the production-ready version of this project at ClimbsRocks/auto_ml

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I just built out v2 of this project that now gives you analytics info from your models, and is production-ready. machineJS is an amazing research project that clearly proved there's a hunger for automated machine learning. auto_ml tackles this exact same goal, but with more features, cleaner code, and the ability to be copy/pasted into production.

machine-learning data-science machine-learning-library machine-learning-algorithms ml data-scientists javascript-library scikit-learn kaggle numerai automated-machine-learning automl auto-ml neuralnet neural-network algorithms random-forest svm naive-bayes bagging optimization brainjs date-night sklearn ensemble data-formatting js xgboost scikit-neuralnetwork knn k-nearest-neighbors gridsearch gridsearchcv grid-search randomizedsearchcv preprocessing data-formatter kaggle-competition

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

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

kaggle

Amazon-Forest-Computer-Vision - Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks

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Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks

pytorch data-augmentation kaggle-competition kaggle deep-learning computer-vision keras neural-networks neural-network-example transfer-learning

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

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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 🐍.

machine-learning deep-learning kaggle pipeline feature-engineering reproducible-experiments reproducibility pipeline-framework lightgbm xgboost neptune competition credit-scoring credit-risk open-source python3 python35

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

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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).

machine-learning deep-learning kaggle neural-networks competition

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

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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.

lightgbm xgboost boosted-trees machine-learning gpu benchmark azure distributed-systems gbdt gbm gbrt kaggle

socialpowernba - Social Power in the NBA (Comparing on the court performance with Social Influence in R and Python)

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This project and the data explores the relationship between Social Media, Salary, Influence, Performance and Team Valuation in the NBA.

r nba ggplot2 ipython-notebook jupyter-notebook machine-learning machine-learning-algorithms ml prediction social-network social-media salary influence court-performance pie social kaggle kaggle-dataset social-networks

pytorch-speech-commands - Speech commands recognition with PyTorch

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Convolutional neural networks for Google speech commands data set with PyTorch. We, xuyuan and tugstugi, have participated in the Kaggle competition TensorFlow Speech Recognition Challenge and reached the 10-th place. This repository contains a simplified and cleaned up version of our team's code.

speech-recognition deep-learning cifar10 neural-network kaggle classification pytorch resnet resnext densenet wide-residual-networks dual-path-networks

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

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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 kaggle-competition

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

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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.

kaggle xgboost mnist neural-network scikit-learn kaggle-contest

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

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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.

blog-series data-science scikit-learn xgboost kaggle machine-learning

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

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

kaggle malware machine-learning

kaggle-coupon-purchase-prediction - Code for RECRUIT Challenge. 5th place.

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Code for Coupon Purchase Prediction (RECRUIT Challenge). Note: This code is able to achieve a 5th place score (Private LB: 0.008776). But this is not a full version of my submitted solution (Private LB: 0.008905). My submitted solution is average of this solution and another XGBoost solution. This repositoy provides a simple version of 5th place solution.

kaggle