Apartment-Interest-PredictionPredict people interest in renting specific NYC apartments. The challenge combines structured data, geolocalization, time data, free text and images.
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ferFacial Expression Recognition
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argus-tgs-saltKaggle | 14th place solution for TGS Salt Identification Challenge
Stars: ✭ 73 (-38.14%)
Kaggle CompetitionsThere are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.
Stars: ✭ 86 (-27.12%)
Amazon Forest Computer VisionAmazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks
Stars: ✭ 346 (+193.22%)
SegmentationTensorflow implementation : U-net and FCN with global convolution
Stars: ✭ 101 (-14.41%)
Machine Learning Workflow With PythonThis is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation
Stars: ✭ 157 (+33.05%)
digit recognizerCNN digit recognizer implemented in Keras Notebook, Kaggle/MNIST (0.995).
Stars: ✭ 27 (-77.12%)
StoreItemDemand(117th place - Top 26%) Deep learning using Keras and Spark for the "Store Item Demand Forecasting" Kaggle competition.
Stars: ✭ 24 (-79.66%)
HumanOrRobota solution for competition of kaggle `Human or Robot`
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kaggler🏁 API client for Kaggle
Stars: ✭ 50 (-57.63%)
Data Science CompetitionsGoal of this repo is to provide the solutions of all Data Science Competitions(Kaggle, Data Hack, Machine Hack, Driven Data etc...).
Stars: ✭ 572 (+384.75%)
kaggleKaggle solutions
Stars: ✭ 17 (-85.59%)
Kaggle Homedepot3rd Place Solution for HomeDepot Product Search Results Relevance Competition on Kaggle.
Stars: ✭ 452 (+283.05%)
Data Science Bowl 2018End-to-end one-class instance segmentation based on U-Net architecture for Data Science Bowl 2018 in Kaggle
Stars: ✭ 56 (-52.54%)
Qh finsight国内首个迁移学习赛题 中国平安前海征信“好信杯”迁移学习大数据算法大赛 FInSight团队作品(算法方案排名第三)
Stars: ✭ 55 (-53.39%)
WheatWheat Detection challenge on Kaggle
Stars: ✭ 54 (-54.24%)
Pytorch zooA collection of useful modules and utilities (especially helpful for kaggling) not available in Pytorch
Stars: ✭ 84 (-28.81%)
Facial Expression RecognitionClassify each facial image into one of the seven facial emotion categories considered using CNN based on https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge
Stars: ✭ 82 (-30.51%)
Kaggle NdsbCode for National Data Science Bowl. 10th place.
Stars: ✭ 45 (-61.86%)
Kaggle HousepricesKaggle Kernel for House Prices competition https://www.kaggle.com/massquantity/all-you-need-is-pca-lb-0-11421-top-4
Stars: ✭ 113 (-4.24%)
Tgs SaltnetKaggle | 21st place solution for TGS Salt Identification Challenge
Stars: ✭ 81 (-31.36%)
Unet TgsApplying UNET Model on TGS Salt Identification Challenge hosted on Kaggle
Stars: ✭ 81 (-31.36%)
MachinelearningcourseA collection of notebooks of my Machine Learning class written in python 3
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HealthcheckHealth Check ✔ is a Machine Learning Web Application made using Flask that can predict mainly three diseases i.e. Diabetes, Heart Disease, and Cancer.
Stars: ✭ 35 (-70.34%)
Kaggle Daekaggleのporto-seguro-safe-driver-prediction, michaelのsolver
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Pytorch ToolbeltPyTorch extensions for fast R&D prototyping and Kaggle farming
Stars: ✭ 942 (+698.31%)
KaggleMy kaggle competition solution and notebook
Stars: ✭ 14 (-88.14%)
CryptoCryptocurrency Historical Market Data R Package
Stars: ✭ 112 (-5.08%)
Data Science Bowl 2018DATA-SCIENCE-BOWL-2018 Find the nuclei in divergent images to advance medical discovery
Stars: ✭ 76 (-35.59%)
InterviewInterview = 简历指南 + LeetCode + Kaggle
Stars: ✭ 7,207 (+6007.63%)
MlboxMLBox is a powerful Automated Machine Learning python library.
Stars: ✭ 1,199 (+916.1%)
Deepfake DetectionDeepFake Detection: Detect the video is fake or not using InceptionResNetV2.
Stars: ✭ 23 (-80.51%)
D2l EnInteractive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge.
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