digit recognizerCNN digit recognizer implemented in Keras Notebook, Kaggle/MNIST (0.995).
Stars: ✭ 27 (-89.73%)
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 (-40.3%)
Kaggle TitanicA tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.
Stars: ✭ 709 (+169.58%)
KaggleKaggle Kernels (Python, R, Jupyter Notebooks)
Stars: ✭ 26 (-90.11%)
Tgs SaltnetKaggle | 21st place solution for TGS Salt Identification Challenge
Stars: ✭ 81 (-69.2%)
spark-fmA parallel implementation of factorization machines based on Spark
Stars: ✭ 73 (-72.24%)
word2vec-moviesBag of words meets bags of popcorn in Python 3 中文教程
Stars: ✭ 54 (-79.47%)
Kaggle dstl submissionCode for a winning model (3 out of 419) in a Dstl Satellite Imagery Feature Detection challenge
Stars: ✭ 159 (-39.54%)
Amazon Forest Computer VisionAmazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks
Stars: ✭ 346 (+31.56%)
Data science bowl 2018My 5th place (out of 816 teams) solution to The 2018 Data Science Bowl organized by Booz Allen Hamilton
Stars: ✭ 147 (-44.11%)
kaggleKaggle solutions
Stars: ✭ 17 (-93.54%)
WheatWheat Detection challenge on Kaggle
Stars: ✭ 54 (-79.47%)
Quora QuestionPairs DLKaggle Competition: Using deep learning to solve quora's question pairs problem
Stars: ✭ 54 (-79.47%)
KaggleMy kaggle competition solution and notebook
Stars: ✭ 14 (-94.68%)
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 (+117.49%)
Cikm 2019 Analyticup1st Solution for 2019-CIKM-Analyticup, Efficient and Novel Item Retrieval for Large-scale Online Shopping Recommendation
Stars: ✭ 173 (-34.22%)
automated-essay-gradingSource code for the paper A Memory-Augmented Neural Model for Automated Grading
Stars: ✭ 101 (-61.6%)
ferFacial Expression Recognition
Stars: ✭ 32 (-87.83%)
Machine Learning And Data ScienceThis is a repository which contains all my work related Machine Learning, AI and Data Science. This includes my graduate projects, machine learning competition codes, algorithm implementations and reading material.
Stars: ✭ 137 (-47.91%)
Machine-LearningThe projects I do in Machine Learning with PyTorch, keras, Tensorflow, scikit learn and Python.
Stars: ✭ 54 (-79.47%)
SegmentationTensorflow implementation : U-net and FCN with global convolution
Stars: ✭ 101 (-61.6%)
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 (-67.3%)
Apartment-Interest-PredictionPredict people interest in renting specific NYC apartments. The challenge combines structured data, geolocalization, time data, free text and images.
Stars: ✭ 17 (-93.54%)
Unet TgsApplying UNET Model on TGS Salt Identification Challenge hosted on Kaggle
Stars: ✭ 81 (-69.2%)
argus-tgs-saltKaggle | 14th place solution for TGS Salt Identification Challenge
Stars: ✭ 73 (-72.24%)
Data Science Bowl 2018DATA-SCIENCE-BOWL-2018 Find the nuclei in divergent images to advance medical discovery
Stars: ✭ 76 (-71.1%)
HumanOrRobota solution for competition of kaggle `Human or Robot`
Stars: ✭ 16 (-93.92%)
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 (-79.09%)
kuzushiji-recognition5th place solution for the Kaggle Kuzushiji Recognition Challenge
Stars: ✭ 41 (-84.41%)
customer churn prediction零售电商客户流失模型,基于tensorflow,xgboost4j-spark,spark-ml实现LR,FM,GBDT,RF,进行模型效果对比,离线/在线部署方式总结
Stars: ✭ 58 (-77.95%)
StoreItemDemand(117th place - Top 26%) Deep learning using Keras and Spark for the "Store Item Demand Forecasting" Kaggle competition.
Stars: ✭ 24 (-90.87%)
Kaggle Homedepot3rd Place Solution for HomeDepot Product Search Results Relevance Competition on Kaggle.
Stars: ✭ 452 (+71.86%)
deep-ctrNo description or website provided.
Stars: ✭ 92 (-65.02%)
kaggler🏁 API client for Kaggle
Stars: ✭ 50 (-80.99%)
audiotagging20196th place solution to Freesound Audio Tagging 2019 kaggle competition
Stars: ✭ 21 (-92.02%)
DetoxifyTrained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers.
Stars: ✭ 188 (-28.52%)