StoreItemDemand(117th place - Top 26%) Deep learning using Keras and Spark for the "Store Item Demand Forecasting" Kaggle competition.
Stars: ✭ 24 (-11.11%)
ferFacial Expression Recognition
Stars: ✭ 32 (+18.52%)
HumanOrRobota solution for competition of kaggle `Human or Robot`
Stars: ✭ 16 (-40.74%)
Amazon Forest Computer VisionAmazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks
Stars: ✭ 346 (+1181.48%)
Kaggle Homedepot3rd Place Solution for HomeDepot Product Search Results Relevance Competition on Kaggle.
Stars: ✭ 452 (+1574.07%)
SegmentationTensorflow implementation : U-net and FCN with global convolution
Stars: ✭ 101 (+274.07%)
kaggleKaggle solutions
Stars: ✭ 17 (-37.04%)
argus-tgs-saltKaggle | 14th place solution for TGS Salt Identification Challenge
Stars: ✭ 73 (+170.37%)
Apartment-Interest-PredictionPredict people interest in renting specific NYC apartments. The challenge combines structured data, geolocalization, time data, free text and images.
Stars: ✭ 17 (-37.04%)
kaggler🏁 API client for Kaggle
Stars: ✭ 50 (+85.19%)
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 (+218.52%)
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 (+2018.52%)
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 (+481.48%)
VAE-Latent-Space-ExplorerInteractive exploration of MNIST variational autoencoder latent space with React and tensorflow.js.
Stars: ✭ 30 (+11.11%)
kaggle-berlinMaterial of the Kaggle Berlin meetup group!
Stars: ✭ 36 (+33.33%)
digitRecognitionImplementation of a digit recognition using my Neural Network with the MNIST data set.
Stars: ✭ 21 (-22.22%)
Fun-with-MNISTPlaying with MNIST. Machine Learning. Generative Models.
Stars: ✭ 23 (-14.81%)
Kaggle-Quora-Question-PairsThis is our team's solution report, which achieves top 10% (305/3307) in this competition.
Stars: ✭ 58 (+114.81%)
Hand-Digits-RecognitionRecognize your own handwritten digits with Tensorflow, embedded in a PyQT5 GUI. The Neural Network was trained on MNIST.
Stars: ✭ 11 (-59.26%)
word2vec-moviesBag of words meets bags of popcorn in Python 3 中文教程
Stars: ✭ 54 (+100%)
fast retrainingShow how to perform fast retraining with LightGBM in different business cases
Stars: ✭ 56 (+107.41%)
MineColabRun Minecraft Server on Google Colab.
Stars: ✭ 135 (+400%)
Data-ScienceUsing Kaggle Data and Real World Data for Data Science and prediction in Python, R, Excel, Power BI, and Tableau.
Stars: ✭ 15 (-44.44%)
CNN-MNISTCNN classification model built in Keras used for Digit Recognizer task on Kaggle (https://www.kaggle.com/c/digit-recognizer)
Stars: ✭ 23 (-14.81%)
AutoXAutoX is an efficient automl tool, which is mainly aimed at data mining tasks with tabular data.
Stars: ✭ 431 (+1496.3%)
MNIST-CoreMLPredict handwritten digits with CoreML
Stars: ✭ 63 (+133.33%)
Any-file-to-Google-DriveThis Google Colab notebook will help you download any file directly to Google Drive with the help of the JDownloader web interface
Stars: ✭ 47 (+74.07%)
TF2DeepFloorplanTF2 Deep FloorPlan Recognition using a Multi-task Network with Room-boundary-Guided Attention. Enable tensorboard, quantization, flask, tflite, docker, github actions and google colab.
Stars: ✭ 98 (+262.96%)
Quora QuestionPairs DLKaggle Competition: Using deep learning to solve quora's question pairs problem
Stars: ✭ 54 (+100%)
catacombThe simplest machine learning library for launching UIs, running evaluations, and comparing model performance.
Stars: ✭ 13 (-51.85%)
tensorflow-mnist-AAETensorflow implementation of adversarial auto-encoder for MNIST
Stars: ✭ 86 (+218.52%)
Quantitative-Big-Imaging-2018(Latest semester at https://github.com/kmader/Quantitative-Big-Imaging-2019) The material for the Quantitative Big Imaging course at ETHZ for the Spring Semester 2018
Stars: ✭ 50 (+85.19%)
docker-kaggle-ko머신러닝/딥러닝(PyTorch, TensorFlow) 전용 도커입니다. 한글 폰트, 한글 자연어처리 패키지(konlpy), 형태소 분석기, Timezone 등의 설정 등을 추가 하였습니다.
Stars: ✭ 46 (+70.37%)