KienMN/Machine-Learning-A-Z

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Created over 6 years ago
Updated over 6 years ago

KienMN/Machine-Learning-A-Z

KienMN

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Comprehensive Python code of the course Machine learning A-Z

STGNN-for-Covid-in-Korea

Spatio-temporal graph neural network for predicting COVID-19 new cases in Korea.

JupyterHub-on-Kubernetes

Manual deployment of JupyterHub on Kubernetes for a single machine

Weisfeiler-Lehman-Neural-Machine

Python code for "M. Zhang, Y. Chen, Weisfeiler-Lehman Neural Machine for Link Prediction, KDD 2017"

Jupyter Notebook

Feature-Selection-and-Feature-Extraction

Feature Selection and Feature Extraction methods for data pre-processing.

Jupyter Notebook

Standalone-Spark-on-Kubernetes

Deployment of Standalone spark on Kubernetes for a single machine

Image-Processing-2018-Water-Extraction-Using-Object-Oriented-Method

Water extraction using object oriented method which was described in the same name paper.

Autoencoder-Experiments

Implementation of different autoencoders, evaluating on different datasets.

COVID-19-in-Korea-graph

COVID-19 patient classification in Korea using Heterogeneous Graph Neural Network.

Activation-Experiments

Experiments using different activation functions.

Plant-Monitoring

Applying IoT solution for Plant monitoring

WebFinal2017

GR-Curve-Unit-Breakdown

Several functions to analyse and visualise curves of gamma ray

sale-forecasting-notebooks

Different models when dealing with time series data

Jupyter Notebook

Cloudera-Training

Human-activity-recoginition

Human activity recognition based on a time series dataset

Jupyter Notebook