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  • πŸ‘‹ Hi, I’m Hieu
  • πŸ‘€ I’m interested in Machine Learning
  • 🌱 I’m currently learning Time Series Analysis
  • πŸ’žοΈ I’m looking to collaborate on Machine Learning
  • πŸ“« How to reach me ...

Hieu Vu Tran's Projects

arc-tutorial icon arc-tutorial

Tutorial on Activity Recognition Chain for Wearable Devices

best-of-ml-python icon best-of-ml-python

πŸ† A ranked list of awesome machine learning Python libraries. Updated weekly.

cen icon cen

Causal Effect Network

covariate-shift-adaption icon covariate-shift-adaption

An example of using a discriminator to correct for a difference in the distributions between the training and test data.

data-gen-keras icon data-gen-keras

This repository is associated with the blog post on "Data Generators with Keras and Tensorflow on Google Colab"

dipole icon dipole

DIstributed Persistence-Optimized Local Embeddings

ecg-523 icon ecg-523

Cardiologist-level arrhythmia detection and classification using deep neural networks.

ecg-mit-bih icon ecg-mit-bih

ECG classification using MIT-BIH data, a deep CNN learning implementation of Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network, https://www.nature.com/articles/s41591-018-0268-3 and also deploy the trained model to a web app using Flask, introduced at

entropyhub icon entropyhub

An open-source toolkit for entropic time-series analysis.

espresso icon espresso

Entropy and ShaPe awaRe timE-Series SegmentatiOn forprocessing heterogeneous sensor data

face-api.js icon face-api.js

JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js

flirt icon flirt

Are you ready to FLIRT with your wearable data?

gaf-cnn icon gaf-cnn

Multivariate timeseries to multivariate timeseries convolution regressor based on the article "Encoding Time Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks" by Wang, Z.; Oates, T. in Proceedings of the Workshops at AAAI Conference on Artificial Intelligence, Honolulu, HI, USA, 27 January–1 February 2019, pp. 40–46.

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