Pinned Repositories
codingTest
deep-learning-for-time-series-data
The examples showcase two ways of using deep learning for classifying time-series data, i.e. ECG data. The first way is using continuous wavelet transform and transfer learning, whereas the second way is using Wavelet Scattering and LSTMs. The explanations of the code are in Chinese. The used data set can be download on：https://github.com/mathworks/physionet_ECG_data/
ecg
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
eeg-gcnn
Resources for the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with an oral spotlight!) at ML4H Workshop, NeurIPS 2020.
myBlog-record
`nginx` + `chevereto `+ `halo` + `nextcloud` 个人博客、图床、网盘
Cuteriavka's Repositories
Cuteriavka/myBlog-record
`nginx` + `chevereto `+ `halo` + `nextcloud` 个人博客、图床、网盘
Cuteriavka/codingTest
Cuteriavka/deep-learning-for-time-series-data
The examples showcase two ways of using deep learning for classifying time-series data, i.e. ECG data. The first way is using continuous wavelet transform and transfer learning, whereas the second way is using Wavelet Scattering and LSTMs. The explanations of the code are in Chinese. The used data set can be download on：https://github.com/mathworks/physionet_ECG_data/
Cuteriavka/ecg
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
Cuteriavka/eeg-gcnn
Resources for the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with an oral spotlight!) at ML4H Workshop, NeurIPS 2020.