| key | value |
|---|---|
| id | 74831258 |
| name | HAR-stacked-residual-bidir-LSTMs |
| full_name | guillaume-chevalier/HAR-stacked-residual-bidir-LSTMs |
| html_url | https://github.com/guillaume-chevalier/HAR-stacked-residual-bidir-LSTMs |
| description | Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets. |
| created_at | Nov. 26, 2016, 2:41 p.m. |
| updated_at | Oct. 29, 2024, 1:35 a.m. |
| pushed_at | Nov. 6, 2022, 5:59 p.m. |
| size | 533 |
| stargazers_count | 319 |
| watchers_count | 19 |
| forks_count | 100 |
| open_issues | 3 |
| language | Python |
| awesome_list |
https://github.com/guillaume-chevalier/Awesome-Deep-Learning-Resources |