# guillaume-chevalier/HAR-stacked-residual-bidir-LSTMs

<https://repo.masters-masters.com/en/repos/guillaume-chevalier/HAR-stacked-residual-bidir-LSTMs>

[Home](https://repo.masters-masters.com/en) › [guillaume-chevalier](https://repo.masters-masters.com/en/owners/guillaume-chevalier) › [HAR-stacked-residual-bidir-LSTMs](https://repo.masters-masters.com/en/repos/guillaume-chevalier/HAR-stacked-residual-bidir-LSTMs)

guillaume-chevalier/HAR-stacked-residual-bidir-LSTMs has 322 stars on GitHub. Its RepoMasters card: Alpha tier. Using deep stacked residual bidirectional LSTM…

| Field | Value |
| --- | --- |
| Owner | [guillaume-chevalier](https://repo.masters-masters.com/en/owners/guillaume-chevalier) |
| Stars | 322 |
| Forks | 96 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | Apache-2.0 |
| Created | 2016 |
| Topics | tensorflow, lstm, rnn, stacked-layers, residual-layers, residual-lstm-cells, bidirectional-lstm-cells, human-activity-recognition |
| Tier | [Alpha (250 to 699 ★)](https://repo.masters-masters.com/en/ranks/b_movie) |
| View on GitHub | [guillaume-chevalier/HAR-stacked-residual-bidir-LSTMs](https://github.com/guillaume-chevalier/HAR-stacked-residual-bidir-LSTMs) |

## Repository

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.

The tier for repos with 250 to 699 ★. Published odds per card pulled: 20%. Rarity: Uncommon.

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