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# <img src='https://raw.githubusercontent.com/cefet-rj-dal/daltoolboxdp/master/inst/logo.png' alt='Logo do pacote daltoolboxdp' align='centre' height='150' width='139'/> DAL Toolbox Deep Python

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daltoolboxdp extends `daltoolbox` with Python-backed components, with
emphasis on deep learning and Python-native modeling. In the package
name, `dp` stands for **Deep Python**.

It currently focuses on:

- Deep learning models backed by `torch`
- Scikit-learn classifiers exposed through the `daltoolbox` API
- Time-series forecasting models backed by Python
- Integration of Python model objects into the `daltoolbox`
  architecture

These capabilities rely on the `reticulate` bridge, so the package can
keep the object and workflow conventions of `daltoolbox` while
delegating training, encoding, and prediction to Python libraries such
as `torch` and `scikit-learn`.

The architecture is inspired by the **Experiment Lines** approach, which
promotes modularity, extensibility, and interoperability across tools.  
More information on Experiment Lines is available in [Ogasawara et
al. (2009)](https://doi.org/10.1007/978-3-642-02279-1_20).

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# Examples

Example scripts are available at:

- [Classification](https://github.com/cefet-rj-dal/daltoolboxdp/tree/main/examples/classification)
- [Autoencoders](https://github.com/cefet-rj-dal/daltoolboxdp/tree/main/examples/autoencoder)
- [Time
  Series](https://github.com/cefet-rj-dal/daltoolboxdp/tree/main/examples/timeseries)

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# Installation

You can install the latest stable version from CRAN:

``` r
install.packages("daltoolboxdp")
```

To install the development version from GitHub:

``` r
library(devtools)
devtools::install_github("cefet-rj-dal/daltoolboxdp", force = TRUE, dependencies = FALSE, upgrade = "never")
```

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# Bug reports and feature requests

Please report issues or suggest new features via:

- [GitHub Issues](https://github.com/cefet-rj-dal/daltoolboxdp/issues)
