semvec/pypi-packages huggingface dataset:
Enemizer 2.1.3
Breath of the Wild Enemy Randomizer v2 by Echocolat
Requires Python: >=3.7

# Breath of the Wild Enemy Randomizer v2 by Echocolat 

_Big thanks to ArchLeaders for his help and MintLightning for cleaning up the scripts, some help with tweaking, and the arrangement into one file._

### What do you need:
1. BCML _(`pip install bcml`)_ | Setup required.
2. A legally obtained copy of Breath of the Wild v1.5.0 with DLC
3. **Enemizer** _(`pip install enemizer`)_

### How to use:
1. Open a cmd/terminal in any folder and type `enemize`.
2. Follow the prompts and wait for completion.
3. Enjoy **Enemizer**!!
	
_If you need any help, because you don't know how to make the mod working for example, don't hesitate to contact me (@Echocolat#9988 on Discord) or to put a comment on the GameBanana page of the mod._
luna-fviz 0.3.0
Feature Visualization with Tensorflow 2
Requires Python: >=3.6

# Luna

<img src="https://raw.githubusercontent.com/Sparkier/luna/master/luna.png" alt="icon showing a stylized moon" width="100"/>

Inspired by [Lucid](https://github.com/tensorflow/lucid), **Luna** is a Feature Visualization package for Tensorflow.
While Lucid does not support Tensorflow 2, **Luna** was built with Tensorflow 2 at its core.

**Luna is under active development. It is research code and not production-ready.**

To learn how to use Luna and for insights into its API, see our [documentation](http://a13x.io/luna/).

## Contributing

We greatly appreciate any effort to improve **Luna**.
If you want to contribute to its development, see our [contribution guidelines](./CONTRIBUTING.md).

## Recomended Reading

- [Feature Visualization](https://distill.pub/2017/feature-visualization/)
- [The Building Blocks of Interpretability](https://distill.pub/2018/building-blocks/)
- [Using Artiﬁcial Intelligence to Augment Human Intelligence](https://distill.pub/2017/aia/)
- [Visualizing Representations: Deep Learning and Human Beings](http://colah.github.io/posts/2015-01-Visualizing-Representations/)
- [Differentiable Image Parameterizations](https://distill.pub/2018/differentiable-parameterizations/)
- [Activation Atlas](https://distill.pub/2019/activation-atlas/)

## Related Talks

- [Lessons from a year of Distill ML Research](https://www.youtube.com/watch?v=jlZsgUZaIyY) (Shan Carter, OpenVisConf)
- [Machine Learning for Visualization](https://www.youtube.com/watch?v=6n-kCYn0zxU) (Ian Johnson, OpenVisConf)

## Community

While we admire their work, we have no affiliation with the Lucid authors or project. Nonetheless, if you are interested in research like this, the Distill slack ([join link](http://slack.distill.pub)) might be a good place for you to get to know other people in this area.

On the awesome Distill slack, Lucid has its own `#proj-lucid` channel, where general questions about the technology are discussed.