semvec/pypi-packages huggingface dataset:
pbatoolkit-gpu 0.0.3
Physics Based Animation Toolkit with GPU support
Requires Python: >=3.10

[![wheels](https://github.com/Q-Minh/pbatoolkit-gpu/actions/workflows/wheels.yml/badge.svg?event=release)](https://github.com/Q-Minh/pbatoolkit-gpu/actions/workflows/wheels.yml)
![pbatoolkit-gpu](https://img.shields.io/pypi/v/pbatoolkit-gpu?label=pbatoolkit-gpu)
![downloads](https://img.shields.io/pypi/dm/pbatoolkit-gpu)

Repository for re-packaging the [PhysicsBasedAnimationToolkit](https://github.com/Q-Minh/PhysicsBasedAnimationToolkit)'s python bindings [`pbatoolkit`](https://pypi.org/project/pbatoolkit/) into [`pbatoolkit-gpu`](https://pypi.org/project/pbatoolkit-gpu/). Refer to [PhysicsBasedAnimationToolkit](https://github.com/Q-Minh/PhysicsBasedAnimationToolkit) for documentation.
md-katex 0.3.0
A Markdown extension for rendering LaTeX math using Katex
Requires Python: >=3.9

## About md_katex

![Build Status](https://github.com/dbzhang800/md_katex/actions/workflows/python-package.yml/badge.svg)

**md_katex** is a KaTeX plugin initially developed for use in personal blogs with **Pelican**. It focuses on rendering mathematical formulas directly in the browser using the KaTeX JavaScript library, rather than performing offline conversions.

### Features

- **Client-Side Rendering**: Unlike other plugins, **md_katex** does not convert formulas offline. Instead, the KaTeX JavaScript file in the browser handles the rendering, making the publishing process simpler and more efficient.
- **Supports Multiple Formula Delimiter Styles**:
  - **GitLab Style**: Use `` $` `` and `` `$ `` for inline formulas, and `~~~math` code blocks for block-level formulas.
  - **Brackets Style**: Use `\(` and `\)` for inline formulas, and `\[` and `\]` for block-level formulas.
  - **GitHub Style**: Use `$$` as both the opening and closing delimiters for block-level formulas.

### Installation

First, ensure that you have the `python-markdown` library installed. Then, you can install this plugin using the following command:

```bash
pip install md_katex
```

The generated HTML will include KaTeX formulas, and you will need to load KaTeX JavaScript on the frontend to complete the rendering.

To ensure proper rendering, include the KaTeX script via a `<script>` tag in your HTML page. For example, using a CDN:

```html
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous">
    <script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script>
    <script>document.addEventListener("DOMContentLoaded", function () {
 var mathElements = document.getElementsByClassName("math");
 var macros = [];
 for (var i = 0; i < mathElements.length; i++) {
  var texText = mathElements[i].firstChild;
  if (mathElements[i].tagName == "SPAN" || mathElements[i].tagName === "DIV") {
   katex.render(texText.data, mathElements[i], {
    displayMode: mathElements[i].classList.contains('display'),
    throwOnError: false,
    macros: macros,
    fleqn: false
   });
}}});
    </script>
```

## References

* https://github.com/mbarkhau/markdown-katex
* https://github.com/martenlienen/pelican-katex
* https://github.com/oruelle/md_mermaid
* https://github.com/goessner/markdown-it-texmath
* https://docs.gitlab.com/ee/user/markdown.html#math
* https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/writing-mathematical-expressions
* https://katex.org/docs/autorender
SapphireDL 0.1.6
Library for simple DL structure

# Description of the Deep Learning Library Sapphire

## Introduction
This documentation is dedicated to a deep learning library designed for the efficient creation and training of machine learning models.

## Installation
To install the library, run the following command:
```bash
pip install SapphireDL
```

## Key Features
Example for XOR

### 1. Creating a Model
The library allows you to easily create complex neural networks using high-level APIs.

```python
import numpy as np

from sapphiredl.training import train
from sapphiredl.neural_networks import NeuralNetwork
from sapphiredl.layers import LinearLayer, HypTan

inputs = np.array([
    [0, 0],
    [1, 0],
    [0, 1],
    [1, 1]
])

targets = np.array([
    [1, 0],
    [0, 1],
    [0, 1],
    [1, 0]
])

nn = NeuralNetwork([
    LinearLayer(input_size=2, output_size=2),
    HypTan(),
    LinearLayer(input_size=2, output_size=2)
])
```

### 2. Training the Model
Training models becomes simple with built-in methods.

```python
num_epochs = 5000

train(neural_network=nn, inputs=inputs, targets=targets, epochs_count=num_epochs)
```

### 3. Prediction
After training, you can use the model to predict new data.

```python
for x, y in zip(inputs, targets):
    predicted = nn.forward(x)
    print(x, predicted, y)  # np.round(predicted, decimals=7)
```

## API Documentation
For more detailed information about each method and class in the library, please refer to the API documentation.

## Additional Resources

- [Documentation](https://github.com/itbert/SapphireDL/documentation)
- [Usage Examples](https://github.com/itbert/SapphireDL)