semvec/pypi-packages huggingface dataset:
qterm 0.0.2
QTerm is a Python module for spawning child applications and controlling them automatically
Requires Python: >=3.6

# QTerm
QTerm is a Python module for spawning child applications and controlling them automatically.

## **Install**

```
pip install qterm
```

## **Usage**

To interract with a child process use `QTerm` class:

```python
from qterm import QTerm

with QTerm() as term:
    print(term.exec('cd'))
```

In case command prompt pattern changes use `temp_prompt` method:

```python
from qterm import QTerm, PYTHON

print(PYTHON)

with QTerm() as term:
    with term.temp_prompt(*PYTHON):
        print(term.exec('1+1'))

# TermArgs(SPAWN_COMMAND='python', PROMPT=re.compile('^>>> '), EXIT_COMMAND='exit()')
# ['2']
```
manim-ml-alechelbling 0.0.1
Machine Learning Animations in python using Manim
Requires Python: >=3.6

# Manim Machine Learning
<a href="https://github.com/helblazer811/ManimMachineLearning">
    <img src="examples/ManimMLLogo.gif">
</a>

[![GitHub license](https://img.shields.io/github/license/helblazer811/ManimMachineLearning)](https://github.com/helblazer811/ManimMachineLearning/blob/main/LICENSE.md)
[![GitHub tag](https://img.shields.io/github/v/release/helblazer811/ManimMachineLearning)](https://img.shields.io/github/v/release/helblazer811/ManimMachineLearning)
[![Github All releases](https://img.shields.io/github/downloads/helblazer811/ManimMachineLearning/total)](https://GitHub.com/helblazer811/ManimMachineLearning/releases/)
[![Follow Twitter](https://img.shields.io/twitter/follow/alec_helbling?style=social)](https://twitter.com/alec_helbling)

Manim Machine Learning is a project focused on providing animations and visualizations of common machine learning concepts with the [Manim Community Library](https://www.manim.community/). We want this project to be a compilation of primitive visualizations that can be easily combined to create videos about complex machine learning concepts. Additionally, we want to provide a set of abstractions which allow users to focus on explanations instead of software engineering.

## Table of Contents

1. [Getting Started](#getting-started)
2. [Examples](#examples)

## Getting Started 
First you will want to [install manim](https://docs.manim.community/en/stable/installation.html). Then you can run the following to generate the example videos. 

`manim -pqh src/vae.py VAEScene`

## Examples

Checkout the ```examples``` directory for some example videos with source code. 

### Variational Autoencoders

This is a visualization of a Variational Autoencoder. 

<img src="examples/VAEScene.gif" width="600">

### VAE Disentanglement 

This is a visualization of disentanglement with a Variational Autoencoder

<img src="examples/DisentanglementScene.gif" width="600">

### Neural Networks

This is a visualization of a Neural Network. 

<img src="examples/TestNeuralNetworkScene.gif" width="600">