semvec/pypi-packages huggingface dataset:
nbtex 1.0.7
A light-weight wrapper around LaTeX for mathematical symbols for Jupyter Notebooks

# nbTeX  

A light-weight pythonic wrapper around LaTeX specifically to python-like interface to write mathemical latex in Jupyter Notebooks.   
**NOTE:** Only Jupyter Notebooks are supported. Tests done in notebooks too  

## Motivation

A lot of us in data science extensively use notebooks and sometimes have to write some latex in markdown. Most of us are not familiar with LaTeX. Therefore instead of going on a hunt for LaTeX on the internet, you can use this library for writing LaTeX in a pythonic manner.   
**NOTE:** We only support Math LaTeX because Jupyter Notebook users normally do other stuff in normal markdown and not in LaTeX.

## Features

You can create latex for:

+ `+`: Adding
+ `-`: Subtraction
+ `*`: Multiplication
+ `pow`: Exponents
+ `^`: Exponents (another way)
+ `/`: Fractions
+ `|`: Space in generated latex
+ `()`: Function calling with arbitrary args
+ `<`, `>`, `<=`, `>=`: Ordering operators
+ `==`, `!=`: Equal and Not Equal
+ `~`: Inversion of the topmost operator in the expression (eg: ~(x*y) will give LaTeX for division/fraction)
+ Matrices
+ Differentiation
+ Partial Differentiation
+ Integration
+ Integration over partials
+ Summation
+ Product
+ Vector notation

## Usage  

The usage is best presented by examples  
Look at the [docs](https://github.com/narang99/nbtex/tree/master/docs)
edutorch 0.0.3
Rewritten PyTorch framework designed to help you learn AI/ML
Requires Python: >=3.7

# edutorch
Rewritten PyTorch framework designed to help you learn AI/ML!

[![Python 3.7+](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/release/python-370/)
[![Build Status](https://travis-ci.com/TylerYep/edutorch.svg?branch=master)](https://travis-ci.com/TylerYep/edutorch)
[![GitHub license](https://img.shields.io/github/license/TylerYep/edutorch)](https://github.com/TylerYep/edutorch/blob/master/LICENSE)
[![codecov](https://codecov.io/gh/TylerYep/edutorch/branch/master/graph/badge.svg)](https://codecov.io/gh/TylerYep/edutorch)
[![Downloads](https://pepy.tech/badge/edutorch)](https://pepy.tech/project/edutorch)

PyTorch is one of the most amazing frameworks for building and training deep neural networks. One of its biggest strengths is providing an intuitive and extendable interface for building and training these models.

In this project, I provide my own version of the PyTorch framework, designed to help you understand the key concepts. The goal is to provide explicit implementations of popular layers, models, and optimizers. Above all else, this code is designed to be readable and clear. Many of these examples are modified from Stanford's CS 230 / 231N course materials available online.

## EduTorch vs PyTorch
One notable difference between EduTorch and PyTorch is that EduTorch _does NOT provide autograd_. There are many educational benefits to deriving/implementing the backprop step yourself, and if you want automatic gradient calculations, you are better off using the real framework. Additionally, if you wanted to learn how the autograd system is implemented, you can check out Andrej Karpathy's [micrograd project](https://github.com/karpathy/micrograd).

There is no CUDA or GPU support either, for the same reasons.

# Contributing
All issues and pull requests are much appreciated!
- First, be sure to run `scripts/install-hooks`.
- To run all tests and use auto-formatting tools, check out `scripts/run-tests`.
- To only run unit tests, run `pytest`.