Update requirements.txt

changing the example to what is actually in the example

Merge pull request #2 from Jensen-holm/dev

making the example code cleaner

loss functions now just have staticmethods

adding unit tests for activation functions (using pytest)

adding example documentation

format

adding example program 'example.py'

found the bug in setup.py that was stopping me from getting the package

readme updates

pypi package works!

fixed bug where I forgot to change the name from nn to numpyneuron

stuck on getting this package to work on pypi

python package not really working, but it is all set up okay I think

better about and readme docs

Merge pull request #1 from Jensen-holm/package

making it a python package on PyPI

Moving towards making this a python package for simple neural network

getting rid of regression tab, thinking about having a custom data tab

fixing bug that happened with a type that I made, I think huggingface

features added:

adding Sigmoid as an option for output layer activation

changing name to Numpy-Neuron

getting rid of unessesary imports

latest update, finishing classification with MNIST! More details on

switching to gradio and a complete rewrite that I did over in the

fixed bug that returned same number twice in response

also returning accuracy score from test data from the api

ready for prod

working on readme and adding ml vis submodule

adding accuracy score tracking

adding CORS support

added loss history in the return to the user so they can make graphs and

must specify werkzwug for flask to work on render

forgot to add gunicorn

ignore and removing dockerfile

adding dockerfile

adding accuracy score metric

moving on to testing other datasets!

currently debugging mismatched shape error when computing weights and

init weights and biases, and getting through epochs

init weights and biases

successful example

handling errors well with the neural netork api

working on new python implementation with cleaner code

initial dumb api working

stuck on working with computeOutput function, getting dim error every

mapping activation arg to the actual function

initializing weights and biases