re formatting the api to contain the image bytes in json

fixing typos

think I might have to programatically create the path for the images ...

think I was right??

think I have to specify the full path??

instead of returning the image bytes to the api user, we are moving towards hosting them on the backend but we need to be careful moving forwards and make sure that we delete the images after use

reverting more code back to python 3 syntax

having to retract to pyhton 3.7 syntax for render

getting rid of some more problematic type hints

clustering plot for kmeans

making better loss history / epoch plot

savving memory with close fig

switching to png

think that the way we had teh plot function setup may have been causing some problems with buffers in the cloud

believe to have created a function to build a svg plot and send it over the network

working on kmeans

cleaning up readme, now moving on to building clustering algorithms from scratch

fixed dimensions issue with the iris dataset

having some issues with backprop when trying to use the iris dataset

fixing train test split issue with iris dataset

trying to move forwards with the iris dataset

render did not seem to like my type hints in the logs

successfully returning data on how the neural network performed

updating requirements.txt and cleaning things up. Thought it would be working but for some reason requests from postman are not making it to the api

working with serializing the model with to_dict()

produced a solution to the problem of funcions not being accessed

cutting the front end mess and just using this as a backend api

splitting up some backprop functions that may have been too long

working on making it just a flask app, got rid of the main.py stuff

getting rid of lambda funcs

configured the run_dev shell script

added baseline stuff for making this a flask app with tailwind

we should try to do this with regression on a simple dataset from the internet somewhere because I think it is hard to see how well it is doing with completley random data

added animated plot of loss history

plot functionality'

computing loss history over time

got it working! now I want to make it so that we can have more than just one node in the middle

returning dictionary of computations after each epoch to try to measure accuracy

making more modular so we can develop our other methods too

believe to be performing backprop with the weights without error

better organization for generalization once we add other methods and such

just focusing on neural_network first

working on setting up the neural_network functionality