Explore how (stochastic) gradient descent works on a simple linear regression. I built this demo to help me better understand backpropagation, by keeping an eye on the values of the weights as they get updated.
I've also added step-by-step remarks and graph plots to the values of the weights and the loss function.
Things you can play around with: optimiser, learning rate, variable initialiser, loss function, batch size, no. of epochs
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[ 4.0 ms ] story [ 20.2 ms ] threadI've also added step-by-step remarks and graph plots to the values of the weights and the loss function.
Things you can play around with: optimiser, learning rate, variable initialiser, loss function, batch size, no. of epochs
JavaScript libraries used: Dagre-D3 (GraphViz + d3), MathJax, ApexCharts, jQuery
Any comments to this demo are welcome!
You might want to check out my repo https://github.com/raibosome/raibosome.github.io/tree/master... It's a little messy oops.