Understanding Neural Networks

A few months ago I made a presentation on Tensor Flow for MBA students. The challenge was to simplify the technical underpinnings of the technology sufficiently while preserving a semblance of what it was good for. One particular challenge was visualizing the technology. Showing python code and output was out of the question and at the time TensorBoard was not up to the challenge.

This week I was pleasantly surprised to find A Neural Network Playground. It is a demo of a Neural Network built on top of Tensor Flow. The underlying mechanism is completely abstracted away, so it is more about understanding how a neural network works and less about Tensor Flow.

The playground allows you to modify parameters of the neural network and observe the outcomes. You can select one of four different data sources. Adjust the noise and number of observations in the data. Split it into training and testing datasets. And alter the inputs into the model.

All in all, I think it is a great tool to observe the effects of various parameters we have to deal with every day on the results of the models we fit to the data.

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