Many supervised learning tasks follow a fairly standardized workflow.
Step-1) Define the problem and assemble a dataset
Step-2) Define a model which can 'capture' the trends in the data.
Step-3) Divide your data-set into "training" and "validation" sets (to monitor over-fitting)
Step-4) Prepare your data: This is typically done by normalizing the data, which makes it easier to fit.
Step-5) Choose a measure of success to gauge the "quality" of a given parameterization of the model
For curve fitting the "success" often means finding a model with a low error metric such as mean square error (MSE)
Step-6) "Train" a model (numerical optimization)
For parametric models, training" means doing a "multi-variable" optimization problem to find the model parameters that "best fit" the data
This is done by finding the "best fit" model parameters which minimize some error metric, such as mean square error.
If your model is complex enough to over-fit the data, then you will need to regularize the model to prevent over fitting
This last bullet isn't applicable for simple models, however, they become vary important for more complex models such as neural networks.
Step-7) Use your trained model to make predictions for "new" unseen data points
This work-flow is often true for both classification and regression problems.
The reason we are showing you this is that the workflow maps perfectly onto training many more complicated models, such as neural networks.
If you understand the workflow below, then you are well on your way to learning how to "train" neural networks, ... more on this in other DSAN classes
In this lab we go through this workflow for a simple low dimensional non-linear curve fitting example.
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(1) A PDF (or HTML) of the completed form of this notebook
The final uploaded version should NOT have any code-errors present
All outputs must be visible in the uploaded version, including code-cell outputs, images, graphs, etc
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