This Jupyter Notebook is a small experiment I did to solidify my knowlege of gradient descent and curve-fitting. It follows the example for overfitting and underfitting in Bishop (2006) Pattern Recognition and Machine Learning by learning the coefficients of a polynomial using data points generated by a sine wave with some added Gaussian noise.
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Experiments on overfitting and underfitting.
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cbonitz/overfitting-underfitting
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