algorithm

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Do not overlearn your data too much, learn to generalize - Part 3

April 28, 2019

This post goes through the hyperparameter optimization for model selection, via cross-validation, and the regularization technique. The former topic is more recently referred to as **meta-learning**.

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Do not overlearn your data too much, learn to generalize - Part 2

April 21, 2019

This post aims at visualizing the bias-variance dilemma, understanding how the model capacity relates to its performance and why it is common practice to split the dataset into training and testing, creating some learning curves that should clarify whether gathering additional data might be worthy.

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Do not overlearn your data too much, learn to generalize - Part 1

April 14, 2019

This post defines the bias and variance concepts and apply it to both a linear and a non-linear model.

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Learning to classify coffee from cappuccino - Part 8

April 7, 2019

This post implements a logistic regression in Scikit-learn for the MNIST dataset.

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Learning to classify coffee from cappuccino - Part 7

March 31, 2019

This post implements a logistic regression in Scikit-learn for the digits dataset.