Introduction The problem of classification consists in assigning an observation to the category it belongs. That means, for instance, taking a picture of a handwritten digit and correctly classifying which digit (0-9) it is, matching pictures of faces to whom they belong or classifying the sentiment in a text. In general, when dealing with classification we use supervised learning (when we have an annotated training set from which we can learn our model - as we did up until this point).

Introduction TensorFlowâ„¢ is an open source machine learning library for Python (and C++) initially developed by the Google Brain Team for research and released under the Apache 2.0 Open Source License on November 9th, 2015 . It is currently used by Google in their speech recognition, Gmail, Google Photos, Search services and recently adopted by the DeepMind team .
Features Though TensorFlow was built with deep learning in mind, its framework is general enough so that we can also implement clustering methods, graphical models, optimization problems and others.

This post is a continuation of Linear Regression.
Introduction In multiple linear regression we extend the notion developed in linear regression to use multiple descriptive values in order to estimate the dependent variable, which effectively allows us to write more complex functions such as higher order polynomials ($y = \sum_{i_0}^{k} w_ix^i$), sinusoids ($y = w_1 sin(x) + w_2 cos(x)$) or a mix of functions ($y = w_1 sin(x_1) + w_2 cos(x_2) + x_1x_2$).

Introduction Linear regression is a method used to find a relationship between a dependent variable and a set of independent variables. In its simplest form it consist of fitting a function $ \boldsymbol{y} = w.\boldsymbol{x}+b $ to observed data, where $\boldsymbol{y}$ is the dependent variable, $\boldsymbol{x}$ the independent, $w$ the weight matrix and $b$ the bias.
Illustratively, performing linear regression is the same as fitting a scatter plot to a line.