The Autocorrelation Function


The purpose of this tutorial is to show a simple technique to estimate periodicity in time series, called autocorrelation.

This tutorial is part of a longer series that focuses on how to analyse time series.

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Time Series Decomposition


This tutorial will teach you how you can extract valuable information from time series, such as your sold copies on Steam or your Google Analytics. The previous part of this series introduced a technique called moving average, which has been used to attenuate the effects of noise in a signal. When signals represent an event that evolves over time, we are in front of a time series. Classical decomposition is a technique that attempts to find the main trends within time series.

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An Introduction to Signal Smoothing


Noise is everywhere. Whether you’re sampling accelerometer data for a mobile game or trying to measure the temperature of a room, noise will be there. Even if you could remove all the noise from an input device, you’ll still have a certain degree of uncertainty. If a player has tapped on the screen, where did they really wanted to tap? All these scenarios forces to re-think about how we gather and preprocess data.

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Understanding Deep Dreams

understanding deep dreams

In the last few months the Internet has been flooded with deep dreams: images augmented by neural networks which look incredibly trippy. Deep dreams have the potential to become the new fractals; beautifully backgrounds everyone knows are related to Maths, but no one knows really how. What are deep dreams, how are they generated and what can they teach us?

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