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Posted on December 20, 2018 at 9:30pm 0 Comments 0 Likes
In a previous blog-post we have seen how we can use Signal Processing techniques for the classification of time-series and signals.
A very short summary of that post is: We can use the Fourier Transform to transform a signal from its time-domain to its frequency domain. The peaks in the frequency spectrum indicate the most…
ContinuePosted on July 5, 2018 at 11:48am 0 Comments 0 Likes
Recurrent Neural Nets (RNN) detect features in sequential data (e.g. time-series data). Examples of applications which can be made using RNN’s are anomaly detection in time-series data, classification of ECG and …
ContinuePosted on April 12, 2018 at 6:00am 0 Comments 1 Like
Stochastic Signal Analysis is a field of science concerned with the processing, modification and analysis of (stochastic) signals.
Anyone with a background in Physics or Engineering knows to some degree about signal analysis techniques, what these technique are and how they can be used to analyze, model and classify signals.
Data Scientists coming from a different fields, like Computer Science or Statistics, might not be aware of the analytical power these techniques bring with…
ContinuePosted on December 4, 2017 at 5:00am 0 Comments 3 Likes
In a previous blog post we have seen how to build Convolutional Neural Networks (CNN) in Tensorflow, by building various CNN architectures (like LeNet5, AlexNet, VGGNet-16) from scratch and training them on the MNIST, CIFAR-10 and Oxflower17 datasets.
It starts to get interesting when you start thinking about the practical applications of CNN and other Deep Learning methods. If you have been following the latest technical developments you probably know that CNN’s are…
ContinuePosted 12 April 2021
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