The Conference for Machine Learning Innovation

Deep Learning advances for Signal Processing

This talk originates from the archive. To the CURRENT program
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Wednesday, June 19 2019
09:00 - 10:00

Deep neural networks are becoming irreplaceable for analyzing most kinds of data that humans supposed to exceed in – images, video, sounds, texts. Meanwhile, we are forgetting about another very important source of data: signals or time series. They may get less hype in public, but benefit a lot from applying deep learning comparing to classical approaches, especially for IoT. 

In this talk, we will review the sources of time series, what business goals are we solving while analyzing them, what are “old” tools for analysis, and how deep neural nets overcome them. We will learn the latest trends as well as bust some myths. Moreover, we will see how generative models can be applied to the signal processing as well. After this talk, you’ll be able to boost your current solutions in signal processing or time series analysis with deep learning. It will be also interesting for practitioners in other areas like computer vision or NLP, since we will discuss some concepts that are widely applicable. 

Previous experience with time series is not required, but some theoretical or practical understanding of machine learning and/or neural networks is preferred.

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