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Deep Learning DIY

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I recently lead a 1 hour workshop at FOSSASIA 2017 in Singapore.

Similarly to the previous year, this workshop was hands-on : After a brief background on deep learning, participants started quickly, getting an understanding of features (and feature creation) by a 'blame assignment' algorithm (aka Backprop).

For this FOSSASIA workshop, I also created a stand-alone CNN filter demo, so that people could get an intuitive understanding of what a single 3x3 CNN filter can do.

Then, using the VirtualBox VM-on-a-stick, we had a quick look at a working GoogLeNet model. And then went on to a longer segment on how the CNN mechanism can be applied to Speech Recognition - a new application/module I put together specifically for FOSSASIA (in the preceeding 5 days, from concept to working & demonstrable code).

Naturally, this being a FOSS event, all the source is available on GitHub - if you have questions on the software, please leave an 'issue' there.

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If there are any questions about the presentation please ask below, or contact me using the details given on the slides themselves.

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The presentation was kindly recorded by Engineers.sg.