I recently lead a 92 minute workshop at PyCon SG 2016 in Singapore.

This workshop was hands-on : After a brief background on deep learning, participants started quickly, interacting with offline experiments with a ConvNet.js model, and also the TensorFlow Playground.

But this on-line portion was partly to allow everyone enough time to get a ~1Gb VirtualBox “appliance” created for the event installed on their laptops. Fortunately, over 90% of the people who came already had VirtualBox installed, which was a huge relief.

Once everyone was up-to-speed tools-wise, the workshop then progressed through a series of Jupyter (fka iPython) notebooks, ranging from Theano basics, through MNIST, to ImageNet networks (pretrained models of both GoogLeNet and Inception-v3 were included in the VM).

Then, for the last half-hour, we went over an interesting application : Reinforcement Learning applied to the game “Bubble Breaker”. This application, built from the ground up for PyCon, illustrates how simple it is to get Deep-Q-Learning working - with a ‘small board’ version being trainable in ~2-3 minutes on participants’ laptops. A pretrained full-size model was also included in the VM, which now outperforms its creator…

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.

PS: And if you liked the Workshop, please ‘star’ the Deep Learning Workshop repo :: Star

Presentation Screenshot

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

Presentation Content Example

And there’s also a video of the presentation available on YouTube, which was done very professionally, however I can’t vouch for its usefulness since I can’t bear to watch myself…

Martin Andrews

{Finance, Software, AI} entrepreneur, living in Singapore with my family.

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