Using the updated VirtualBox VM-on-a-stick, we had a quick look at a working NASNet model. And then went on showing how transfer learning can work. That essentially covered the first two sections of the talk : Learning with lots of data; and Learning with some data.
For the second half of this FOSSASIA workshop, I introduced meta-learning, with the emphasis on one-shot learning, to show how models can be learned from very little data. This topic was prompted by the publication (3 weeks earlier) of the OpenAI “Reptile” paper, and the VM included an updated version of the reptile-sines code.
I also (re-)created a stand-alone Reptile one-shot learning demo, so that people could get an intuitive understanding of what a test example for the meta-learning task look like, without being connected to the internet (and without needing the VirtualBox VM running).
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.
If there are any questions about the presentation please ask below, or contact me using the details given on the slides themselves.
The presentation was kindly recorded by Engineers.sg.
- Presentation (49)
- ImageNet (4)
- TransferLearning (5)
- MetaLearning (3)
- OneShotLearning (3)
- Reptile (3)
- FOSSASIA (2)
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