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Comment by Ayyar | Hacker News Reader

This is Deep Learning 101 level material, rather than an advanced insider technique.

It's even listed in the Tensorflow tutorials: https://codelabs.developers.google.com/codelabs/tensorflow-f...

While I agree that you could build a demo in Deep Learning 101 that could work for some small set of examples, I disagree that this is 101 level material.

Facebook just released: https://techcrunch.com/2017/02/02/facebooks-ai-unlocks-the-a...

You could also call this Deep Learning 101. But it really isn't because building a usable platform that works at scale actually delivers performance and solves problems is a lot tougher than what can be taught in an intro to Deep Learning 101 course.

Just poked around a bit with your API, and the learning with just 25 samples is impressive! And the getting training samples from the web is a great touch. But that 25 sample number seems too low for classes that are "closer" together? How do you quantify if you have done a good job on training or if you need some varied samples?

Thanks for trying us out! Internally we have validation metrics which have a number that says how good a model is at class separation. One naive way to do this entropy (-plogp). We are planning on exposing this to users soon. So once you create a model you'll receive feedback as to how "good" we think it is. In case it's not working well we might ask you for more data (we hope we don't need to do this too frequently)

Transfer learning is fairly well studied. However we have seen lot of companies facing problems that can be solved using this technique but can't because of lack of knowledge, pretrained models availability, engineering challenges involved. We are just trying to make the process easier for them.

Another great example is the last post on the keras blog [1] "Using pre-trained word embeddings in a Keras model". You take advantage of a large pre-trained network for a text classification task.

The OpenFace face recognition library also offers this technique. You take advantage of their large pre-trained network for face embedding: transforming a face into features distinct enough for classification. You then train another few layers for recognizing your own samples.

1: https://blog.keras.io/using-pre-trained-word-embeddings-in-a...

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