jm + deep-learning + tensorflow   1

How a Japanese cucumber farmer is using deep learning and TensorFlow
Unfortunately the usual ML problem arises at the end:
One of the current challenges with deep learning is that you need to have a large number of training datasets. To train the model, Makoto spent about three months taking 7,000 pictures of cucumbers sorted by his mother, but it’s probably not enough. "When I did a validation with the test images, the recognition accuracy exceeded 95%. But if you apply the system with real use cases, the accuracy drops down to about 70%. I suspect the neural network model has the issue of "overfitting" (the phenomenon in neural network where the model is trained to fit only to the small training dataset) because of the insufficient number of training images."

In other words, as with ML since we were using it in SpamAssassin, maintaining the training corpus becomes a really big problem. :(
google  machine-learning  tensorflow  cucumbers  deep-learning  ml 
september 2016 by jm

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