jm + deep-learning   3

When DNNs go wrong – adversarial examples and what we can learn from them
Excellent paper.
[The] results suggest that classifiers based on modern machine learning techniques, even those that obtain excellent performance on the test set, are not learning the true underlying concepts that determine the correct output label. Instead, these algorithms have built a Potemkin village that works well on naturally occuring data, but is exposed as a fake when one visits points in space that do not have high probability in the data distribution.
ai  deep-learning  dnns  neural-networks  adversarial-classification  classification  classifiers  machine-learning  papers 
23 days ago by jm
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
Fast Forward Labs: Fashion Goes Deep: Data Science at Lyst
this is more than just data science really -- this is proper machine learning, with deep learning and a convolutional neural network. serious business
lyst  machine-learning  data-science  ml  neural-networks  supervised-learning  unsupervised-learning  deep-learning 
december 2015 by jm

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