jm + tensorflow   3

Tensorflow whinge
"Built to do really fancy cutting-edge stuff and also to make common workflows look very easy, but without a middle ground, so either you are doing something very ordinary and your code is 2 lines that magically work, or you’re lost in cryptic error messages coming from mysterious middleware objects that, you learn 5 hours later, exist so the code can run on a steam-powered deep-sea quantum computer cluster or something"

This seems to be a bit of a Google trait. (via Alison Parrish)
tensorflow  api  open-source  snark  whinges 
9 weeks ago by jm
The engineering team behind streaming music service Deezer just open-sourced Spleeter, their audio separation library built on Python and TensorFlow that uses machine learning to quickly and freely separate music into stems.

The results, just using the pretrained models, are frankly incredible. Gonna be a lot of random mashups and remixes using this....
audio  music  spleeter  deezer  tensorflow  python  cool  hacks  machine-learning 
november 2019 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

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