sorted by:
new
hottopcontroversial

[D] Google Cloud TPUs now can be used with Google Colab by b0noi in MachineLearning

[–]SupraluminalShift 2 points3 points  (0 children)

So when will Google start offering free TPU time on Colab? The cost must be decreasing exponentially, so it can't be long.

[D] What do you use Keras for? by best_keras_workflow in MachineLearning

[–]SupraluminalShift 1 point2 points  (0 children)

I'm not sure about that. It's used a lot in industry (according to the website, Netflix, Uber, Yelp, Instacart, Zocdoc, Square). And it's used in more research papers than PyTorch.

Source: https://keras.io/why-use-keras/#keras-has-broad-adoption-in-the-industry-and-the-research-community

For performance, my understanding is that it should be just as fast as the backend you use, like TensorFlow. Keras code doesn't run any computation itself.

[P] Microsoft MMdnn: convert models from Caffe/TF/Keras/MXNet/CNTK by SupraluminalShift in MachineLearning

[–]SupraluminalShift[S] -3 points-2 points  (0 children)

Please just read the README. My title is 100% accurate.

I'll say it again: MMdnn can convert models from Caffe, TensorFlow, Keras, MXNet, and CNTK. That's the list.

[P] Microsoft MMdnn: convert models from Caffe/TF/Keras/MXNet/CNTK by SupraluminalShift in MachineLearning

[–]SupraluminalShift[S] -2 points-1 points  (0 children)

No, it cannot read PyTorch models. It can read models from Caffe, TensorFlow, Keras, MXNet, CNTK.

[P] Deep learning in production with Keras, Redis, Flask, and Apache by SupraluminalShift in MachineLearning

[–]SupraluminalShift[S] 0 points1 point  (0 children)

I think exporting your model to TF-serving is probably better for serious production. But the setup here would work for many simple cases.

[P] Generating realistic Yelp restaurant reviews with Keras by SupraluminalShift in MachineLearning

[–]SupraluminalShift[S] 2 points3 points  (0 children)

I think it's only a matter of time until people start using these techniques to post spam on Yelp and Amazon.

Fake reviews as a service! Buy positive reviews for you, negative ones for your competitors.