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Francois Chollet is leaving Google

(developers.googleblog.com)
377 points xnx | 1 comments | | HN request time: 0.201s | source
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osm3000 ◴[] No.42132316[source]
I loved Keras at the beginning of my PhD, 2017. But it was just the wrong abstraction: too easy to start with, too difficult to create custom things (e.g., custom loss function).

I really tried to understand TensorFlow, I managed to make a for-loop in a week. Nested for-loop proved to be impossible.

PyTorch was just perfect out of the box. I don't think I would have finished my PhD in time if it wasn't for PyTorch.

I loved Keras. It was an important milestone, and it made me believe deep learning is feasible. It was just...not the final thing.

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hooloovoo_zoo ◴[] No.42132756[source]
Keras was a miracle coming from writing stuff in Theano back in the day though.
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1. braza ◴[] No.42136619[source]
Of course, it's easy to be ideological and defend technology A or B nowadays, but I agree 100% that in 2016/2016 Keras was the first touchpoint of several people and companies with Deep Learning.

The ecosystem, roughly speaking was: * Theano: Verbosity nightmare * Torch: Not-user friendly * Lasagne: A complex attraction on top of Theano. * Caffe: No flexibility at all, anything not the traditional architectures would be hard to implement * Tensor Flow: Unnecessarily complex API and no debuggability

I do not say that Keras solved all those things right away, but honestly, until just the fact that you could implement some Deep Learning architecture in 2017 on top of Keras I believe was one of the critical moments in Deep Learning history.

Of course today people have different preferences and I understand why PyTorch had its leap, but Keras was in my opinion the best piece of software back in the day to work with Deep Learning.