At Google, tf.keras (the internal TensorFlow implementation of the Keras spec) is made by 8 awesome engineers (come work with us!), and many more people contribute to it. And more great folks are working on the Keras TypeScript implementation as part of TensorFlow.js.
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And then there are many more people elsewhere working on 3rd-party Keras implementations: the Keras-MXNet team at AWS, the Scala implementation from SkyMind, PlaidML, CoreML... As for me, I mostly do code reviews and I participate in design discussions.
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Another misconception I see is that I have a leadership position inside TensorFlow. I do not. I don’t actually make any important decisions with regard to TF, and never have. That’s the role of the TF leads, 5 people, of which I am not one.
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In general, the TF team is quite large, with over 100 people (I think?), and none of them report to me. My scope is very narrow: looking at API changes within tf.keras, reviewing tf.keras code. That's really it.
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One last misconception I see is that Keras is a TensorFlow wrapper library. In fact, Keras is an API standard for defining & training deep learning models, similar to what Gluon is to MXNet. Like Gluon, it's meant not to be tied to a specific implementation.
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Keras has implementations in TensorFlow, MXNet, TypeScript, JavaScript, CNTK, Theano, PlaidML, Scala, CoreML, and others I think? But I have only been involved with tf.keras and the TensorFlow/Theano implementation.
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Again, my scope when it comes to TensorFlow & Keras is very narrow. I’m just doing code review, looking at the tf.keras API. Sometimes I code. I do very little compared to everyone else, and hopefully I'll do even less in the future. I don’t enjoy the harassment on social media.
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We’ll said and lot of honesty in these words. Thx for beginning the great history of keras.
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Naah man. You the OG.
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