Doing some data crunching. Graph: daily number of new StackOverflow questions that either contain one of {"keras", "tensorflow"} in their tags or titles, or one of {"torch", "pytorch", "torchvision"}. Past ~6 months.pic.twitter.com/H5uRT9NHYj
Deep learning @google. Creator of Keras. Author of 'Deep Learning with Python'. Opinions are my own.
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Doing some data crunching. Graph: daily number of new StackOverflow questions that either contain one of {"keras", "tensorflow"} in their tags or titles, or one of {"torch", "pytorch", "torchvision"}. Past ~6 months.pic.twitter.com/H5uRT9NHYj
75 questions per day on average -- you could employ a few people full time just answering them
Daily PyPI downloads (EMA). Labeled this time.pic.twitter.com/WhvE1B2PYr
Now here's a really fascinating one. Weekly number of research papers indexed on arXiv specifically, and on all of Google Scholar (which includes arXiv), that mention TensorFlow, Keras, or PyTorch.pic.twitter.com/oVBsukcqXg
This tells us two things: 1) The pure ML research community (posts on arXiv) is evenly split between TF and PyTorch 2) The broader scientific community interested in applying ML (doesn't post on arXiv; often doesn't even publish preprints), which is ~20x larger, uses TF/Keras
I am happy to see these results but I would refrain from including SO questions. Why? Because it doesn't differentiate between:
1. Unable to understand the api usage?
2. v1-> v2 migration problem?
3. Something confusing/buggy?
Saying it again: We should have official forums 
Every data source is biased in some way. Which is why we look at many sources to see the real picture
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