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Eugene T (E.T.)
243 followers -
Adventurous software developer
Adventurous software developer

243 followers
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🏃‍♂️ Fitness App Identity Sketches
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Sentiment Analysis with PySpark

https://search.app.goo.gl/SFSh

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Developers love trendy new languages but earn more with functional programming

https://search.app.goo.gl/NCjw

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A wizard with a stylus, this guy finally started filling an #Artstation portfolio.
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Just look how amazing this looks!. You need a good bender for this recipe (good enough to turn cashews and water into cream with no lumps), otherwise, try substituting the cashew cream for something else. Perfect for a late winter meal. Enjoy.
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Benchmarking Google’s new TPUv2

For most of us, deep learning still happens on Nvidia GPUs. There is currently no alternative with practical relevance. Google’s Tensor Processing Unit (TPU), a custom-developed chip for deep learning, promises to change that. Nine months after the initial announcement, Google last week finally released TPUv2 to early beta users on the Google Cloud Platform. At RiseML, we got our hands on them and ran a couple of quick benchmarks. Below, we’d like to share our experience and preliminary results. More competition in the market for deep learning hardware has been long sought after and has the potential of breaking up Nvidia’s monopoly on hardware for deep learning. Along with that, this will define what the deep learning infrastructure of the future will look like. Keep in mind that TPUs are still in early beta — as unmistakingly communicated by Google in many places — so some of the things we discuss might change in the future....Conclusion: On the models we tested, TPUs compare very well, both, performance-wise and economically, to the latest generations of GPUs. This stands in contrast to previous reports. Overall, the experience of using TPUs and adapting TensorFlow code is already pretty good for a beta. We think that once TPUs are available to a larger audience, they could become a real alternative to Nvidia GPUs.
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