Four shorter backlinks: 3 Oct 2017

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Truth is Serious, TensorFlow in Prod, Dashboard, and Occasion Detection from Wikipedia

  1. We Are Not in a Simulation (Cosmos Magazine) — Ringel and Kovrizhi showed that makes an attempt to use quantum Monte Carlo to model devices exhibiting anomalies, this sort of as the quantum Hall outcome, will constantly turn out to be unworkable. They uncovered that the complexity of the simulation elevated exponentially with the selection of particles currently being simulated. If the complexity grew linearly with the selection of particles currently being simulated, then doubling the selection of partices would necessarily mean doubling the computing electricity expected. If, on the other hand, the complexity grows on an exponential scale—where the amount of money of computing electricity has to double each individual time a single particle is added—then the process swiftly gets unattainable. Whew, I can ultimately slumber at night time. (by means of Slashdot)
  2. TFX: A TensorFlow-primarily based Generation-Scale Device Mastering System — greatest description is from The Morning Paper. The new baseline: so far, you have embraced automated testing, constant integration, constant shipping, most likely constant deployment, and you have the sophistication to rollout new changes in a gradual way, monitor behaviour, and quit or rollback when a problem is detected. On leading of this, you have place in position a advanced metrics procedure and a constant experimentation platform. Thanks to the increasing complexity of devices, you may also have to have to prolong this to a normal purpose black-box optimization platform. But you are even now not performed however! All those device learning versions you have been optimizing have to have to be qualified, validated, and served someway. You have to have a device learning platform. That’s the subject matter of today’s paper preference, which describes the device learning platform inside of Google, TFX.
  3. redash — GPLv3 dashboard, connects to RedShift, ElasticSearch, BigQuery, MongoDB, MySQL, PostgreSQL.
  4. Wikipedia Graph Mining: Dynamic Framework of Collective Memory — they use the transforming recognition of web pages to recognize significant gatherings, even separating predictable gatherings like tournaments from unpredictable kinds like tragedies.

Carry on reading Four shorter backlinks: 3 Oct 2017.



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