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gradle/verification-keyring.keys
EphGnOh5rXXIu6NSoDWatoOJHQPoS8v66tq9rpzH0+Sq0BVVZqbvDMg3oU55SGa4 x3z7qZBO18KGudoOyk7XVshLTCYYWHEYzHQKafxO/+CLWl1M5JltFDA3vgIkA2u/ BLm8oBKXXuvDPpqTTbzF9pDG8RewkcpaenFZLu37gbW7j/aLkg/MIEWtlQa+ArtA 0exi44pzGoDQgpoHgjN52Qq/o9PIYvwtdn24pcKxZBy5VcvkBEqae/TYSGnnBfVf aNqBDS14YrHbPFlNHZxu3Zq72QauPeHxnLKgz8scdFa9Wyx0iXB4N54edEbsFuws 7mHPnNDClw3uCrM7scTHorZIV3ooHkCOfURQ+zoAzYb+Fm9lbc5xKcn/5EbLBwLH
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Mon Apr 01 11:46:17 UTC 2024 - 525.2K bytes - Viewed (0) -
src/testdata/Isaac.Newton-Opticks.txt
again, to see if I could make it better than that which I kept. And thus by many Trials I learn'd the way of polishing, till I made those two reflecting Perspectives I spake of above. For this Art of polishing will be better learn'd by repeated Practice than by my Description. Before I ground the Object-Metal on the Pitch, I always ground the Putty on it
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Mon Oct 01 16:16:21 UTC 2018 - 553.9K bytes - Viewed (0) -
okhttp-idna-mapping-table/src/main/resources/okhttp3/internal/idna/IdnaMappingTable.txt
1F933..1F93E ; valid ; ; NV8 # 9.0 SELFIE..HANDBALL 1F93F ; valid ; ; NV8 # 12.0 DIVING MASK 1F940..1F94B ; valid ; ; NV8 # 9.0 WILTED FLOWER..MARTIAL ARTS UNIFORM 1F94C ; valid ; ; NV8 # 10.0 CURLING STONE 1F94D..1F94F ; valid ; ; NV8 # 11.0 LACROSSE STICK AND BALL..FLYING DISC
Registered: Sun Jun 16 04:42:17 UTC 2024 - Last Modified: Sat Feb 10 11:25:47 UTC 2024 - 854.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td
Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform random variables. See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer Programming, Volume 2. Addison Wesley }]; let arguments = (ins Arg<TF_I32OrI64Tensor, [{1-D integer tensor. Shape of independent samples to draw from each
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 793K bytes - Viewed (0) -
RELEASE.md
* `tf.keras`: * `Model.fit` major improvements: * You can now use custom training logic with `Model.fit` by overriding `Model.train_step`. * Easily write state-of-the-art training loops without worrying about all of the features `Model.fit` handles for you (distribution strategies, callbacks, data formats, looping logic, etc) * See the default
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 23:24:08 UTC 2024 - 730.3K bytes - Viewed (0)