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→Machine Learning (TensorFlow)
= Use Case Implementation =
== Machine Learning (TensorFlow) ==
The following models have been tested on a CUDA-enabled GPU, versus an Apple Neural Engine from an M1. The Python TensorFlow implementation abstracts away from hardware and allows the same code to be ran on various devices such as these. The code for these models reside in the [https://github.com/tensorflow/models/ TensorFlow Model Garden].
TensorFlow Machine Learning Algorithms Tested:
[https://github.com/tensorflow/models/tree/master/official/nlp/bert BERT (Bidirectional Encoder Representations from Transformers) Models Test]
[https://github.com/tensorflow/models/tree/master/orbit Orbit Standard Runner Test]
[https://github.com/tensorflow/models/tree/238922e98dd0e8254b5c0921b241a1f5a151782f/official/vision/image_classification Image Classifier Trainer Util Test]
[[File:NVIDIA-Apple.png|500px]]
“NVIDIA's GTX 1080 does around 8.9 teraflops” [https://www.ign.com/articles/2019/03/19/what-are-teraflops-and-what-do-they-tell-us-about-googles-stadia-performance Source]
