Transfer Learning on EfficientDet
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Transfer Learning is an effiicent way to train an existing model on a new and unique dataset with equivalent accuracy and significantly less training time.
Use an HPU
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This tutorial is for users who have tried these approaches and found that they need fine-grained control of how TensorFlow uses the HPU.
Tags: GaudiTensorFlow
Distributed Training Using TensorFlow and Horovod
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This tutorial demonstrates how distributed training works with Horovod using Habana Gaudi AI processors. Horovod is a …
Distributed Training Using TensorFlow and HPUStrategy
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This tutorial demonstrates how distributed training works with HPUStrategy using Habana Gaudi AI processors. tf.distribute.Strategy is a TensorFlow …
Data Packing Process for MLPERF BERT
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Introduction Often NLP datasets have large variations in their samples length. setting a maximum sequence …
Intro to Autoencoders
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An adaptation of Intro to Autoencoders tutorial using Habana Gaudi AI processors. This tutorial introduces autoencoders …
Convolutional Neural Network (CNN)
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An adaptation of Convolutional Neural Network (CNN) tutorial using Habana Gaudi AI processors. This tutorial demonstrates …
Mixed precision
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An adaptation of TensorFlow Mixed precision tutorial using Habana Gaudi AI processors. This tutorial demonstrates enabling …
Image classification
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An adaptation of Image classification tutorial using Habana Gaudi AI processors. This tutorial shows how …