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Makefile

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@@ -100,7 +100,6 @@ download:
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wget -N https://s3.amazonaws.com/pytorch-tutorial-assets/imagenet_1k.zip -P $(DATADIR)
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unzip $(ZIPOPTS) $(DATADIR)/imagenet_1k.zip -d advanced_source/data/
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docs:
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make download
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make html

_static/img/bert.png

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beginner_source/transfer_learning_tutorial.py

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plt.ioff()
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plt.show()
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######################################################################
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# Further Learning
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# -----------------
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#
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# If you would like to learn more about the applications of transfer learning,
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# checkout our `Quantized Transfer Learning for Computer Vision Tutorial <https://pytorch.org/tutorials/intermediate/quantized_transfer_learning.html>`_.
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#
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index.rst

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:figure: /_static/img/qat.png
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:description: :doc:`advanced/static_quantization_tutorial`
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.. customgalleryitem::
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:tooltip: Perform quantized transfer learning with feature extractor
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:description: :doc:`/intermediate/quantized_transfer_learning_tutorial`
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:figure: /_static/img/quantized_transfer_learning.png
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.. customgalleryitem::
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:tooltip: Convert a well-known state-of-the-art model like BERT into dynamic quantized model
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:description: :doc:`/intermediate/dynamic_quantization_bert_tutorial`
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:figure: /_static/img/bert.png
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.. raw:: html
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<div style='clear:both'></div>
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beginner/text_sentiment_ngrams_tutorial
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beginner/torchtext_translation_tutorial
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beginner/transformer_tutorial
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.. toctree::
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:maxdepth: 2
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:includehidden:
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advanced/dynamic_quantization_tutorial
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advanced/static_quantization_tutorial
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intermediate/quantized_transfer_learning_tutorial
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intermediate/dynamic_quantization_bert_tutorial
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.. toctree::
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:maxdepth: 2

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