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@@ -85,7 +86,7 @@ After that, you can use :code:`Vivado HLS` to synthesize the model:
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# Print out the report if you want
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hls4ml.report.read_vivado_report('my-hls-test')
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Done! You've built your first project using ``hls4ml``! To learn more about our various API functionalities, check out our tutorials `here <https://github.com/fastmachinelearning/hls4ml-tutorial>`__.
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Done! You've built your first project using ``hls4ml``! To learn more about our various API functionalities, check out our tutorials `here <https://github.com/fastmachinelearning/hls4ml-tutorial>`__.
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If you want to configure your model further, check out our :doc:`Configuration <api/configuration>` page.
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@@ -100,15 +101,15 @@ To follow this tutorial, you must first download our ``example-models`` reposito
The model files, along with other configuration parameters, are defined in the ``.yml`` files.
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Further information about ``.yml`` files can be found in :doc:`Configuration <api/configuration>` page.
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In order to create an example HLS project:
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@@ -179,4 +180,4 @@ Existing examples
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Examples of model files and weights can be found in `example_models <https://github.com/fastmachinelearning/example-models>`_ directory.
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*
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Training codes and examples of resources needed to train the models can be found `in the tutorial <https://github.com/fastmachinelearning/keras-training>`__.
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Training codes and examples of resources needed to train the models can be found in the `tutorial <https://github.com/fastmachinelearning/hls4ml-tutorial>`__.
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