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@@ -244,7 +244,7 @@ We can now run the decorated function above. Pass `print_data=True` to see the p
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Computation times Computation times
================= =================
**11:47.213** total execution time for **getting-started_tutorials** files: **11:30.701** total execution time for **getting-started_tutorials** files:
+---------------------------------------------------------------------------------------------------------+-----------+--------+ +---------------------------------------------------------------------------------------------------------+-----------+--------+
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+---------------------------------------------------------------------------------------------------------+-----------+--------+ +---------------------------------------------------------------------------------------------------------+-----------+--------+
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+---------------------------------------------------------------------------------------------------------+-----------+--------+ +---------------------------------------------------------------------------------------------------------+-----------+--------+
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+---------------------------------------------------------------------------------------------------------+-----------+--------+ +---------------------------------------------------------------------------------------------------------+-----------+--------+

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<p class="sphx-glr-script-out">Out:</p> <p class="sphx-glr-script-out">Out:</p>
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Note however that the PyTorch <cite>softmax</cite> operation is more general and will works on tensors of any shape.</p></li> Note however that the PyTorch <cite>softmax</cite> operation is more general and will works on tensors of any shape.</p></li>
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getting-started/tutorials/04-low-memory-dropout.html
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@@ -370,7 +370,7 @@ to explore the <cite>triton/language/random</cite> folder!</p>
<dd><p>Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”, JMLR 2014</p> <dd><p>Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”, JMLR 2014</p>
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