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Nov 28, 2022
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Update optimization_tutorial.py #2129

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2 changes: 1 addition & 1 deletion beginner_source/basics/optimization_tutorial.py
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===========================

Now that we have a model and data it's time to train, validate and test our model by optimizing its parameters on
our data. Training a model is an iterative process; in each iteration (called an *epoch*) the model makes a guess about the output, calculates
our data. Training a model is an iterative process; in each iteration the model makes a guess about the output, calculates
the error in its guess (*loss*), collects the derivatives of the error with respect to its parameters (as we saw in
the `previous section <autograd_tutorial.html>`_), and **optimizes** these parameters using gradient descent. For a more
detailed walkthrough of this process, check out this video on `backpropagation from 3Blue1Brown <https://www.youtube.com/watch?v=tIeHLnjs5U8>`__.
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