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_downloads/0662a149d54bd776924742c96eb6282d/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
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our data. Training a model is an iterative process; in each iteration the model makes a guess about the output, calculates
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the error in its guess (*loss*), collects the derivatives of the error with respect to its parameters (as we saw in
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the `previous section <autograd_tutorial.html>`_), and **optimizes** these parameters using gradient descent. For a more
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the `previous section <autogradqs_tutorial.html>`_), and **optimizes** these parameters using gradient descent. For a more
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detailed walkthrough of this process, check out this video on `backpropagation from 3Blue1Brown <https://www.youtube.com/watch?v=tIeHLnjs5U8>`__.
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Prerequisite Code
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