Optimizing Model Parameters =========================== 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 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>`__.
Tasks: Optimizers, Tensors, Backpropagation
Task Categories: Deep Learning Fundamentals