One of the benefits of using TensorFlow, is that it can keep track of operations and automatically update model variables based on back propagation. In this recipe, we will introduce how to use this aspect to our advantage when training machine learning models.
Now we will introduce how to change our variables in the model in such a way that a loss function is minimized. We have learned about how to use objects and operations, and create loss functions that will measure the distance between our predictions and targets. Now we just have to tell TensorFlow how to back propagate errors through our computational graph to update the variables and minimize the loss function. This is done via declaring an optimization function. Once we have an optimization function declared, TensorFlow will go through and figure out the back propagation terms for all of our computations in the graph. When we feed data in and minimize the loss function, TensorFlow will modify...