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.

# Implementing backpropagation

# Getting ready

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 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...