which is a single neuron or a perceptron we've learned about stacking and composing these perceptrons together to form complex hierarchical representations and how we can mathematically optimize these networks using a technique called back propagation using their loss and finally we address the practical side of training these models such as mini batching regularization and adaptive learning rates as well with that I'll finish up I can take a couple questions and then we'll move on to office lecture on deep sequential modeling I'll take any like maybe a couple questions if