We start by reviewing what core concepts in machine learning. Then we discuss how deep learning differs from machine learning, i.e. what deep means and why it is important. We'll learn specifically what neural networks look like and how they are trained using back-propagation. We'll also introduce the concept of computational graphs which is how neural networks are implemented in popular deep learning libraries such as TensorFlow, Torch, etc. We'll end the section with learning regularization techniques specific to deep learning.