Understanding the Basics of Deep Learning and Neural Networks

Last week I had the opportunity to visit my graduate school alma mater, The University of Arizona where I studied artificial intelligence and image processing many years ago. I remember signing up for my first semester classes and electing to challenge myself with Professor Neifeld's neural network class. It already had the reputation of being one of the toughest classes requiring students to understand both the mathematical theory and real-world application of neural networks to solve classification and other problems. Neural Networks Before Cloud Computing Of course back then there wasn't cloud computing or easy access to parallel computing methods or deep learning Python libraries. As students, we had to program the algorithms by hand starting with the mathematics of a single neuron, the iterations to loop through all the neurons in each layer, and the algorithms to implement the backpropagation learning algorithms.