DATA 442 - Neural Networks & Deep Learning

Credits: (3)
Prerequisite(s): DATA 301
College Curriculum: COLL 400
This course teaches the foundations of Neural Networks and Deep Learning. Students entering into this course should have, at minimum, a background in data preprocessing, cleaning, manipulation, and dimensionality reduction within python. Through an applied learning project, you will learn how to implement a machine learning project from design to implementation in the context of neural networks. Topics we will cover include the basic building blocks of neural networks, RNNs, convolutional networks and computer vision, backpropagation basics and strategies (including inductive transfer approaches), differences between technical implementations (i.e., TensorFlow, Keras, Torch), and more.