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T81-558: Applications of Deep Neural Networks Spring 2018 ... - GitHub

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T81-558: Applications of Deep Neural Networks Washington University in St. Louis

Spring 2018, Mondays, 6 - 8:30 PM, Online and Cupples II / L001 Jeff Heaton, Ph.D., [email protected] http://sites.wustl.edu/jeffheaton (636) 525-1842 This syllabus is subject to change as the semester progresses.

Course Description: Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction. Prerequisite(s): None; however, general programming experience is assumed. The Python programming language will be used for this class and reviewed as appropriate. Elements from mathematics (generally at a Calculus I level) will be introduced and explained. Credit Hours: 3

Text: No required text. Course Objectives: At the completion of this course, students will be able to: 1. Explain how neural networks (deep and otherwise) compare to other machine learning models. 2. Determine when a deep neural network would be a good choice for a particular problem. 3. Demonstrate their understanding of the material through a final project.

1

Grade Distribution: Class Attendance & Participation Module Assignments (10) Kaggle Project Final Project

10% 50% 20% 20%

Letter Grade Distribution: 100 94 87 85 82 79

A+ A AB+ B B-

77 75 73