During the 8-week summer session, we will offer our deep learning course online. The course is open to anyone (including non-CS majors) with solid programming background and will cover deep learning fundamentals and how to apply them to a variety of problems.
CSCE 479/879: Introduction to Deep Learning
Instructor: Stephen Scott, sscott2@unl.edu
Session: 8-week
Delivery: Online
Textbook: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, second edition, by Aurélien Géron, O’Reilly Media, 2019
Overview:
Deep learning (DL) is an approach to machine learning (ML) (itself a subarea of artificial intelligence) that is based on artificial neural networks with many layers, designed to automatically improve with experience. In recent years, most cutting-edge machine learning approaches have utilized deep learning, including image classification, natural language processing, game playing, autonomous cars, and robot control. At the same time, there have been important advances in the theory and algorithms that form the foundations of this field.
The goal of this course is to present the fundamentals and current trends in deep learning. The approaches we will cover are applicable to several areas, including game playing, bioinformatics, text categorization, speech recognition, autonomous systems, and machine vision. The emphasized topics in this course will be different approaches and technologies in deep learning, including various activation functions and regularizers, as well as convolutional layers, recurrent networks, reinforcement learning, and autoencoders. At the end of the course, students will sufficiently understand the fundamentals of these areas of machine learning to begin basic research in the area and to understand the ideas of technical papers in deep learning.
This course will be delivered online. Course grades will be based on completion of lab assignments using existing deep learning libraries such as Python’s TensorFlow 2, as well as a course project. The only prerequisite is programming competence.
Catalog Description:
Fundamentals and current trends in deep learning. Backpropagation, activation functions, loss functions, choosing an optimizer, and regularization. Common architectures such as convolutional, autoencoders, and recurrent. Applications such as image analysis, text analysis, sequence analysis, and reinforcement learning.