Comprehensive introduction to deep learning-based generative techniques and models for generating, synthesizing, and manipulating visual data; establishment of a sequence of concepts that will develop mastery through real-world hands-on projects; survey of numerous applications in visualization, data science, computer science, and the arts; aimed at interest in data science, computer science, and related engineering programs. Prerequisites: Graduate classification; two semesters of calculus, linear algebra, and Python programming recommended Credits 3. 2 Lecture Hours. 2 Lab Hours.
Comprehensive introduction to deep learning-based generative techniques and models for generating, synthesizing, and manipulating visual data; establishment of a sequence of concepts that will develop mastery through real-world hands-on projects; survey of numerous applications in visualization, data science, computer science, and the arts; aimed at interest in data science, computer science, and related engineering programs. Prerequisites: Graduate classification; two semesters of calculus, linear algebra, and Python programming recommended Credits 3. 2 Lecture Hours. 2 Lab Hours.
