Designing, Visualizing & Understanding Deep Neural Networks | AI, Computer Vision, NLP

Explore the design principles, visualization tools, and theoretical understanding of deep neural networks in this comprehensive course. Ideal for students interested in AI, computer vision, and natural language processing.

Introduction

Deep Networks have revolutionized computer vision, speech recognition and language translation. They have growing impact in many areas of science and engineering. This course attempts to cover the interplay between intuitive insights, theoretical modeling, practical implementations, empirical studies, and scientific analyses of deep neural networks.

Highlights

• Design principles and best practices: design motifs that work well in particular domains, structure optimization and parameter optimization.
• Visualizing deep networks: Exploring the training and use of deep networks with visualization tools.
• Understanding deep networks: Methods with formal guarantees, including generative and adversarial models, and tensor factorization.

Recommendation

This course is suitable for students with a strong background in calculus, linear algebra, probability and statistics, machine learning, and programming. It provides an in-depth exploration of the design, visualization, and understanding of deep neural networks, which is highly relevant for students interested in computer vision, speech recognition, language processing, and other areas of artificial intelligence.