Convolutional neural networks (CNNs) enable very powerful deep learning based techniques for processing, generating, and sensemaking of visual information. These are revolutionary techniques in computer vision that impact technologies ranging from e-commerce to self-driving cars. This course offers an in-depth examination of CNNs, their fundamental processes, their applications, and their role in visualization and image enhancement. The course covers concepts, processes, and technologies such as CNN layers and architectures. It also explains CNN image classification and segmentation, deep dream and style transfer, super-resolution, and generative adversarial networks (GANs). Learners who come to this course with a basic knowledge of deep learning principles, some computer vision experience, and exposure to engineering math should gain the ability to implement CNNs and use them to create their own visualizations.
- Discover the connections between CNNs and the biological principles of vision
- Understand the advantages and trade-offs of various CNN architectures
- Survey the history and evolution of CNN's on-going development
- Learn to apply the latest GAN, style transfer, and semantic segmentation techniques
- Explore CNN applications, visualization, and image enhancement
Nell Watson serves on the Faculty of AI & Robotics at Singularity University and is Dean of Cognitive Science at Exosphere Academy. She founded Poikos (now QuantaCorp), where she created a computer vision technology for body scanning using stereophotogrammetry techniques. Nell is a Fellow of the British Computing Society, a chartered IT professional, and a global lecturer on machine learning.