A survey of Deep Learning research topics in computer vision and data science. Deep learning techniques may include Deep Neural Networks, Convolutional Neural Networks, Recurrent Networks, Deep Generative Models and Reinforcement Learning. Application domains will focus on computer vision problems, including image classification, object detection and image segmentation. Additional application domains relevant to graduate students taking the course will be included. Software tools will be introduced for practical application. Weekly hours: 3 Seminar/Discussion hoursNote: Instructor approval required. Students may not receive credit for both CMPT 489 and CMPT 828.
A survey of Deep Learning research topics in computer vision and data science. Deep learning techniques may include Deep Neural Networks, Convolutional Neural Networks, Recurrent Networks, Deep Generative Models and Reinforcement Learning. Application domains will focus on computer vision problems, including image classification, object detection and image segmentation. Additional application domains relevant to graduate students taking the course will be included. Software tools will be introduced for practical application. Weekly hours: 3 Seminar/Discussion hoursNote: Instructor approval required. Students may not receive credit for both CMPT 489 and CMPT 828.
