Explore the practical applications of Convolutional Neural Networks (ConvNets) in this comprehensive 52-minute lecture by Yann LeCun. Delve into the fundamentals of convolutions, their uses, and the importance of stacking layers. Learn about object detection, multiple object recognition, and character recognition techniques. Discover the sliding window ConvNet approach and its application in face detection. Engage with a whiteboard session and Q&A to solidify your understanding. Investigate semantic segmentation and its role in robot navigation. Examine category-level semantic segmentation, FPGA ConvNet accelerators, and error rates on ImageNet. Compare different network architectures, including ResNet, to gain insights into state-of-the-art deep learning techniques.
Overview
Syllabus
– Welcome to class
– ConvNets in practice
– What are convolutions good for?
– Why do we need to stack layers?
– Object detection, multiple object recognition
– Multiple character recognition
– Sliding window ConvNet
– Face detection
– Whiteboard time!
– Q&A
– Semantic segmentation
– Robot navigation using semantic segmentation
– Category-level semantic segmentation
– FPGA ConvNet accelerator
– Error rate on ImageNet
– ResNet
– Networks comparison
Taught by
Alfredo Canziani
