Explore the fundamentals of Convolutional Neural Networks (CNNs) in this comprehensive lecture by Yann LeCun. Begin with a visualization of a 6-layer neural network before delving into the core concepts of convolution and CNNs. Examine various parameter transformations in CNNs, introduce the concept of kernels for hierarchical feature learning, and understand how CNNs classify input data. Discover the biological inspirations behind CNNs and trace their evolution through history. Gain insights into modern CNN architectures, focusing on LeNet5 and its application in digit recognition using the MNIST dataset. Analyze the design principles of CNNs and their advantages in exploiting compositionality, stationarity, and locality features in natural images. Conclude by exploring feature binding and the diverse applications of ConvNets in various domains.
Overview
Syllabus
– Week 3 – Lecture
– Visualization of Neural Networks
– Parameter Transformations, the Convolution Operator, and Deep Convolutional Neural Networks
– Inspirations from Biology
– The First ConvNets
– LeNet5 and digit recognition
– Feature Binding and What are ConvNets Good for?
Taught by
Alfredo Canziani
