Overview
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore neural network architectures for image processing in this comprehensive 47-minute lecture. Delve into tasks such as classification, segmentation, denoising, blind deconvolution, superresolution, and inpainting. Examine various architectures including Multilayer Perceptrons, Convolutional Neural Networks, Residual Nets, Encoder-decoder nets, and autoencoders. Learn about the concept of sparing networks from learning already known information. Cover topics like LexNet, VGGNet, CNNs, Residual Blocks, Encoder-Decoder Units, Compressive Autoencoders, and MRI reconstruction. Access accompanying lecture notes for further study and explore referenced research papers for in-depth understanding of the field.
Syllabus
Introduction
Multilayer Perceptrons
LexNet
VGGNet
CNNs
Examples
Residual Blocks
SuperResolution
EncoderDecoder
Unit
Autoencoders
Compressive Autoencoders
superresolution autoencoders
sparing nets
MRI reconstruction
Taught by
Paul Hand