Explore the fundamentals of convolutional neural networks in this comprehensive lecture. Delve into discrete convolutions, vertical edge detection, and GA filters before examining the architecture of CNNs. Learn about pooling techniques, including max pooling, and their role in feature extraction. Analyze a digit recognition example to understand feature maps and classification processes. Investigate sparse connections, weights, and various CNN architectures. Conclude with insights into the training process for these powerful deep learning models.
Overview
Syllabus
Intro
Example
Discrete convolutions
Vertical edge detection
GA Filters
Architecture
What is pooling
Digit recognition example
Feature maps
Max pooling
Classification
Sparse connections
Weights
Architectures
Training
Taught by
Pascal Poupart
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