Deep Learning: Edge Detection and Neural Networks

This lecture covers the fundamentals of edge detection and the evolution of neural networks, particularly focusing on deep learning techniques. The instructor begins by discussing edge detection, specifically the Canny algorithm, which transforms images into gradient images to identify contours. The challenges of parameter tuning in real-world images are highlighted, emphasizing the need for machine learning approaches to improve edge detection accuracy. The lecture transitions into the development of deep learning, explaining how traditional logistic regression evolved into multi-layer perceptrons and convolutional neural networks (CNNs). The importance of non-linear activation functions, such as ReLU, is discussed, along with the advantages of deeper networks in modeling complex functions. The instructor also touches on the significance of training databases and the role of GPUs in making deep learning practical. Finally, the lecture introduces advanced architectures like U-Net and transformers, showcasing their applications in image segmentation and processing, and concludes with a discussion on the current state of deep learning in computer vision.