Explore convolutional neural networks for computer vision in this 37-minute lecture from MIT's Introduction to Deep Learning course. Delve into how computers perceive images, learn visual features, and perform feature extraction through convolution. Examine the architecture of convolutional neural networks, including non-linearity and pooling layers. Follow along with a code example and discover real-world applications, including end-to-end self-driving cars. Gain a comprehensive understanding of CNNs and their role in advancing computer vision technology.

Syllabus

- Introduction
- What computers "see"
- Learning visual features
- Feature extraction and convolution
- Convolution neural networks
- Non-linearity and pooling
- Code example
- Applications
- End-to-end self driving cars
- Summary