Introduction to Computer Vision

Computer vision is one of the applications that are rapidly growing thanks to deep learning. Some of the applications of computer vision that are using deep learning include:

1. Self-Driving Cars: Utilizing deep learning for real-time image processing and decision-making.
2. Face Recognition: Leveraging deep learning for accurate and efficient facial recognition systems.

Deep learning is also enabling new types of art to be created. Rapid changes to computer vision are making new applications that weren't possible a few years ago possible. Computer vision deep leaning techniques are always evolving.

A notable example of cross-domain influence is Andrew Ng's application of concepts from computer vision to speech recognition, demonstrating the interconnectivity of deep learning techniques across different fields.

Examples of a computer vision problems include:

• Image Classification: Categorizing images into various classes.
• Object Detection: Identifying and localizing objects within images.
• Neural Style Transfer: Transforming the style of an image using the stylistic elements of another image.

One of the primary challenges in computer vision is managing the large size of input vectors from images. For instance, a $1000 \times 1000$ pixel image, accounting for RGB channels, results in 3 million input features. This scale of inputs makes it impractical to use fully connected neural networks due to the immense computational resources required.

If the above hidden layer contains 1000 nodes, then we will want to learn weights of the shape $[1000, \text{3 million}]$ which is 3 billion parameters only in the first layer! This is way too computationally expensive!

The solution is the adoption of convolution layers as opposed to fully connected layers. Convolution layers significantly reduce the number of parameters, making it feasible to process large images. The convolution operation is crucial in handling larger images efficiently and effectively in deep learning models.