Deep Learning For Visual Computing

Deep learning is a part of machine learning where computers learn to understand data by building knowledge step by step, from simple patterns to complex ideas. For example, when a machine looks at an image, it first learns basic features like lines, edges, curves, and colors. Next, it combines these features to recognize parts of objects such as faces, trees, or buildings. At higher levels, it learns to identify complete objects like people, animals, or mountains, and finally understands the full meaning of the image, such as recognizing a person standing in front of a mountain. Deep learning teaches machines to automatically learn these features and relationships without being explicitly programmed. This approach is widely used in applications like handwritten character recognition, object detection, image captioning, self-driving cars, and generating synthetic images. This course introduces both the theory and hands-on coding practice in deep learning for visual computing, using Python and PyTorch through well-designed practical exercises based on current technologies.

Source: Deep Learning For Visual Computing - IITKGP [Youtube Channel]