This chapter focuses on various computer vision tasks utilizing deep learning. A deep learning network is trained using input samples along with their corresponding task-specific labels. These labeled training samples guide the learning process, enabling the model to perform specific tasks such as classification, object detection, and segmentation by calculating an value (objective loss function) that measures the discrepancy between the model’s output and the expected result (ground truth). During training, the network’s weights and biases are adjusted or fine-tuned to learn the accurate prediction tasks. As discussed in the previous chapter, several components play a pivotal role in facilitating this process. Once trained, the model can be applied to unlabeled test inputs to predict results on unseen images.