Computer Vision With MobileNet

This course provides a comprehensive understanding of MobileNet, a state-of-the-art deep learning architecture for resource-constrained devices such as smartphones and IoT devices. MobileNet is optimized for real-time image and video classification, making it an ideal choice for cutting-edge computer vision applications. One of the key innovations in MobileNet is the use of depthwise separable convolutions, which allow for efficient computation and reduced memory footprint compared to traditional convolutional neural networks (CNNs). In this course, you'll learn about the computational costs of standard convolutions and how depthwise separable convolutions reduce computational overhead. In addition, you'll explore squeeze and excitation layers, which add a self-attention mechanism to the network, allowing it to focus on the most important features in an input image.