Learners can explore the concepts of convolutional neural network (CNN); the underlying architecture, principles, and methods needed to build a CNN; and its implementation in a deep neural network. In this 12-video course, you will examine visual perception, and the ability to interpret the surrounding environment by using light in the visible spectrum. First, learn about CNN architecture; how to analyze the essential layers; and the impact of an initial choice of layers. Next, you will learn about nonlinearity in the first layer, and the need for several pooling techniques. Then learn how to implement a convolutional layer and sparse interaction. Examine the hidden layers of CNN, which are convolutional layers, ReLU (rectified linear unit) layers, or activation functions, the pooling layers, the fully connected layer, and the normalization layer. You will examine machine learning semantic segmentation to understand an image at the pixel level, and its implementation using Texton Forest and a random based classifier. Finally, this course examines Gradient Descent and its variants.
Learning Objectives
Learners can explore the concepts of convolutional neural network (CNN); the underlying architecture, principles, and methods needed to build a CNN; and its implementation in a deep neural network. In this 12-video course, you will examine visual perception, and the ability to interpret the surrounding environment by using light in the visible spectrum. First, learn about CNN architecture; how to analyze the essential layers; and the impact of an initial choice of layers. Next, you will learn about nonlinearity in the first layer, and the need for several pooling techniques. Then learn how to implement a convolutional layer and sparse interaction. Examine the hidden layers of CNN, which are convolutional layers, ReLU (rectified linear unit) layers, or activation functions, the pooling layers, the fully connected layer, and the normalization layer. You will examine machine learning semantic segmentation to understand an image at the pixel level, and its implementation using Texton Forest and a random based classifier. Finally, this course examines Gradient Descent and its variants.