Deep Learning: CNNs for Visual Recognition

Welcome to this course: Deep Learning - Learn Convolutional Neural Networks.

Deep Learning has made some huge and significant contributions and it’s one of the mostly adopted techniques in order to drive insights from your data nowadays. Convolutional neural networks have gained a special status over the last few years as an especially promising form of deep learning. Rooted in image processing, convolutional layers have found their way into virtually all subfields of deep learning, and are very successful for the most part. Convolutional Neural Networks are very similar to ordinary Neural Networks: they are made up of neurons that have learnable weights and biases. Convolutional neural networks (CNNs) enable very powerful deep learning based techniques for processing, generating, and sensemaking of visual information. These are revolutionary techniques in computer vision that impact technologies ranging from e-commerce to self-driving cars. 

In this course, we will learn an in-depth examination of CNNs, their fundamental processes, their applications, and their role in visualization and image enhancement. We will cover concepts, processes, and technologies such as CNN layers and architectures. You'll learn CNN image classification and segmentation, deep dream and style transfer, super-resolution, and generative adversarial networks (GANs). At the end of this course, students who come to this course with a basic knowledge of deep learning principles, some computer vision experience, and exposure to engineering math should gain the ability to implement CNNs and use them to create their own visualizations.

Don’t wait. Enroll in this course today.