This course on Convolutional Neural Networks covers foundational and advanced principles, focusing on architecture, applications, and hands-on projects with datasets.
Dive into the world of visual data processing with our comprehensive CNN course! This program covers everything from the basic principles of Convolutional Neural Networks to advanced applications. You'll begin with an in-depth exploration of CNN architecture, including convolutional layers, pooling layers and fully connected layers, while mastering key concepts like feature extraction, activation functions and loss functions. With practical implementation in MATLAB and Python, you'll learn how to build and optimize CNN models. The primary focus is on explaining the CNN with numerical examples, which gives you hands-on experience in executing the CNN with real numerical data. Through the real example, you'll learn how to transform raw pixel data into feature maps, apply convolution operations and use pooling layers by directly applying the numeric data. You'll also work on optimizing weights and hyperparameters through the numerical examples. Additionally, case studies will explore how CNN works in image classification, along with their performance evaluation. By the end of this course, you'll achieve mastery in CNNs, ready to utilize them across various domains, making it suitable for both beginners and seasoned professionals.