Course Description

The course "Convolutional Neural Networks for Visual Recognition" is a comprehensive introduction to the theory and practice of Convolutional Neural Networks (CNNs), a type of deep learning model that is widely used in computer vision tasks such as image classification, object detection, and segmentation. The course covers the key concepts and techniques used in CNNs, including convolution, pooling, activation functions, and fully connected layers. It also explores the different architectures of CNNs, such as AlexNet, VGG, and ResNet, and how they can be adapted and optimized for different visual recognition tasks. The course is designed for students with a background in computer science and mathematics, and covers both the theoretical foundations and practical implementations of CNNs. It includes lectures, assignments, and projects that allow students to apply their knowledge to real-world problems. One of the main strengths of the course is its focus on hands-on experience. Students will have access to state-of-the-art deep learning libraries such as TensorFlow and PyTorch, and will learn how to train and evaluate CNNs on large datasets such as ImageNet. The course also emphasizes the importance of data preparation and augmentation in CNNs. Students will learn how to preprocess and augment images to increase the robustness and accuracy of CNNs, and how to deal with common challenges such as overfitting and vanishing gradients. By the end of the course, students will have a deep understanding of CNNs and their applications in visual recognition, as well as the ability to design, train, and evaluate CNNs for different tasks. They will also have a solid foundation in deep learning that can be applied to other domains beyond computer vision. Overall, "Convolutional Neural Networks for Visual Recognition" is an excellent course for anyone interested in learning about CNNs and their applications in computer vision. With its combination of theory and practice, it provides a comprehensive and engaging learning experience that is sure to be valuable for students and practitioners alike.