Futures: Applications and Classes of Deep Neural Networks

"Applications and Classes of Deep Neural Networks" provides a detailed exploration of deep learning architectures and their real-world applications. The course covers key classes of deep neural networks such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Radial Basis Function Networks (RBFNs), while also examining their use in tasks like classification, image processing, and data generation. The course concludes with an introduction to the Natural Language Toolkit library (nltk) applied to sentiment analysis, which forms a launch point for the subsequent Natural Language Processing (NLP) courses in this series. Students will learn how CNNs are utilized in computer vision for image classification and recognition and how GANs are employed to generate realistic data such as images and videos. The course also examines RBFNs, a specialized class of networks, particularly effective for classification tasks and function approximation. Additionally, students will explore sentiment analysis using NLP techniques, a critical tool in understanding customer feedback and social media trends. By the end of the course, students will gain the knowledge and skills to apply Deep Neural Networks (DNNs) across a wide range of applications, from complex classification to image processing to data generation as well as in sentiment analysis using the foundational components of NLP, making them proficient in these technologies for current and emerging industries.