Deep Learning

The "Deep Learning" course represents an advanced level of study in machine learning, focusing on deep neural networks and their application in solving complex tasks. Students deepen their understanding of neural networks fundamentals, exploring various architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), as well as deep architectures including deep autoencoders and Generative Adversarial Networks (GANs). The course covers both theoretical aspects and practical methods of training deep models, including optimization techniques, regularization, and data preprocessing. Students also familiarize themselves with modern libraries and tools for implementing deep learning, such as TensorFlow and PyTorch. The course prepares students to apply deep learning in various fields, including computer vision, natural language processing, medical sciences, and autonomous systems.