Artificial neural networks form the foundation of modern AI systems. “Deep Learning” offers participants a comprehensive introduction to the core principles and fundamental building blocks used in today’s neural networks. The course covers the most important types of neural networks, like MLPs, CNNs, RNNs, and Transformers, as well as practical techniques for efficient training and the reuse large pre-trained models.
Throughout the course, students will gain a robust understanding of the general training process and key differences between different network types, as well as practical knowledge through hands-on programming exercises.
By the end of the course, students will be equipped with the knowledge and skills to understand, train, and apply deep neural networks to a variety of problems, laying a strong foundation for advanced exploration of the field.