Foundations of Deep Learning

The "Foundations of Deep Learning" series consists of three courses, providing a comprehensive introduction to modern deep learning. The part "Basics" introduces core principles such as neural network architecture, backpropagation, optimisation, and regularisation. The part "Architectures & Methodology" delves into specialised models like CNNs, RNNs, and Transformers. The last part "Advanced Topics" presents topics such as generative models, uncertainty estimation, and hyperparameter optimisation.