Concepts of Deep learning

This course builds a clear conceptual foundation in deep learning. You will learn how neural networks work, how they are trained using backpropagation and gradient descent, and how to improve them with regularization and optimization techniques. The course then covers convolutional neural networks (CNNs) for images, recurrent networks (RNNs, LSTMs) for sequences, attention and transformers, and an introduction to autoencoders and generative models. Basic Python and familiarity with machine learning concepts are helpful.