Neural Networks and Deep Learning

Catalog description

Theoretical fundamentals of neural networks and deep learning. Deep neural networks, gradient descent, backpropagation, regularization, convolutional networks, transformers, graph neural networks, generative adversarial networks, autoencoders, long short-term memory, reinforcement learning, modified Hopfield neural networks and MIMO, Kolmogorov-Arnold networks.