Deep Learning.

Catalog description

Theoretical foundations, algorithms, methodologies, and applications for deep neural networks. Techniques to improve neural networks, regularization, optimizations, and hyperparameter tuning. Deep learning models including convolutional neural networks, recurrent neural networks, transformers, and graph neural networks for vision and language tasks. Generative and discriminative probabilistic models. Transfer learning, multi-modal learning, and multi-task learning. Attention and sequence-to-sequence models. Large language models. Design and implementation of deep learning systems in various application domains using contemporary deep learning programming frameworks.