Deep Neural Networks

Catalog description

Covers neural networks, covering feedforward, convolutional, recurrent architectures and transformers. Topics include backpropagation, regularization, optimization techniques (stochastic gradient descent). Applications in data classification (image recognition), natural language processing and image generation. Hands-on implementation using frameworks like PyTorch. Recommend a course in linear algebra, calculus and basic programming skills (Python) prior to enrollment.