Deep Learning for Data Science

An advanced course grounding students in deep learning fundamentals — loss functions, gradient descent, backpropagation — and the architectures built on them, from convolutional networks to transformers. Students build, train, and evaluate models in PyTorch, gain exposure to pre-trained foundation models, and apply the material in a final project.