Deep Learning & Neural Networks
Master deep neural architectures, exact gradient backpropagation calculus, and production PyTorch systems.
This course provides mathematical foundations and high-throughput engineering paradigms for deep neural networks. Students derive backward gradient equations across non-linear manifolds and implement distributed optimization algorithms. Advanced topics include architectural design of Transformers and deployment optimization with TensorRT and ONNX Runtime.
Curriculum
Two complete tracks. Study either or both — each has its own exam and certificate.
A mathematically rigorous exploration of deep learning theory, covering multivariable calculus of backpropagation, empirical risk minimization, representation theory, and modern attention mechanics.
Formalize feedforward neural representations, auto-differentiation topologies, and non-convex gradient dynamics.
- Reverse-Mode Automatic Differentiation & Jacobian Calculus Lab40 min
- Stochastic Gradient Dynamics & Hessian Curvature Analysis Lab45 min
- Generalization Bounds & Spectral Norm Regularization Lab35 min
Careers this prepares you for
- Deep Learning Research Engineer
- Computer Vision Specialist
- NLP Infrastructure Engineer
- Machine Learning Systems Architect