Deep Learning 2: Mathematical principles and backpropagation

Reinforcement Learning 9: Model-based methods

Reinforcement Learning 10: Extended methods

Reinforcement Learning 8: Policy gradient methods

Reinforcement Learning 6: Temporal-difference methods

Reinforcement Learning 4: Dynamic programming

Reinforcement Learning 7: Function approximation

Reinforcement Learning 5: Monte Carlo methods

Reinforcement Learning 3: OpenAI gym

Reinforcement Learning 2: Markov Decision Processes

Reinforcement Learning 1: Foundations

Deep Learning 10: Meta learning and manifold learning

Deep Learning 9: Flow models and implicit networks

Deep Learning 7: Energy-based models

Deep Learning 3: PyTorch programming (coding session)

Deep Learning 8: Sequential models

Deep Learning 6: Adversarial models

Deep Learning 5: Generative models

Deep Learning 4: Designing Models to Generalise

Deep Learning 1: Introduction

Gradient Origin Networks (GONs)

Interactive GPU active contours for segmenting inhomogeneous objects

ABI: Automatic 3D Billboard Imposters

Starship Physics