Deep Learning (Oxford)

Lecture 1 Introduction (Oxford)

Lecture 10 Convolutional Neural Networks (Oxford)

Lecture 11 Max-margin learning, transfer and memory networks (Oxford)

Lecture 12 Recurrent Neural Nets and LSTMs (Oxford)

Lecture 13 Alex Graves on Hallucination with RNNs (Oxford)

Lecture 14 Karol Gregor on Variational Autoencoders and Image Generation (Oxford)

Lecture 15 Deep Reinforcement Learning – Policy search (Oxford)

Lecture 16 Reinforcement learning and neuro-dynamic programming (Oxford)

Lecture 2 Linear models (Oxford)

Lecture 3 Maximum likelihood and information (Oxford)

Lecture 4 Regularization, model complexity and data complexity (part 1) (Oxford)

Lecture 5 Regularization, model complexity and data complexity (part 2) (Oxford)

Lecture 6 Optimization (Oxford)

Lecture 7 Logistic regression, a Torch approach (Oxford)

Lecture 8 Modular back-propagation, logistic regression and Torch (Oxford)

Lecture 9 Neural networks and modular design in Torch (Oxford)