Deep Learning (Oxford)

• Lecture 1 Introduction (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)
• 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 16 Reinforcement learning and neuro-dynamic programming (Oxford)