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├Deep Learning Lecture 1- Introduction.mp4
├Deep Learning Lecture 10- Convolutional Neural Networks.mp4
├Deep Learning Lecture 11- Max-margin learning, transfer and memory networks.mp4
├Deep Learning Lecture 12- Recurrent Neural Nets and LSTMs.mp4
├Deep Learning Lecture 13- Alex Graves on Hallucination with RNNs.mp4
├Deep Learning Lecture 14- Karol Gregor on Variational Autoencoders and Image Generation.mp4
├Deep Learning Lecture 15- Deep Reinforcement Learning - Policy search.mp4
├Deep Learning Lecture 16- Reinforcement learning and neuro-dynamic programming.mp4
├Deep Learning Lecture 2- linear models.mp4
├Deep Learning Lecture 3- Maximum likelihood and information.mp4
├Deep Learning Lecture 4- Regularization, model complexity and data complexity (part 1).mp4
├Deep Learning Lecture 5- Regularization, model complexity and data complexity (part 2).mp4
├Deep Learning Lecture 6- Optimization.mp4
├Deep learning Lecture 7- Logistic regression, a Torch approach.mp4
├Deep Learning Lecture 8- Modular back-propagation, logistic regression and Torch.mp4
└Deep Learning Lecture 9- Neural networks and modular design in Torch.mp4
