Nando de Freitas
I am a machine learning professor at UBC. I am making my lectures available to the world with the hope that this will give more folks out there the opportunity to learn some of the wonderful things I have been fortunate to learn myself. Enjoy.
Deep Learning Lecture 15: Deep Reinforcement Learning - Policy search
Deep Learning Lecture 16: Reinforcement learning and neuro-dynamic programming
Deep Learning Lecture 14: Karol Gregor on Variational Autoencoders and Image Generation
Deep Learning Lecture 13: Alex Graves on Hallucination with RNNs
Лекция 12 по глубокому обучению: Рекуррентные нейронные сети и LSTM
Deep Learning Lecture 11: Max-margin learning, transfer and memory networks
Deep Learning Lecture 10: Convolutional Neural Networks
Deep Learning Lecture 9: Neural networks and modular design in Torch
Deep learning Lecture 7: Logistic regression, a Torch approach
Deep Learning Lecture 8: Modular back-propagation, logistic regression and Torch
Deep Learning Lecture 5: Regularization, model complexity and data complexity (part 2)
Лекция 6 по глубокому обучению: Оптимизация
Deep Learning Lecture 4: Regularization, model complexity and data complexity (part 1)
Deep Learning Lecture 3: Maximum likelihood and information
Deep Learning Lecture 2: linear models
Лекция 1 по глубокому обучению: Введение
Машинное обучение — метод Монте-Карло с цепями Маркова (MCMC) II
Machine learning - Importance sampling and MCMC I
Machine learning - Deep learning II, the Google autoencoders and dropout
Machine learning - Deep learning I
Machine learning - Neural networks
Machine learning - Logistic regression
Machine learning - Unconstrained optimization
Machine learning - Random forests applications
Machine learning - Random forests
Machine learning - Decision trees
Machine learning - Bayesian optimization and multi-armed bandits
Machine learning - Gaussian processes
Machine learning - Introduction to Gaussian processes
Machine learning - Bayesian learning part 2