tailieunhanh - Lecture Machine learning (2014-2015) - Lecture 8: Neural networks

This lecture introduces you to the fascinating subject of classification and regression with artificial neural networks. In particular, it introduces multi-layer perceptrons (MLPs); teaches you how to combine probability with neural networks so that the nets can be applied to regression, binary classification and multivariate classification; discusses the modular approach to backpropagation and neural network construction in Torch, which was introduced in the previous lecture. | Neural networks Nando de Freitas UNIVERSITY OF OXFORD Outline of the lecture This lecture introduces you to the fascinating subject of classification and regression with artificial neural networks. In particular it Introduces multi-layer perceptrons MLPs Teaches you how to combine probability with neural networks so that the nets can be applied to regression binary classification and multivariate classification. Discusses the modular approach to backpropagation and neural network construction in Torch which was introduced in the previous lecture. MLP - 1 neuron 1 01 xi1 g txzem We u 0 i2 o y P yi 1 xi q 03 01 0. .

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• Machine learning
• Lecture Machine learning
• Machine learning algorithms
• Data analysis methods
• Neural networks
• Artificial neural networks
• Max margin learning
• Multi instance learning
• Reinforcement learning
• Delayed rewards
• Machine Learning Introduction
• Machine Learning Work
• Types of Machine Learning
• Supervised Learning
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• Bayesian Learning
• Back propagation
• Boltzmann machines
• Linear prediction
• Linear regression models
• Maximum likelihood
• Gaussian distributions
• Nonlinear ridge regression risk
• Cross validation
• Newton’s algorithm
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• Logistic regression
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• Formulate layers
• Convolutional neural networks
• Design convolutional layers
• Recurrent nets
• Recurrent neural networks
• Lecture Introduction to Machine learning and Data mining
• Bài giảng Học máy và khai phá dữ liệu
• Machine learning and data mining
• Linear regression
• Learning a regression function
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• Deep Belief Network
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• Beyond classification and regression
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• Introduction to machine learning
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