Introduction to Neural Networks and Machine Learning (CSC321, Winter 2013)

Tijmen Tieleman, Geoffrey Hinton, University of Toronto

In this course, we study neural networks of various types. Topics include: neural network architectures, perceptrons, the backpropagation algorithm, neuro-probabilistic language models, convolutional nets for digit recognition, mini-batch gradient descent, the momentum method, recursive neural networks, Bayesian approach, Hopfield Nets, RBM learning, Deep autoencoders, Semantic Hashing,

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  • Free schedule
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  • Language: English Gb

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