Neural Networks for Machine Learning
By   |  July 28, 2017

This Mooc is offered by University of Toronto. It is an introduction of Neural Networks for Machine Learning.

Learn about artificial neural networks and how they’re being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We’ll emphasize both the basic algorithms and the practical tricks needed to get them to work well.

This course contains the same content presented on Coursera beginning in 2013. It is not a continuation or update of the original course. It has been adapted for the new platform. Please be advised that the course is suited for an intermediate level learner – comfortable with calculus and with experience programming (Python).

Topics covered include:

  • The Perceptron learning procedure
  • The backpropagation learning proccedure
  • Learning feature vectors for words
  • Object recognition with neural nets
  • Optimization: How to make the learning go faster
  • Recurrent neural networks
  • Ways to make neural networks generalize better
  • Combining multiple neural networks to improve generalization
  • Hopfield nets and Boltzmann machines
  • Restricted Boltzmann machines (RBMs)
  • Stacking RBMs to make Deep Belief Nets
  • Deep neural nets with generative pre-training
  • Modeling hierarchical structure with neural nets
  • Recent applications of deep neural nets

Details:

  • Length: 16 weeks
  • Effort: 4-6 hours per week
  • Price: Free
  • Subject: Computer Science
  • Level: Intermediate
  • Languages: English

For more information: https://fr.coursera.org/learn/neural-networks#

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