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).
Introduction to the course - machine learning and neural nets
An overview of the main types of neural network architecture
Learning the weights of a linear neuron
Learning to predict the next word
In this module we look at why object recognition is difficult.
We delve into mini-batch gradient descent as well as discuss adaptive learning rates.
This module explores training recurrent neural networks
We continue our look at recurrent neural networks
We discuss strategies to make neural networks generalize better
This module we look at why it helps to combine multiple neural networks to improve generalization
This module deals with Boltzmann machine learning
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