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Neural Networks for Machine Learning Training

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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. There is an emphasis hands-on labs to fully explain and extend the curriculum. 

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Course Outline

An overview of the main types of neural network architecture 

    The backpropagation learning procedure 

Learning the weights of a linear neuron 

    Learning feature vectors for words 

Learning to predict the next word 

    Object recognition with neural nets 

In this module we look at why object recognition is difficult 

    Optimization: How to make the learning go faster 

We delve into mini-batch gradient descent as well as discuss adaptive learning rates 

    Recurrent neural networks 

This module explores training recurrent neural networks 

    More recurrent neural networks 

We continue our look at recurrent neural networks 

    Ways to make neural networks generalize better 

We discuss strategies to make neural networks generalize better 

    Combining multiple neural networks to improve generalization 

This module we look at why it helps to combine multiple neural networks to improve generalization 

    Hopfield nets and Boltzmann machines 

Restricted Boltzmann machines (RBMs) 

    This module deals with Boltzmann machine learning 

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 

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