Deep Networks: Overview

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(Advantages of deep networks)
(Difficulty of training deep architectures)
Line 69: Line 69:
researchers had little success training deep architectures.
researchers had little success training deep architectures.
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The main method that researchers were using was to randomly initialize
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The main learning algorithm that researchers were using was to randomly initialize
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the weights of the deep network, and then train it using a labeled
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the weights of a deep network, and then train it using a labeled
training set <math>\{ (x^{(1)}_l, y^{(1}), \ldots, (x^{(m_l)}_l, y^{(m_l}) \}</math>
training set <math>\{ (x^{(1)}_l, y^{(1}), \ldots, (x^{(m_l)}_l, y^{(m_l}) \}</math>
using a supervised learning objective, using gradient descent to try to
using a supervised learning objective, using gradient descent to try to

Revision as of 20:25, 13 May 2011

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