Deep Networks: Overview

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(Greedy layer-wise training)
(Difficulty of training deep architectures)
Line 71: Line 71:
The main learning algorithm that researchers were using was to randomly initialize
The main learning algorithm that researchers were using was to randomly initialize
the weights of a deep network, and then train it using a labeled
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, for example by applying gradient descent to try to
using a supervised learning objective, for example by applying gradient descent to try to
drive down the training error.  However, this usually did not work well.
drive down the training error.  However, this usually did not work well.

Revision as of 18:07, 18 May 2011

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