Search System Tutorial Simplifies Deep Learning
June 21, 2013
In the Wikipedia UFLDL Tutorial, you can learn the basics of Unsupervised Feature Learning and Deep Learning. Of course the tutorial is meant for those who already have some understanding of machine learning (if you need and even more basic approach, you can visit the Machine Learning Course to catch up on supervised learning, logistics regression and gradient descent). The tutorial covers Sparse Autoencoder, Vectorized implementation, Preprocessing: PCA and Whitening as well as Softmax Regression and Building Deep Networks. One exercise for Self-Taught Learning states,
“In this exercise, we will use the self-taught learning paradigm with the sparse autoencoder and softmax classifier to build a classifier for handwritten digits.You will be building upon your code from the earlier exercises. First, you will train your sparse autoencoder on an “unlabeled” training dataset of handwritten digits. This produces feature that are pen stroke-like…These features will then be used as inputs to the softmax classifier that you wrote in the previous exercise.”
The tutorial walks you through each step with a number of examples and exercises, turning what might be fairly expected to be a complicated process into a veritable textbook- streamlined, straightforward and easy to understand. It turns out search systems can be very simple when automated and partially automated learning are implemented.
Chelsea Kerwin, June 21, 2013
Sponsored by ArnoldIT.com, developer of Augmentext.
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