CS294A Lecture notes Sparse autoencoder （稀疏自编码器课程讲义，吴恩达）
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2017-11-20
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CS294A Lecture notes
Andrew Ng
Sparse autoencoder
1 Introduction
Supervised learning is one of the most powerful to ols of AI, and has led to
automatic zip code recognition, speech recognition, self-driving cars, and a
continually improving understanding of the human genome. Despite its sig-
nificant successes, supervised learning today is still severely limited. Specifi-
cally, most applications of it still require that we manually specify the input
features x given to the algorithm. Once a good feature representation is
given, a supervised learning algorithm can do well. But in such domains as
computer vision, audio processing, and natural language processing, there’re
now hundreds or perhaps thousands of researchers who’ve sp ent years of their
lives slowly and laboriously hand-engineering vision, audio or text features.
While much of this feature-engineering work is extremely clever, one has to
wonder if we can do better. Certainly this labor-intensive hand-engineering
approach does not scale well to new problems; further, ideally we’d like to
have algorithms that can automatically le arn even be tter feature representa-
tions than the hand-engineered ones.
These notes describe the sparse autoencoder learning algorithm, which
is one approach to automatically learn features from unlabeled data. In some
domains, such as computer vision, this approach is not by itself competitive
with the best hand-engineered features, but the features it can learn do turn
out to be useful for a range of problems (including ones in audio, text, etc).
Further, there’re more sophisticated versions of the sparse auto encoder (not
described in these notes, but that you’ll hear more about later in the class)
that do surprisingly well, and in many cases are competitive with or superior
to even the best hand-engineered representations.
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