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Deep Learning: An Introduction from the NLP Perspective
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2014-04-29
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如题,从NLP的视角介绍Deep Learning。第一部分讲Neural Networks,第二部分介绍DBN,第三部分讲Stacked Auto-Encoders。英文,但是比较易懂。
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Introduction
Neural Networks
Deep Learning Approach 1
Deep Learning Approach 2
Disclaimer
I am not (yet) an expert in Deep Learning. Let me know if
these slides contain any mistakes.
The focus here is Natural Language Processing (NLP); I’m
glossing over much active work in Vision & Speech.
Lots of good tutorial information online, some borrowed here:
[Bengio, 2009] Excellent short book summarizing the area
[Socher et al., 2012a] Tutorial with video
Step-by-step code based on Theano python library:
http://deeplearning.net/tutorial/
2/43
Introduction
Neural Networks
Deep Learning Approach 1
Deep Learning Approach 2
Outline
1
Introduction
2
Neural Networks
Preliminaries
1-Layer & 2-Layer Nets
Neural Language Models
3
Deep Learning Approach 1: Deep Belief Nets
Preliminaries
Restricted Boltzman Machines
Deep Belief Nets
4
Deep Learning Approach 2: Stacked Auto-Encoders
Auto-Encoders
Stacked Auto-Encoders
Denoising Auto-Encoders and Variants
3/43
Introduction
Neural Networks
Deep Learning Approach 1
Deep Learning Approach 2
What is Deep Learning?
A model (e.g. neural network) with many layers, trained in a
layer-wise way
An approach for unsupervised learning of feature
representations, at successively higher levels
These two definitions are very related, but correspond to
different motivations.
4/43
Introduction
Neural Networks
Deep Learning Approach 1
Deep Learning Approach 2
What is Deep Learning?
A model (e.g. neural network) with many layers, trained in a
layer-wise way
An approach for unsupervised learning of feature
representations, at successively higher levels
These two definitions are very related, but correspond to
different motivations.
4/43
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