CS 4510/9010 Applied Machine Learning. Deep Learning. Paula Matuszek Fall copyright Paula Matuszek 2016
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1 CS 4510/9010 Applied Machine Learning 1 Deep Learning Paula Matuszek Fall 2016
2 Beyond Simple Neural Nets 2 In the last few ideas we have seen some surprisingly rapid progress in some areas of AI Image recognition Google Image Search: match this image People matching in photos: Google phptp, iphoto, Facebook, etc. Handwriting recognition: Swype Speech recognition: Okay, Google; Amazon Echo; Siri; Cortina Gaming: AlphaGo
3 Why? 3 Some is due to the massive amounts of data now available, mostly on the web. Some is due to increases in computing power and storage Much is due to an enhancement to learning algorithms called deep learning The basic concept behind deep learning is a multilayered neural network with MANY layers. These layers work in stages
4 Deep Learning 4 Multi-layered NNs are not new. But multiple layers are difficult to train The vanishing gradient problem Back to the organic brain for inspiration Vision processing works in stages earlier neurons recognize components such as a straight line later neurons recognize assemblages of components and eventually you recognize your professor Can we do something similar with neural nets?
5 The Big Deep Learning Aha! 5 The new way to train multi-layer NNs
6 The new way to train multi-layer NNs Train this layer first
7 The new way to train multi-layer NNs Train this layer first then this layer
8 The new way to train multi-layer NNs Train this layer first then this layer then this layer then this layer finally this layer
9 So How Do We Do This? 9 A Neural Net for classification is supervised If we are training a single hidden layer what are we using as the outcome layer? We use an auto-encoder An auto-encoder is a three-layer NN: input, output, and one hidden layer. The input and output layers are the same The hidden layer has fewer nodes.
10 an auto-encoder is trained, with an absolutely standard weightadjustment algorithm to reproduce the input
11 Auto-Encoders 11 We are using the auto-encoder to capture information about the input, summarizing it in higher level form By doing this with fewer units than the inputs, usually many fewer, hidden layer nodes learn to find features. We have used the auto-encoder to provide dimension reduction Note that this is still black box. We don t know what features the layer has found. They may not correspond to anything a person would consider a meaningful feature However, they do summarize the input data in some way
12 Stacked Auto-Encoders 12 So now we can train a layer. The next step is to throw away the output layer, and instead take the output from the auto-encoder and feed that into the next layer.
13 Many-Layered NNs 13 A deep learning network will have many layers The earliest layers are pre-training The are summarizing the input data As we move along the stacked layers, each new layer gets as input the features that have been identified by the previous layer, and produces more complex features that are combinations of earlier features. Until finally we reach an output layer of the classes we are trying to identify.
14 Why Deep Learning 14 Large number of input attributes visual data: pixels in an photo text: words in a document audio: sounds in a speech patterns in a GO game Large number of instances It takes a lot of data to successfully train multiple layers Complex pattern recognition
15 Summary 15 This is a general view of deep learning, and one architecture There are others, generally based on some form of neural net The common thread is the idea of stacked layers, with the output of earlier layers feeding the input of later layers This field is moving very fast.
CS 4510/9010 Applied Machine Learning. Neural Nets. Paula Matuszek Fall copyright Paula Matuszek 2016
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