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  • Now if you're like me, starting a Deep Learning project sounds really exciting. But when it

  • comes to picking the right kind of net to use, well, things can get a little confusing.

  • You first need to decide if you're trying to build a classifier or if you're trying

  • to find patterns in your data. Beyond that, I’ll try to help by giving you some general

  • guidelines.

  • Before we get started, I want to give you a bit of a heads up. I’m going to be using

  • some terminology that may sound a little scary right now, but don’t worry. I’ll cover

  • all these terms in detail in the upcoming videos.

  • If youre interested in unsupervised learningthat is, you want to extract patterns

  • from a set of unlabelled datathen your best bet is to use either a Restricted Boltzmann

  • Machine, or an autoencoder.

  • What type of projects would you need to use a Deep Net for? Please comment and let me

  • know your thoughts.

  • If you have labeled data for supervised learning and you want to build a classifier, you have

  • several different options depending on your application.

  • For text processing tasks like sentiment analysis, parsing, and named entity recognitionuse

  • a Recurrent Net or a Recursive Neural Tensor Network, which well refer to as an RNTN.

  • For any language model that operates on the character level, use a Recurrent Net.

  • For image recognition, use a Deep Belief Network or a Convolutional Net.

  • For object recognition, use a Convolutional Net or an RNTN.

  • Finally, for speech recognition, use a Recurrent Net.

  • In general, Deep Belief Networks and Multilayer Perceptrons with rectified linear unitsalso

  • known as RELUare both good choices for classification. For time series analysis,

  • it’s best to use a Recurrent Net.

  • Deep Nets are the current state of the art in pattern recognition, but it’s worth noting

  • that neural nets have been around for decades. So you might be wondering: why did it take

  • almost 50 years for Deep Nets to come on to the scene? Well, as it turns out, Deep Nets

  • are very hard to train, which we will see in the next video.

Now if you're like me, starting a Deep Learning project sounds really exciting. But when it

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B1 US recurrent recognition convolutional classifier deep learning learning

Your choice of Deep Net - Ep. 4 (Deep Learning SIMPLIFIED)

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    alex posted on 2016/05/07
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