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  • If you want your computer to recognize very complex patternsthen trust me on this

  • you really need to start using neural networks. When the patterns get really complex,

  • neural nets start to outperform all of their competition. Plus, GPUs can train them faster

  • than ever before! Let's take a look.

  • Neural nets truly have the potential to revolutionize the field of Artificial Intelligence. We all

  • know that computers are very good with repetitive calculations and detailed instructions, but

  • they've historically been bad at recognizing patterns. Thanks to deep learning, this is

  • all about to change.

  • If you only need to analyze simple patterns, a basic classification tool like an SVM or

  • Logistic Regression is typically good enough. But when your data has 10s of different inputs

  • or more, neural nets start to win out over the other methods. Still, as the patterns

  • get even more complex, neural networks with a small number of layers can become unusable.

  • The reason is that the number of nodes required in each layer grows exponentially with the

  • number of possible patterns in the data. Eventually training becomes way too expensive and the

  • accuracy starts to suffer. So for an intricate patternlike an image of a human face,

  • for examplebasic classification engines and shallow neural nets simply aren’t good

  • enoughthe only practical choice is a deep net.

  • Have you ever run into a wall when trying to work with highly complex data? Please comment

  • and let me know your thoughts.

  • But what enables a deep net to recognize these complex patterns? The key is that deep nets

  • are able to break the complex patterns down into a series of simpler patterns. For example,

  • let's say that a net had to decide whether or not an image contained a human face. A

  • deep net would first use edges to detect different parts of the facethe lips, nose, eyes,

  • ears, and so onand would then combine the results together to form the whole face.

  • This important featureusing simpler patterns as building blocks to detect complex patterns

  • is what gives deep nets their strength. The accuracy of these nets has become very

  • impressivein fact, a deep net from google recently beat a human at a pattern recognition

  • challenge.

  • It’s not surprising that deep nets were inspired by the structure of our own human

  • brains. Even in the early days of neural networks, researches wanted to link a large number of

  • perceptrons together in a layered weban idea which helped improve their accuracy.

  • It is believed that our brains have a very deep architecture and that we decipher patterns

  • just like a deep netwe detect complex patterns by first detecting, and combining,

  • the simple ones.

  • There is one downside to all of thisdeep nets take much longer to train. The good news

  • is that recent advances in computing have really reduced the amount of time it takes

  • to properly train a net. High performance GPUs can finish training a complex net in

  • under a week, when fast CPUs may have taken weeks or even months.

  • Before we talk more about the various Deep Learning models, we're going to briefly

  • discuss which types of deep nets are suitable for different machine learning tasks. That's

  • coming up in the next video.

If you want your computer to recognize very complex patternsthen trust me on this

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B1 INT US deep neural complex net detect accuracy

3 reasons to go Deep - Ep. 3 (Deep Learning SIMPLIFIED)

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    firefox   posted on 2017/06/14
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