All we have are individual “low-level” sound wave frequency and amplitude, or pixel values indicating intensity and color. With tasks like voice and image recognition, structured informative predictor information like this is not available. In predicting insurance fraud, we might guess that policy age would be predictive. In predicting possible bank failure, for example, we would guess that certain financial ratios (return on assets, return on equity, etc.) might have predictive value. For example, to explain how it can recognize faces out of a matrix of pixel values (i.e., an image).Īs a data science educator, for years I have been seeking a clear and intuitive explanation of this transformative core of deep learning-the ability of the neural net to “discover” what machine learning specialists call “higher level features.” Older statistical modeling and machine learning algorithms, including neural nets, worked with databases where those features with predictive power already exist. But they don’t really shed much light on deep learning’s seemingly magical powers. But what, exactly, is deep` learning?ĭozens of articles tell you that it’s a complex, multilayered neural network. Image recognition is the key to self-driving cars. Voice recognition allows you to talk to your robot devices. Deep learning by complex neural networks lies behind the applications that are finally bringing artificial intelligence out of the realm of science fiction into reality. On Wednesday, March 27, the 2018 Turing Award in computing was given to Yoshua Bengio, Geoffrey Hinton and Yann LeCun for their work on deep learning.
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