Scientific American on Deep Learning

Scientific American does a good job of providing a good,  intuitive and largely non-technical view of the math and applications of deep learning.   Good for use with management with a reasonable amount of patience.   I would not call it deep or complete, but enough for a useful intro.

A Deep Dive into Deep Learning
A personal journey to understand what lies beneath the startling powers of advanced neural networks
By Peter Bruce on April 10, 2019 in Sciam

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. 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. Voice recognition allows you to talk to your robot devices. Image recognition is the key to self-driving cars. But what, exactly, is deep learning?

Dozens of articles tell you that it’s a complex, multilayered neural network. But they don’t really shed much light on deep learning’s seemingly magical powers. For example, to explain how it can recognize faces out of a matrix of pixel values (i.e., an image).

As 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. 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. In predicting insurance fraud, we might guess that policy age would be predictive.  .... "