Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

In 2006, Geoffrey Hinton et al. published a paper1 showing how to train a deep neural
network capable of recognizing handwritten digits with state-of-the-art precision
(>98%). They branded this technique “Deep Learning.” A deep neural network is a
(very) simplified model of our cerebral cortex, composed of a stack of layers of artificial
neurons. Training a deep neural net was widely considered impossible at the
time,2 and most researchers had abandoned the idea in the late 1990s. This paper
revived the interest of the scientific community, and before long many new papers
demonstrated that Deep Learning was not only possible, but capable of mindblowing
achievements that no other Machine Learning (ML) technique could hope to
match (with the help of tremendous computing power and great amounts of data).
This enthusiasm soon extended to many other areas of Machine Learning.
A decade or so later, Machine Learning has conquered the industry: it is at the heart
of much of the magic in today’s high-tech products, ranking your web search results,
powering your smartphone’s speech recognition, recommending videos, and beating
the world champion at the game of Go. Before you know it, it will be driving your car.