Цитаты из книги Deep Learning (Adaptive Computation and Machine Learning series)

Each piece of information included in the representation of the patient is known as a feature. Logistic regression learns how each of these features of the patient correlates with various outcomes.

This book is about a solution to these more intuitive problems. This solution isto allow computers to learn from experience and understand the world in terms ofa hierarchy of concepts, with each concept defined through its relation to simplerconcepts. <...>

If we draw a graph showing how these concepts are built on top of each other, the graph is deep, with many layers. For this reason,we call this approach to AI deep learning

A simple machine learning algorithm called logistic regressioncan determine whether to recommend cesarean delivery (Mor-Yosef et al., 1990). A simple machine learning algorithm called naive Bayes can separate legitimate e-mail from spam e-mail.

The difficulties faced by systems relying on hard-coded knowledge suggestthat AI systems need the ability to acquire their own knowledge, by extracting2CHAPTER 1. INTRODUCTIONpatterns from raw data. This capability is known asmachine learning.

IBM’s Deep Blue chess-playing system defeated worldchampion Garry Kasparov in 1997

Inventors have long dreamed of creating machines that think. This desire datesback to at least the time of ancient Greece. The mythical figures Pygmalion,Daedalus, and Hephaestus may all be interpreted as legendary inventors, andGalatea, Talos, and Pandora may all be regarded as artificial life (Ovid and Martin,2004; Sparkes, 1996; Tandy, 1997)