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Machine-learning technology powers many aspects of modern society: from web searches to content filtering on social networks to recommendations on e-commerce websites, and it is increasingly present in consumer products such as cameras and smartphones.Perhaps more surprisingly, deep learning has produced extremely promising results for various tasks in natural language understanding14, particularly topic classification, sentiment analysis, question answering15 and language translation16,17.For decades, constructing a pattern-recognition or machine-learning system required careful engineering and considerable domain expertise to design a feature extractor that transformed the raw data (such as the pixel values of an image) into a suitable internal representation or feature vector from which the learning subsystem, often a classifier, could detect or classify patterns in the input.

Machine-learning technology powers many aspects of modern
society: from web searches to content filtering on social networks to recommendations on e-commerce websites, and
it is increasingly present in consumer products such as cameras and
smartphones. Machine-learning systems are used to identify objects
in images, transcribe speech into text, match news items, posts or
products with users’ interests, and select relevant results of search.
Increasingly, these applications make use of a class of techniques called
deep learning.
Conventional machine-learning techniques were limited in their
ability to process natural data in their raw form. For decades, constructing a pattern-recognition or machine-learning system required
careful engineering and considerable domain expertise to design a feature extractor that transformed the raw data (such as the pixel values
of an image) into a suitable internal representation or feature vector
from which the learning subsystem, often a classifier, could detect or
classify patterns in the input.
Representation learning is a set of methods that allows a machine to
be fed with raw data and to automatically discover the representations
needed for detection or classification. Deep-learning methods are
representation-learning methods with multiple levels of representation, obtained by composing simple but non-linear modules that each
transform the representation at one level (starting with the raw input)
into a representation at a higher, slightly more abstract level. With the
composition of enough such transformations, very complex functions
can be learned. For classification tasks, higher layers of representation
amplify aspects of the input that are important for discrimination and
suppress irrelevant variations. An image, for example, comes in the
form of an array of pixel values, and the learned features in the first
layer of representation typically represent the presence or absence of
edges at particular orientations and locations in the image. The second
layer typically detects motifs by spotting particular arrangements of
edges, regardless of small variations in the edge positions. The third
layer may assemble motifs into larger combinations that correspond
to parts of familiar objects, and subsequent layers would detect objects
as combinations of these parts. The key aspect of deep learning is that
these layers of features are not designed by human engineers: they
are learned from data using a general-purpose learning procedure.
Deep learning is making major advances in solving problems that
have resisted the best attempts of the artificial intelligence community for many years. It has turned out to be very good at discovering
intricate structures in high-dimensional data and is therefore applicable to many domains of science, business and government. In addition
to beating records in image recognition1–4 and speech recognition5–7, it
has beaten other machine-learning techniques at predicting the activity of potential drug molecules8
, analysing particle accelerator data9,10,
reconstructing brain circuits11, and predicting the effects of mutations
in non-coding DNA on gene expression and disease12,13. Perhaps more
surprisingly, deep learning has produced extremely promising results
for various tasks in natural language understanding14, particularly
topic classification, sentiment analysis, question answering15 and language translation16,17.
We think that deep learning will have many more successes in the
near future because it requires very little engineering by hand, so it
can easily take advantage of increases in the amount of available computation and data. New learning algorithms and architectures that are
currently being developed for deep neural networks will only accelerate this progress