  Deep learning provides powerful methods to impute structured information from
large-scale, unstructured text and image datasets. For example, economists
might wish to detect the presence of economic activity in satellite images, or
to measure the topics or entities mentioned in social media, the congressional
record, or firm filings. This review introduces deep neural networks, covering
methods such as classifiers, regression models, generative AI, and embedding
models. Applications include classification, document digitization, record
linkage, and methods for data exploration in massive scale text and image
corpora. When suitable methods are used, deep learning models can be cheap to
tune and can scale affordably to problems involving millions or billions of
data points.. The review is accompanied by a companion website, EconDL, with
user-friendly demo notebooks, software resources, and a knowledge base that
provides technical details and additional applications.
