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Some key aspects and applications of digital recognition using deep learning are as follows:

u Image Recognition: Image Recognition involves identifying and
classifying objects within images. For example, face recognition is the
task of comparing an input face to a database of multiple face identities.
This task is often used for security and surveillance systems. A good
example is facial recognition in law enforcement.
u Speech Recognition: Speech Recognition involves converting spoken
language into text. DL models have dramatically improved the accuracy and performance of speech
recognition systems. For example, speech recognition is widely used in systems like Google Assistant, Siri,
and Alexa to understand verbal commands.

u Optical Character Recognition: Digital Documentation is one of the prominent areas where deep learning
has made significant advancements in OCR accuracy, particularly in complex fonts and formatting.
Automation of the documentation process saves time and operational cost of businesses and enhance
customer satisfaction.

Knowledge Botwledge Bot
Kno
On the INTERPOL website, there is a forensics section which explains how they use facial recognition to
identify persons of interest at airports and border crossings.

Differences Between Artificial Intelligence, Machine Learning, and Deep Learning

In general, Artificial Intelligence is a branch of computer science that is made up of two main terminologies:
Machine Learning and Deep Learning. Machine Learning is a subfield of AI, whereas Deep Learning is the subfield
of Machine Learning.
Artificial Intelligence

AI Engineering of making intelligent
machines and programs
Machine Learning
ML Ability to learn without being
explicitly programmed
DL Deep Learning
Learning based on deep neural
networks

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