The Methodology Behind Deep Learning

Deep Learning is a subset of machine learning that deals with algorithms called artificial neural networks inspired by the brain's structure and function. Most of us have been using numerous applications and tools that exploit deep learning.  For instance, on consumer devices such as phones, laptops, TVs, and hands-free speakers, it is the key to voice control. Lately, and with good cause, deep learning is receiving a lot of attention. It's achieving outcomes that were previously not possible.

In deep learning, a computer model learns directly from images, text, or sound to carry out classification tasks. Deep learning models, sometimes exceeding human-level performance, can achieve state-of-the-art accuracy. models are trained By using a wide collection of labeled data and neural network architectures that include several layers.

How it Works:

Neural network architectures are used for most deep learning approaches, which is why deep learning models are sometimes referred to as deep neural networks.

The word "deep" commonly refers to the amount of layers in the neural network that are concealed. Just 2-3 hidden layers are used in standard neural networks, while deep networks may have as many as 150.

By using large sets of labeled data and neural network architectures that learn features directly from the data without the need for manual extraction of features, deep learning models are equipped. Following is a diagrammatic representation of a neural network.

 

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Some applications of deep learning:

Self driving: Deep learning is used by automobile researchers to detect objects such as stop signs and traffic signals automatically. In addition, deep learning is exploited  to identify pedestrians and minimize injuries.

Medical Research: Deep learning is used by cancer researchers to detect cancer cells automatically. UCLA teams have developed an advanced microscope that yields a high-dimensional data set used to train a deep learning application to identify cancer cells accurately.

Industrial Automation: By automatically identifying whether humans or items are within a dangerous distance from equipment, deep learning aims to improve workplace safety around heavy machinery.

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Ashpreet Kaur - Jul 2, 2021

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