Deep learning is a machine learning technique that teaches computers to do what comes naturally to people. It is an important technology that allows to define or distinguish deep learning. It is the key to volume control in consumer devices such as phones, tablets, TVs and handsfree speakers. Deep learning has been getting a lot of attention lately and for good reason. It achieves results that were not possible before.

In deep learning, a computer model learns to perform classification tasks directly from images, text or sound. Deep learning models can achieve cutting-edge accuracy, sometimes exceeding human-level performance. Models are trained using neural network architectures with a large labeled data set and many layers.


Why is Deep Learning Important?

How does deep learning achieve such impressive results?

In one word, accuracy. Deep learning provides higher levels of recognition accuracy than ever before. This helps consumer electronics meet user expectations and is essential for safety-critical applications such as driverless cars. Recent advances in deep learning have improved to the point where deep learning performs better than humans in some tasks, such as classifying objects in images.

While deep learning was first theorized in the 1980s, there are two main reasons why it has become useful recently:

Deep learning requires a large amount of tagged data. For example, driverless vehicle development requires millions of images and thousands of hours of video.

Deep learning requires significant computing power. High performance GPUs have an efficient parallel architecture for deep learning. Combined with clusters or cloud computing, this allows development teams to reduce training time from weeks to hours or less for a deep learning network.


Here are Deep Learning Examples

Deep learning applications are used in industries from automatic driving to medical devices.

Auto Driving: Automotive researchers use the deep learning method to automatically detect objects such as stop signs and traffic lights. Also, deep learning is used to detect pedestrians that help reduce accidents.

Aviation and Defense: Deep learning is used to identify objects from satellites that find their interests and to identify safe or unsafe areas for troops.

Medical Research: Cancer researchers use the deep learning method to automatically detect cancer cells. Teams at UCLA have developed an advanced microscope that provides a high-dimensional dataset used to train a deep learning practice to accurately identify cancer cells.

Industrial Automation: Deep learning helps to increase worker safety around heavy machinery by automatically detecting that people or objects are located at an insecure distance from machines.

Electronics: Used for deep learning, automatic hearing and speech translation. For example, home aid devices that respond to your voice and know your preferences are supported by deep learning practices.

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