Deep Learning Practical-Neural Network Projects Bootcamp2021
A newly re-invigorated form of machine learning, which is itself a subset of artificial intelligence, deep learning employs powerful computers, massive data sets, “supervised” (trained) neural networks and an algorithm called back-propagation (backprop for short) to recognize objects and translate speech in real time by mimicking the layers of neurons in a human brain’s neocortex.
Deep learning (sometimes known as deep structured learning) is a subset of machine learning, where machines employ artificial neural networks to process information. Inspired by biological nodes in the human body, deep learning helps computers to quickly recognize and process images and speech. Computers then “learn” what these images or sounds represent and build an enormous database of stored knowledge for future tasks. In essence, deep learning enables computers to do what humans do naturally- learn by immersion and example.
Deep learning has been around since the 1950s, but its elevation to star player in the artificial intelligence field is relatively recent. In 1986, pioneering computer scientist Geoffrey Hinton — now a Google researcher and long known as the “Godfather of Deep Learning” — was among several researchers who helped make neural networks cool again, scientifically speaking, by demonstrating that more than just a few of them could be trained using backpropagation for improved shape recognition and word prediction. By 2012, deep learning was being used in everything from consumer applications like Apple’s Siri to pharmaceutical research.
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