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Deep learning (also known as deep structured learning) is a type of machine learning technology that uses artificial neural networks to learn representations. There are three types of learning: supervised, semi-supervised, and unsupervised.
Deep-learning architectures like deep neural networks, deep belief networks, recurrent neural networks, and convolutional neural networks have been used in fields like computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection, and board game programs, producing results that are comparable to and better than traditional methods.
Information processing and dispersed communication nodes in biological systems inspired artificial neural networks (ANNs). ANNs differ from biological brains in a number of ways. In particular, neural networks are static and symbolic, whereas most live animals' organic brains are dynamic (plastic) and analog.
Deep learning gets its name from the fact that it employs numerous levels in the network. Early research shown that a linear perceptron cannot be used as a universal classifier, but that a network with a nonpolynomial activation function and one unbounded width hidden layer can. Deep learning is a more recent form that has an unbounded number of layers of bounded size, allowing for practical application and optimization while maintaining theoretical universality under moderate conditions. For the sake of efficiency, trainability, and understandability, deep learning layers are also allowed to be heterogeneous and stray substantially from biologically informed connectionist models, hence the "structured" portion.
Deep Learning is a large field with a lot of knowledge, it has a lot of real-world applications, and we know it's the future, along with programming, artificial intelligence, and Machine Learning with data science. I hope you like this article and that you will share it with your friends and follow us on social media!