ai what is deep learning?
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Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.
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Now a ubiquitous part of modern society, AI refers to any machine that is able to replicate human cognitive skills, such as problem solving. ... In recent years, neural networks have made a comeback, particularly for a form of machine learning called deep learning, which can use very large, complex neural networks.
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Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is AI, but not all AI is machine learning, and so forth.
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Deep learning applications are used in industries from automated driving to medical devices. Automated Driving: Automotive researchers are using deep learning to automatically detect objects such as stop signs and traffic lights. In addition, deep learning is used to detect pedestrians, which helps decrease accidents.
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GPUs are optimized for training artificial intelligence and deep learning models as they can process multiple computations simultaneously. ... Additionally, computations in deep learning need to handle huge amounts of data — this makes a GPU's memory bandwidth most suitable.
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Artificial intelligence is sometimes called machine intelligence, and it refers to the utilization of computer capacities as against natural intelligence. It involves designing computers that mimic human intelligence in speech recognition, learning, reasoning, planning, etc.
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Machine Learning (ML) is commonly used alongside AI but they are not the same thing. ML is a subset of AI. ... Deep Learning (DL) is ML but applied to large data sets. Most AI work now involves ML because intelligent behavior requires considerable knowledge, and learning is the easiest way to get that knowledge.
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AI is a subset of advanced analytics, so advanced analytics is broader than AI when it comes to analytics capabilities. ... AI often involves the development of self-learning algorithms which enable analytics tools to learn from and make assumptions based on data.
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Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing.
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5 Answers. Deep Learning is the branch of Machine Learning based on Deep Neural Networks (DNNs), meaning neural networks with at the very least 3 or 4 layers (including the input and output layers). ... But you can build a shallow CNN for a simple task, in which case it's not (really) Deep Learning.
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Artificial Intelligence is a branch of computer science that focuses on creating intelligent machines which work as human beings. ... Most of the key aspects of machine learning are covered in computer science. An AI machine is capable of using its inputs to deduce key aspects of the problem.
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Despite its widespread lack of familiarity, AI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision-making.
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Is artificial intelligence the future of computer science? Certainly. ... AI Computer Science is to be about world's data computation and processes, as intelligent programs and smart algorithms, to manipulate, store, interpret, learn, understand and communicate digital information.
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To recap the differences between the two: Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. Deep learning structures algorithms in layers to create an "artificial neural network” that can learn and make intelligent decisions on its own.
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As IT operations become agile and dynamic, they are also getting immensely complex. The human mind is no longer capable of keeping up with the velocity, volume, and variety of Big Data streaming through daily operations, making AI a powerful and essential tool for optimizing the analyzing and decision-making processes.
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Deep-belief networks are used to recognize, cluster and generate images, video sequences and motion-capture data. A continuous deep-belief network is simply an extension of a deep-belief network that accepts a continuum of decimals, rather than binary data.
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GPUs are optimized for training artificial intelligence and deep learning models as they can process multiple computations simultaneously. They have a large number of cores, which allows for better computation of multiple parallel processes.