Assertion: artificial neural network | /r/HasProperty | ann
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Source sentences:
text: What Is an Artificial Neural Network (ANN)?
text: Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems.
text: Understanding an Artificial Neural Network (ANN)
text: Artificial Neural Network (ANN)
text: Artificial Neural Network (ANN) –
text: Challenges with Artificial Neural Network (ANN)
text: Backward Propagation of Errors, often abbreviated as BackProp is one of the several ways in which an artificial neural network (ANN) can be trained.
text: An artificial neural network (ANN) is the piece of a computing system designed to simulate the way the human brain analyzes and processes information.
text: An artificial neural network (ANN) is the component of artificial intelligence that is meant to simulate the functioning of a human brain.
text: A neural network (NN), in the case of artificial neurons called artificial neural network (ANN) or simulated neural network (SNN), is an interconnected group of natural or artificial neurons that uses a mathematical or computational model for information processing based on a connectionistic approach to computation.
text: In information technology (IT), an artificial neural network (ANN) is a system of hardware and/or software patterned after the operation of neurons in the human brain.
text: The human brain is a neural network made up of multiple neurons, similarly, an Artificial Neural Network (ANN) is made up of multiple perceptrons (explained later).
text: Consider a scenario where you are to build an Artificial Neural Network (ANN) that classifies images into two classes:
text: Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains.
text: A deep neural network (DNN) is an artificial neural network (ANN) with multiple layers between the input and output layers.
text: Artificial Neural Network (ANN) is an efficient computing system whose central theme is borrowed from the analogy of biological neural networks.
text: Before taking a look at the differences between Artificial Neural Network (ANN) and Biological Neural Network (BNN), let us take a look at the similarities based on the terminology between these two.
text: Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains.
text: An artificial neural network (ANN) is a computational nonlinear model based on the neural structure of the brain that is able to learn to perform tasks like classification, prediction, decision-making, visualization, and others just by considering examples.
text: Definition - What does Artificial Neural Network (ANN) mean?
text: Artificial neural networks (ANN) is the key tool of machine learning.
text: A Capsule Neural Network (CapsNet) is a machine learning system that is a type of artificial neural network (ANN) that can be used to better model hierarchical relationships.
text: The different types of neural networks in deep learning, such as convolutional neural networks (CNN), recurrent neural networks (RNN), artificial neural networks (ANN), etc. are changing the way we interact with the world.
text: Using this concept from the biological neural network, an Artificial Neural Network (ANN), also simply known as the neural network, is formed.
text: This imitation is more commonly known as an artificial neural network (ANN) and consists of many layers of neurons to accurately process a large amount of information.
text: It refers to artificial neural networks (ANN) that are composed of many layers, massive data sets, powerful computer hardware to make complicated training model possible.
text: Similar to this, an Artificial Neural Network (ANN) is a computational network in science that resembles the characteristics of a human brain.
text: An Artificial Neural Network (ANN) is a computational model that is inspired by the way biological neural networks in the human brain process information.
text: An artificial neural network (ANN) is a complex structure, but its main purpose is to calculate complex processes rapidly and efficiently, just like a human neural network.
text: When people talk about artificial intelligence and machine learning, they most often refer to (artificial) neural networks (ANN or NN).
text: Artificial Neural networks (ANN) or neural networks are computational algorithms.
text: Therefore, these mathematically inter-connected formulae are known as artificial neural network (ANN).
text: One answer is to use an artificial neural network (ANN), a computing system that can learn on its own.
text: Artificial intelligence (AI) and artificial neural networks (ANN) are two exciting and intertwined fields in computer science.
text: An artificial neural network (ANN) is a type of artificial intelligence computer system, the design for which has been inspired by the biological structure found in an animal brain.
text: An Artificial Neural Network (ANN) is a computational model that is inspired by the way biological neural networks in the human brain process information.
text: Artificial neural network (ANN) is a network of efficient computing systems the central theme of which is borrowed from the analogy of biological neural networks.
text: Inspired by the way biological nervous systems work, Artificial Neural Network (ANN) will be one of the most important information processing paradigm since the invention of the computer.
text: An artificial neural network (ANN) is a data preparing worldview, which is roused by the organic sensory system, for example, the cerebrum, process data.
text: An Artificial Neural Network (ANN) is an information processing paradigm that is inspired the brain.
text: A neural network (NN), in the case of artificial neurons called artificial neural network (ANN) or simulated neural network (SNN), is an interconnected group of natural or artificial neurons that uses a mathematical or computational model for information processing based on a connectionistic approach to computation.
text: Artificial neural networks (ANNs) or connectionist systems are computing systems inspired by the biological neural networks that constitute animal brains.
text: Artificial neural networks (ANNs or simply “neural networks” for short) refer to a specific type of learning model that emulates the way synapses work in your brain.
text: Artificial neural networks (ANNs) becomes very popular tool in hydrology, especially in rainfall-runoff modelling…
text: Generalizations of backpropagation exist for other artificial neural networks (ANNs), and for functions generally – a class of algorithms referred to generically as "backpropagation".
text: Artificial neural networks (ANNs) are statistical models directly inspired by, and partially modeled on biological neural networks.
text: Artificial neural networks (ANNs) and the more complex deep learning technique are some of the most capable AI tools for solving very complex problems, and will continue to be developed and leveraged in the future.
text: Artificial neural networks (ANNs) and the more complex deep learning technique are some of the most capable AI tools for solving very complex problems and will continue to be developed and leveraged in the future.
text: Artificial neural networks (ANNs for short) may provide the answer to this.
text: In artificial neural networks (ANNs), the basis for deep learning models, each layer may be assigned a specific portion of a transformation task, and data might traverse the layers multiple times to refine and optimize the ultimate output.
text: Artificial neural networks (ANNs) are a class of artificial intelligence algorithms that emerged in the 1980s from developments in cognitive and computer science research.