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Что (кто) такое neural network - определение
COMPUTATIONAL MODEL USED IN MACHINE LEARNING, BASED ON CONNECTED, HIERARCHICAL FUNCTIONS
Neural net; Neural nets; Massive neural network; Neuralnets; Neuralnet; Artificial neural networs; Artificial neural networks; Artificial Neural Networks; Distributed representation; Nervous network; Nueral Network; Simulated neural network; Artificial Neural Network; Simulated Neural Network; Neural Network; Stochastic neural network; Present challenges in neural Networks; Problems in the verge of success in neural network research; Nervous Network; Bayesian neural network; Nueral networks; Neural network processor; Neural network processors; Neural computing; Neural circuitry; Neural Nets; Neural networks in robotics; Artificial neural net; Fuzzy neural networks; Neural network (computer); Neural network models; Neural network model; Models of neural network; Models of neural networks; Neural networks (computer); Aritificial Neuron Network; Computational network; History of Neural Networks; Convergent recursive learning algorithm; Deep stacking network; Tensor deep stacking network; Deep predictive coding networks; Applications of artificial neural networks; Neural network (artificial); Algorithms for training neural networks; Parameter (machine learning); Criticism of artificial neural networks; Self-learning in artificial neural network
.svg?width=200 "An ANN dependency graph")
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neural network
STRUCTURE IN BIOLOGY AND ARTIFICIAL INTELLIGENCE
Neural networks; History of neural networks; Applications of neural networks
neural network
STRUCTURE IN BIOLOGY AND ARTIFICIAL INTELLIGENCE
Neural networks; History of neural networks; Applications of neural networks
(neural networks)
In computing, a neural network is a program or system which is modelled on the human brain and is designed to imitate the brain's method of functioning, particularly the process of learning.
N-COUNT
neural network
STRUCTURE IN BIOLOGY AND ARTIFICIAL INTELLIGENCE
Neural networks; History of neural networks; Applications of neural networks
(also neural net)
¦ noun a computer system modelled on the human brain and nervous system.
Neural network
STRUCTURE IN BIOLOGY AND ARTIFICIAL INTELLIGENCE
Neural networks; History of neural networks; Applications of neural networks
A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological neurons, or an artificial neural network, used for solving artificial intelligence (AI) problems.
Artificial neural network
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains.
artificial neural network
<artificial intelligence> (ANN, commonly just "neural network"
or "neural net") A network of many very simple processors
("units" or "neurons"), each possibly having a (small amount
of) local memory. The units are connected by unidirectional
communication channels ("connections"), which carry numeric
(as opposed to symbolic) data. The units operate only on
their local data and on the inputs they receive via the
connections.
A neural network is a processing device, either an
algorithm, or actual hardware, whose design was inspired by
the design and functioning of animal brains and components
thereof.
Most neural networks have some sort of "training" rule whereby
the weights of connections are adjusted on the basis of
presented patterns. In other words, neural networks "learn"
from examples, just like children learn to recognise dogs from
examples of dogs, and exhibit some structural capability for
generalisation.
Neurons are often elementary non-linear signal processors (in
the limit they are simple threshold discriminators). Another
feature of NNs which distinguishes them from other computing
devices is a high degree of interconnection which allows a
high degree of parallelism. Further, there is no idle memory
containing data and programs, but rather each neuron is
pre-programmed and continuously active.
The term "neural net" should logically, but in common usage
never does, also include biological neural networks, whose
elementary structures are far more complicated than the
mathematical models used for ANNs.
See Aspirin, Hopfield network, McCulloch-Pitts neuron.
Usenet newsgroup: news:comp.ai.neural-nets.
(1997-10-13)
Neural network (disambiguation)
WIKIMEDIA DISAMBIGUATION PAGE
Neural Networks; Neural Networks (disambiguation)
Neural network refers to interconnected populations of neurons or neuron simulations that form the structure and architecture of nervous systems, in animals, humans, and computing systems:
neural nets
Convolutional neural network
![Comparison of the LeNet and AlexNet convolution, pooling and dense layers<br>(AlexNet image size should be 227×227×3, instead of 224×224×3, so the math will come out right. The original paper said different numbers, but Andrej Karpathy, the head of computer vision at Tesla, said it should be 227×227×3 (he said Alex didn't describe why he put 224×224×3). The next convolution should be 11×11 with stride 4: 55×55×96 (instead of 54×54×96). It would be calculated, for example, as: [(input width 227 - kernel width 11) / stride 4] + 1 = [(227 - 11) / 4] + 1 = 55. Since the kernel output is the same length as width, its area is 55×55.)](https://commons.wikimedia.org/wiki/Special:FilePath/Comparison image neural networks.svg?width=200 "Comparison of the LeNet and AlexNet convolution, pooling and dense layers<br>(AlexNet image size should be 227×227×3, instead of 224×224×3, so the math will come out right. The original paper said different numbers, but Andrej Karpathy, the head of computer vision at Tesla, said it should be 227×227×3 (he said Alex didn't describe why he put 224×224×3). The next convolution should be 11×11 with stride 4: 55×55×96 (instead of 54×54×96). It would be calculated, for example, as: [(input width 227 - kernel width 11) / stride 4] + 1 = [(227 - 11) / 4] + 1 = 55. Since the kernel output is the same length as width, its area is 55×55.)")
A CLASS OF DEEP NEURAL NETWORKS, MOST COMMONLY APPLIED TO ANALYZING VISUAL IMAGERY.
ConvNet; Convolutional Neural Network; Deep convolutional neural network; Convoluted neural network; Convolutional neural networks; Max pooling; DropConnect; Stochastic pooling; Max norm constraint; Convolutional neural net; Convolutional neural nets; Applications of convolutional neural networks; Convolutional layer; Pooling (neural networks); DCNN; CNN (machine learning model); Convnet
In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps.
Network: Computation in Neural Systems
BRITISH SCIENTIFIC JOURNAL FOCUSING ON COMPUTATIONAL NEUROSCIENCE
Network:Computation In Neural Systems; Netw Comput Neural Syst; Netw. Comput. Neural Syst.; Network: Computation In Neural Systems
Network: Computation in Neural Systems is a scientific journal that aims to provide a forum for integrating theoretical and experimental findings in computational neuroscience with a particular focus on neural networks. The journal is published by Taylor & Francis and edited by Dr Simon Stringer (University of Oxford).
Википедия
Artificial neural network
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains.
An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold.
Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.
