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Neural backpropagation is the phenomenon in which, after the action potential of a neuron creates a voltage spike down the axon (normal propagation), another impulse is generated from the soma and propagates towards the apical portions of the dendritic arbor or dendrites (from which much of the original input current originated).
In machine learning, backpropagation [1] is a gradient estimation method commonly used for training a neural network to compute its parameter updates. It is an efficient application of the chain rule to neural networks.
An artificial neural network (ANN) combines biological principles with advanced statistics to solve problems in domains such as pattern recognition and game-play. ANNs adopt the basic model of neuron analogues connected to each other in a variety of ways.
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In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. [1] [2] An ANN consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain. Artificial ...
Neural operators can be trained directly using backpropagation and gradient descent-based methods. Another training paradigm is associated with physics-informed machine learning. In particular, physics-informed neural networks (PINNs) use complete physics laws to fit neural networks to solutions of PDEs.
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A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or mathematical models . While individual neurons are simple, many of them together in a network can perform complex tasks.