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Alexander Bain (11 June 1818 – 18 September 1903) was a Scottish philosopher and educationalist in the British school of empiricism and a prominent and innovative figure in the fields of psychology, linguistics, logic, moral philosophy and education reform.
The preliminary theoretical base for contemporary neural networks was independently proposed by Alexander Bain [4] (1873) and William James [5] (1890). In their work, both thoughts and body activity resulted from interactions among neurons within the brain.
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.
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 ...
The artificial neuron is the elementary unit of an artificial neural network. [1] The design of the artificial neuron was inspired by biological neural circuitry. Its inputs are analogous to excitatory postsynaptic potentials and inhibitory postsynaptic potentials at neural dendrites, or activation.
A learning algorithm is such that, over time, a change in connection weight is possible, allowing networks to modify their connections. Connectionist neural networks are able to change over time via their activation. An activation is a numerical value that represents any aspect of a unit that a neural network has at any time.
Neural networks follow two basic principles: Any mental state can be described as a n-dimensional vector of numeric activation values over neural units in a network. Memory and learning are created by modifying the 'weights' of the connections between neural units, generally represented as an n×m matrix.
Video: as the width of the network increases, the output distribution simplifies, ultimately converging to a Neural network Gaussian process in the infinite width limit. Artificial neural networks are a class of models used in machine learning, and inspired by biological neural networks. They are the core component of modern deep learning ...