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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 ...
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.
Each DSN block is a simple module that is easy to train by itself in a supervised fashion without backpropagation for the entire blocks. [8] Each block consists of a simplified multi-layer perceptron (MLP) with a single hidden layer. The hidden layer h has logistic sigmoidal units, and the output layer has linear units.
Schematic of a simple feedforward artificial neural network In machine learning, a neural network is an artificial mathematical model used to approximate nonlinear functions. While early artificial neural networks were physical machines, [ 3 ] today they are almost always implemented in software .
Logistic activation function. The activation function of a node in an artificial neural network is a function that calculates the output of the node based on its individual inputs and their weights.
Perceptrons can be trained by a simple learning algorithm that is usually called the delta rule. It calculates the errors between calculated output and sample output data, and uses this to create an adjustment to the weights, thus implementing a form of gradient descent .
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As a simple example, consider a single neuron with threshold 0, and a single inhibitory self-loop. Its output would oscillate between 0 and 1 at every step, acting as a "clock". Any finite state machine can be simulated by a MCP neural network. [4] Furnished with an infinite tape, MCP neural networks can simulate any Turing machine. [5]