Feedforward neural networkA feedforward neural network is an artificial neural network in which information flows in a single direction – inputs are multiplied by weights to obtain outputs (inputs-to-output). It contrasts with a recurrent neural network, in which loops allow information from later processing stages to feed back to earlier stages.BackpropagationIn machine learning, backpropagation is a gradient computation method commonly used for training a neural network in computing parameter updates. It is an efficient application of the chain rule to neural networks. Backpropagation efficiently computes the gradient of the loss with respect to the network weights for a single input–output example.
Deep learningBranch of machine learningRepresenting images on multiple layers of abstraction in deep learningIn machine learning, deep learning (DL) focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and revolves around stacking artificial neurons into layers and "training" them to process data.
Neural network (machine learning)Computational model used in machine learningA neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain. Here, each blue/green circular node in the hidden and output layers represents an artificial neuron and each red circular node in the far left layer represents an input data value.Perceptron
Activation functionArtificial neural network node functionLogistic activation functionIn artificial neural networks, the activation function of a node is a function that calculates the output of the node based on its individual inputs and their weights. Nontrivial problems can be solved using only a few nodes if the activation function is nonlinear.
Linear separabilityIn Euclidean geometry, linear separability is a property of two sets of points. This is most easily visualized in two dimensions (the Euclidean plane) by thinking of one set of points as being colored blue and the other set of points as being colored red. These two sets are linearly separable if there exists at least one line in the plane with all of the blue points on one side of the line and all the red points on the other side.Heaviside step functionSigmoid functionA sigmoid function is any mathematical function whose graph has a characteristic S-shaped or sigmoid curve. A common example of a sigmoid function is the logistic function. Other sigmoid functions are given in the Examples section. In some fields, most notably in the context of artificial neural networks, the term "sigmoid function" is used as a synonym for "logistic function". Special cases of sigmoid functions include the Gompertz curve (used in modeling systems that satur…Continuous functionIn mathematics, a continuous function is a function such that a small variation of its argument induces at most a small variation of its value. This implies there are no abrupt changes in value, known as discontinuities. More precisely, a function is continuous if arbitrarily small changes in its value can be assured by restricting its argument to sufficiently small changes. A discontinuous function is a function that is not continuous.
Rectified linear unitIn the context of artificial neural networks, the rectifier or ReLU (rectified linear unit) activation function is an activation function defined as the non-negative part of its argument, i.e., the ramp function: ReLU(x)=x+=max(0,x)=x+|x|2={xif x>0,0x≤0{\displaystyle \operatorname {ReLU} (x)=x^{+}=\max(0,x)={\frac {x+|x|}{2}}={\begin{cases}x&{\text{if }}x>0,\\0&x\leq 0\end{cases}}}where x{\displaystyle x} is the input to a neuron.
Hyperbolic functionsIn mathematics, hyperbolic functions are analogues of the ordinary trigonometric functions, but defined using the hyperbola rather than the circle. Just as the points (cos t, sin t) form a circle with a unit radius, the points (cosh t, sinh t) form the right half of the unit hyperbola. Also, similarly to how the derivatives of sin(t) and cos(t) are cos(t) and –sin(t), the derivatives of sinh(t) and cosh(t) are cosh(t) and sinh(t).*As an Amazon Associate I earn from qualifying purchases.