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- Activation Functions in Neural Networks
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Such a bidirectional-arrow notation is frequently used for describing the probability current of quantum mechanics. The differential operator del, also called nabla operator , is an important vector differential operator.
It appears frequently in physics in places like the differential form of Maxwell's equations. In three-dimensional Cartesian coordinates , del is defined:.
Del is used to calculate the gradient , curl , divergence , and Laplacian of various objects. This definition therefore depends on the definition of the scalar product. In the functional space of square-integrable functions on a real interval a , b , the scalar product is defined by. This formula does not explicitly depend on the definition of the scalar product.
It is therefore sometimes chosen as a definition of the adjoint operator.
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A formally self-adjoint operator is an operator equal to its own formal adjoint. The Sturm—Liouville operator is a well-known example of a formal self-adjoint operator. This second-order linear differential operator L can be written in the form. This operator is central to Sturm—Liouville theory where the eigenfunctions analogues to eigenvectors of this operator are considered. Differentiation is linear , i. Any polynomial in D with function coefficients is also a differential operator. We may also compose differential operators by the rule.
Some care is then required: firstly any function coefficients in the operator D 2 must be differentiable as many times as the application of D 1 requires.
To get a ring of such operators we must assume derivatives of all orders of the coefficients used. Secondly, this ring will not be commutative : an operator gD isn't the same in general as Dg.
Activation Functions in Neural Networks
In fact we have for example the relation basic in quantum mechanics :. The subring of operators that are polynomials in D with constant coefficients is, by contrast, commutative. Zotero Mendeley EndNote. Chui and L. Wuytack editors , Elservier Publ. New York, USA, Ortega, W. Argyros, On the Secant method, Publ. Debrecen, 43 , Potra, V. Kurchatov, On the method of linear interpolation for the solution of functional equations, Russion Dolk. Argyros, On the two point Newton-like methods of convergent R-order two, Int.
Range : -infinity to infinity. The Nonlinear Activation Functions are the most used activation functions. Nonlinearity helps to makes the graph look something like this. It makes it easy for the model to generalize or adapt with variety of data and to differentiate between the output. The main terminologies needed to understand for nonlinear functions are:.
Derivative or Differential: Change in y-axis w.
It is also known as slope. Monotonic function: A function which is either entirely non-increasing or non-decreasing. The Nonlinear Activation Functions are mainly divided on the basis of their range or curves -. The Sigmoid Function curve looks like a S-shape.
The main reason why we use sigmoid function is because it exists between 0 to 1. Therefore, it is especially used for models where we have to predict the probability as an output. Since probability of anything exists only between the range of 0 and 1, sigmoid is the right choice.
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The function is differentiable. That means, we can find the slope of the sigmoid curve at any two points. The logistic sigmoid function can cause a neural network to get stuck at the training time. The softmax function is a more generalized logistic activation function which is used for multiclass classification. The range of the tanh function is from -1 to 1. The advantage is that the negative inputs will be mapped strongly negative and the zero inputs will be mapped near zero in the tanh graph.
The function is monotonic while its derivative is not monotonic. The tanh function is mainly used classification between two classes. Both tanh and logistic sigmoid activation functions are used in feed-forward nets. The ReLU is the most used activation function in the world right now. Since, it is used in almost all the convolutional neural networks or deep learning.