VIDEOS 1 TO 50

Sigmoid - Georgia Tech - Machine Learning

Published: 2015/02/23

Channel: Udacity

Neural networks [1.2] : Feedforward neural network - activation function

Published: 2013/11/15

Channel: Hugo Larochelle

The Sigmoid Curve

Published: 2012/11/11

Channel: Pete Dalgleish

Sigmoid function

Published: 2014/08/16

Channel: Audiopedia

Julia Programming : The Sigmoid Function Programming Exercise

Published: 2014/03/02

Channel: Stats-Lab Dublin

Mathematical device used in Deep Learning V - Sigmoid function

Published: 2015/10/23

Channel: Vasu Srinivasan

Intro to Neural Networks

Published: 2013/06/10

Channel: Michael Zibulevsky

Derivative of the sigmoid activation function, 9/2/2015

Published: 2015/02/10

Channel: Lutfi Al-Sharif

Normalised Tunable Sigmoid Function 2.0

Published: 2013/11/24

Channel: Dino Dini

The Gompertz Sigmoid Function and Its Derivative

Published: 2009/07/16

Channel: wolframmathematica

The Gompertz Sigmoid Function and Its Derivative

Published: 2010/04/13

Channel: wolframmathematica

Impact of Bias on the Sigmoid Activation function

Published: 2014/10/19

Channel: N RS

Sigmoid function displacement time servo control

Published: 2009/05/30

Channel: Devraj Joshi

Using the Flexible Spline function (FlexSpline) in Excel

Published: 2013/11/13

Channel: SRS1Software

Normalised Tunable Sigmoid Function Demo in Unity3D

Published: 2013/06/26

Channel: Dino Dini

Mathematical Biology. 19: Sigmoidal Functions, Multisite Systems

Published: 2014/02/25

Channel: UCI Open

Contrast Enhancement of Color Images using Tunable Sigmoid Function.wmv

Published: 2011/03/16

Channel: VERILOG COURSE TEAM

免費統計教學範例39 Sigmoidal Function Fit S曲線回歸

Published: 2013/05/08

Channel: 全傑科技

Logistic function

Published: 2014/08/13

Channel: Audiopedia

Neural network tutorial: The back-propagation algorithm (Part 1)

Published: 2012/01/07

Channel: Ryan Harris

5.3.2 Draw and label a graph showing a sigmoid (S-shaped) population growth curve

Published: 2013/04/05

Channel: Stephanie Castle

딥러닝 2. How Artificial Neurons Work (한국어)

Published: 2016/04/30

Channel: J Hong

Neural Network Learning - Visualization - 1

Published: 2015/12/30

Channel: Christopher Gondek

SIGMOID COLON : DR SAMEH GHAZY

Published: 2014/06/11

Channel: Sameh Ghazy

Neural networks [2.5] : Training neural networks - activation function derivative

Published: 2013/11/15

Channel: Hugo Larochelle

Derivatives of Exponential Functions

Published: 2008/11/29

Channel: patrickJMT

Plot 5 of 6 - Continuous A* - Obstacle Created Using the Product of Sigmoid Functions

Published: 2008/11/12

Channel: joshuaburkholder

Colon Resection

Published: 2010/12/13

Channel: Nucleus Medical Media

Neural Network Learning - Visualization - 2

Published: 2015/12/30

Channel: Christopher Gondek

Curve Fitting with Microsoft Excel

Published: 2013/05/07

Channel: Randy Thomas

Neural Network Part 2

Published: 2010/10/14

Channel: iecbusinessnoida

Neural networks [2.2] : Training neural networks - loss function

Published: 2013/11/15

Channel: Hugo Larochelle

Machine Learning: Lecture 8, Chapter 10

Published: 2016/04/06

Channel: Rutgers Accounting Web

Developing neural network in MATLAB method2 nntool] [fitting tool]

Published: 2013/07/23

Channel: Nagarjuna A

Spiral Weaving Time

Published: 2013/08/24

Channel: SweetHyunho

Wind shear perturbation in AeoLiS: berm, dune ridge and bump

Published: 2016/04/01

Channel: Bas Hoonhout

Plot 6 of 6 - Continuous A* - Simple Maze

Published: 2008/11/12

Channel: joshuaburkholder

Logistic regression

Published: 2011/11/12

Channel: jsantarc

Introduction to Neural Networks for C#(Class 4/16, Part 2/5) - activation function

Published: 2009/02/07

Channel: Jeff Heaton

5.1 Loss Functions | 5 Support Vector Machines | Pattern Recognition Class 2012

Published: 2012/11/22

Channel: UniHeidelberg

Piecewise Linear Functions - ENGAGE NY Algebra I: Module 1: Lesson 1

Published: 2014/01/23

Channel: Tarver Academy Math

Colon, Rectum, Anus

Published: 2014/05/15

Channel: khanacademymedicine

Retro Sigmoid Vestibular Nerve Section for Meniere's Disease

Published: 2009/07/02

Channel: KKRENTHospital

MATLAB - How to write a function for curve fitting (Quick Step by Step)

Published: 2015/09/29

Channel: atombart1

[Ca2+] Waves in Smooth Muscle Cells: Coupling Case 3

Published: 2015/09/07

Channel: University of Canterbury High Performance Computing

Mod-08 Lec-26 Multilayer Feedforward Neural networks with Sigmoidal activation functions;

Published: 2013/12/02

Channel: nptelhrd

Switching PD-Based Sliding Mode Control for Hovering of a Tilting-Thruster Underwater Robot

Published: 2016/03/23

Channel: Sangrok Jin

Oxygen Hemoglobin Dissociation Curve Explained Clearly

Published: 2012/12/16

Channel: MEDCRAMvideos

Developing neural network in MATLAB method1 command window] [fitting tool]

Published: 2013/07/23

Channel: Nagarjuna A

A Neural Network Learning his name

Published: 2014/04/29

Channel: Dwight Naylor

From Wikipedia, the free encyclopedia

This article needs additional citations for verification. (May 2008) (Learn how and when to remove this template message) |

A **sigmoid function** is a mathematical function having an "S" shaped curve (**sigmoid curve**). Often, *sigmoid function* refers to the special case of the logistic function shown in the first figure and defined by the formula

Other examples of similar shapes include the Gompertz curve (used in modeling systems that saturate at large values of t) and the ogee curve (used in the spillway of some dams). A wide variety of sigmoid functions have been used as the activation function of artificial neurons, including the logistic and hyperbolic tangent functions. Sigmoid curves are also common in statistics as cumulative distribution functions, such as the integrals of the logistic distribution, the normal distribution, and Student's *t* probability density functions.

A sigmoid function is a bounded differentiable real function that is defined for all real input values and has a positive derivative at each point.^{[1]}

In general, a sigmoid function is real-valued and differentiable, having either a non-negative or non-positive first derivative^{[citation needed]} which is bell shaped. There are also a pair of horizontal asymptotes as . The differential equation , with the inclusion of a boundary condition providing a third degree of freedom, , provides a class of functions of this type.

The logistic function has this further, important property, that its derivative can be expressed by the function itself,

Many natural processes, such as those of complex system learning curves, exhibit a progression from small beginnings that accelerates and approaches a climax over time. When a detailed description is lacking, a sigmoid function is often used^{[2]} .

Besides the logistic function, sigmoid functions include the ordinary arctangent, the hyperbolic tangent, the Gudermannian function, and the error function, but also the generalised logistic function and algebraic functions like .

The integral of any smooth, positive, "bump-shaped" function will be sigmoidal, thus the cumulative distribution functions for many common probability distributions are sigmoidal. The most famous such example is the error function, which is related to the cumulative distribution function (CDF) of a normal distribution.

Wikimedia Commons has media related to .Sigmoid functions |

- Cumulative distribution function
- Generalized logistic curve
- Gompertz function
- Heaviside step function
- Hyperbolic function
- Logistic distribution
- Logistic function
- Logistic regression
- Logit
- Modified hyperbolic tangent
- Softplus function
- Smoothstep function (Graphics)
- Softmax function
- Weibull distribution
- Netoid function

**^**Han, Jun; Morag, Claudio (1995). "The influence of the sigmoid function parameters on the speed of backpropagation learning". In Mira, José; Sandoval, Francisco.*From Natural to Artificial Neural Computation*. pp. 195–201.**^**Gibbs, M.N. (Nov 2000). "Variational Gaussian process classifiers".*IEEE Transactions on Neural Networks***11**(6): 1458–1464. doi:10.1109/72.883477.

- Mitchell, Tom M. (1997).
*Machine Learning*. WCB–McGraw–Hill. ISBN 0-07-042807-7.. In particular see "Chapter 4: Artificial Neural Networks" (in particular pp. 96–97) where Mitchell uses the word "logistic function" and the "sigmoid function" synonymously – this function he also calls the "squashing function" – and the sigmoid (aka logistic) function is used to compress the outputs of the "neurons" in multi-layer neural nets. - Humphrys, Mark. "Continuous output, the sigmoid function". Properties of the sigmoid, including how it can shift along axes and how its domain may be transformed.

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