# AI News, Artificial Neural Networks/Activation Functions

- On Thursday, October 4, 2018
- By Read More

## Artificial Neural Networks/Activation Functions

There are a number of common activation functions in use with neural networks.

The output is a certain value, A1, if the input sum is above a certain threshold and A0 if the input sum is below a certain threshold.

These kinds of step activation functions are useful for binary classification schemes.

In other words, when we want to classify an input pattern into one of two groups, we can use a binary classifier with a step activation function.

Each identifier would be a small network that would output a 1 if a particular input feature is present, and a 0 otherwise.

Combining multiple feature detectors into a single network would allow a very complicated clustering or classification problem to be solved.

linear combination is where the weighted sum input of the neuron plus a linearly dependant bias becomes the system output.

In these cases, the sign of the output is considered to be equivalent to the 1 or 0 of the step function systems, which enables the two methods be to equivalent if

This is called the log-sigmoid because a sigmoid can also be constructed using the hyperbolic tangent function instead of this relation, in which case it would be called a tan-sigmoid.

Sigmoid functions in this respect are very similar to the input-output relationships of biological neurons, although not exactly the same.

Sigmoid functions are also prized because their derivatives are easy to calculate, which is helpful for calculating the weight updates in certain training algorithms.

The softmax activation function is useful predominantly in the output layer of a clustering system.

- On Thursday, June 27, 2019

**Activation Functions in Neural Networks (Sigmoid, ReLU, tanh, softmax)**

ActivationFunctions #ReLU #Sigmoid #Softmax #MachineLearning Activation Functions in Neural Networks are used to contain the output between fixed values ...

**Neural Network Calculation (Part 2): Activation Functions & Basic Calculation**

From In this part we see how to calculate one section of a neural network. This calculation will be repeated many times to ..

**Deep Learning with Tensorflow - Activation Functions**

Enroll in the course for free at: Deep Learning with TensorFlow Introduction The majority of data ..

**Activation Functions**

In a neural network, the output value of a neuron is almost always transformed in some way using a function. A trivial choice would be a linear transformation ...

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

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

Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IISc Bangalore. For more details on NPTEL visit ...

**Impact of Bias on the Sigmoid Activation function**

**Lecture 4.3 — The softmax output function [Neural Networks for Machine Learning]**

For cool updates on AI research, follow me at Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey ..

**Lec-4 Nonlinear Activation Units and Learning Mechanisms**

Lecture Series on Neural Networks and Applications by Prof.S. Sengupta, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur.

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