Add to ORKGNeural networks have shown tremendous growth in recent years to solve numerous problems. Various types of neural networks have been introduced to deal with different types of problems. However, the main goal of any neural network is to transform the non-linearly separable input data into more linearly separable abstract features using a hierarchy of layers. These layers are combinations of linear and nonlinear functions. The most popular and common non-linearity layers are activation functions (AFs), such as Logistic Sigmoid, Tanh, ReLU, ELU, Swish and Mish. In this paper, a comprehensive overview and survey is presented for AFs in neural networks for deep learning. Different classes of AFs such as Logistic Sigmoid and Tanh based, ReLU base...
Deep Learning in the field of Big Data has become essential for the analysis and perception of trend...
The activation function in neural network is one of the important aspects which facilitates the deep...
In deep learning models, the inputs to the network are processed using activation functions to gener...
Deep neural networks (DNN) have been successfully used in diverse emerging domains to solve real wor...
Inspired by biological neurons, the activation functions play an essential part in the learning proc...
Activation functions are crucial in deep learning networks, given that the nonlinear ability of acti...
MEng (Computer and Electronic Engineering), North-West University, Potchefstroom CampusThe ability o...
Activation functions play an important role in artificial neural networks (ANNs) because they break ...
Activation functions are an essential part of artificial neural networks. Over the years, researches...
In Deep learning neural networks (DNNs) activation functions perform a vital role. In each neuron ac...
Activation functions are essential for deep learning methods to learn and perform complex tasks such...
Deep learning, the study of multi-layered artificial neural networks, has received tremendous attent...
In recent years, various deep neural networks with different learning paradigms have been widely emp...
Activation functions (AFs) are the basis for neural network architectures used in real-world problem...
In this paper the effects of different activation functions on neural networks are argued
Deep Learning in the field of Big Data has become essential for the analysis and perception of trend...
The activation function in neural network is one of the important aspects which facilitates the deep...
In deep learning models, the inputs to the network are processed using activation functions to gener...
Deep neural networks (DNN) have been successfully used in diverse emerging domains to solve real wor...
Inspired by biological neurons, the activation functions play an essential part in the learning proc...
Activation functions are crucial in deep learning networks, given that the nonlinear ability of acti...
MEng (Computer and Electronic Engineering), North-West University, Potchefstroom CampusThe ability o...
Activation functions play an important role in artificial neural networks (ANNs) because they break ...
Activation functions are an essential part of artificial neural networks. Over the years, researches...
In Deep learning neural networks (DNNs) activation functions perform a vital role. In each neuron ac...
Activation functions are essential for deep learning methods to learn and perform complex tasks such...
Deep learning, the study of multi-layered artificial neural networks, has received tremendous attent...
In recent years, various deep neural networks with different learning paradigms have been widely emp...
Activation functions (AFs) are the basis for neural network architectures used in real-world problem...
In this paper the effects of different activation functions on neural networks are argued
Deep Learning in the field of Big Data has become essential for the analysis and perception of trend...
The activation function in neural network is one of the important aspects which facilitates the deep...
In deep learning models, the inputs to the network are processed using activation functions to gener...