During backpropagation, loss function gets updated, and activation function helps the gradient descent curves to achieve their local minima. In this article, I’ll discuss the various types of ...

Neural networks, structured with layers of neurons, rely on activation functions to introduce non-linearity, crucial for learning complex patterns.

An activation function is a mathematical function applied to the output of a neuron (i.e., the linear combination of inputs and weights) in a neural network. It introduces non-linearity into the ...

Activation functions are a crucial component of artificial neural networks. They introduce non-linearity into the models, allowing them to capture complex relationships in data. In this article, we'll ...

The activation function has a critical influence on whether a convolutional neural network in deep learning can converge or not; a proper activation function not only makes the convolutional neural ...

A linear activation function lacks in performing backpropagation. Thus, it is not recommended to be used in a neural network. While a model may perform a task even without the presence of an ...

Feed-forward neural networks (NNs) are a staple machine learning method widely used in many areas of science and technology, including physical chemistry, computational chemistry, and materials ...