Activation Functions in PyTorch
As neural networks become increasingly popular in the field of machine learning, it is important to understand the role that activation functions play in their implementation. In this article, you’ll explore the concept of activation functions that are applied to the output of each neuron in a neural network to introduce non-linearity into the model. Without activation functions, neural networks would simply be a series of linear transformations, which would limit their ability to learn complex patterns and relationships in data.
PyTorch offers a variety of activation functions, each with its own unique properties and use cases. Some common activation functions in PyTorch include ReLU, sigmoid, and tanh. Choosing the right activation function for a particular problem can be