activation functions Archives

How to Fix the Vanishing Gradient Problem Using ReLU

Post author:Anum Fatima
Post published:February 21, 2024
Post category:Machine Learning Basics

Learn how Rectified Linear Unit (ReLU) activation functions can revolutionize your deep neural network training. Discover how ReLU prevents gradients from vanishing, tackles the issue of dying neurons, and explore advanced techniques for optimal performance. Dive into the world of ReLU and unlock the full potential of your neural network models

How to Choose the Best Activation Functions for Hidden Layers and Output Layers in Deep Learning

Post author:Anum Fatima
Post published:February 11, 2024
Post category:Machine Learning Basics

Selecting the best activation function is critical for effective neural network design. For hidden layers, ReLU is commonly used in CNNs and MLPs, while sigmoid and tanh suit RNNs. Output layer activation depends on the task: linear for regression, sigmoid for binary classification, softmax for multi-class, and sigmoid for multi-label classification.