The Vanishing Gradient Problem and 5 Proven Fixes
The vanishing gradient problem is the reason deep neural networks were nearly impossible to train for years. As error gradients […]
ELU Activation Function: 3 Benefits and a Live Plot
The ELU activation function — the Exponential Linear Unit — behaves like ReLU for positive inputs but replaces the flat
Swish Activation Function: 3 Reasons It Beats ReLU
The Swish activation function is a smooth, self-gated activation defined simply as the input times its own sigmoid. Found by
GELU Activation Function: 3 Reasons Transformers Use It
The GELU activation function — the Gaussian Error Linear Unit — is the activation behind modern transformers such as BERT
Leaky ReLU: Fix Dying ReLU in 1 Simple Change
Leaky ReLU is a simple, popular fix for the biggest weakness of ordinary ReLU: dead neurons. Instead of flattening every
Tanh Activation Function: 4 Key Facts and a Live Plot
The tanh activation function — the hyperbolic tangent — squashes inputs into the range −1 to 1. It is a
Softmax Function Explained with a Live Calculator 2026
The softmax function is how a neural network turns a handful of raw scores into class probabilities that add up
Sigmoid Function Explained with a Live Plot 2026
The sigmoid function is the classic S-shaped curve that squashes any real number into a value between 0 and 1.
ReLU Activation Function: 3 Reasons It Powers Deep Nets
The ReLU activation function — short for Rectified Linear Unit — is the default activation for hidden layers in modern
Classification Metrics: 5 to Know and When to Use
A practical guide to classification metrics — accuracy, precision, recall, F1 and ROC-AUC — with a decision process for choosing the right one for balanced vs imbalanced data.