Standard 3 stops to get here · leads to 1
Sigmoid
An activation function that squashes values to range (0,1), often used for binary classification and gates in LSTMs.
Your route here
3 stops · basics first
- Machine Learning ✓ understood
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
- Neural Network ✓ understood
A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.
- Activation Function ✓ understood
A non-linear function applied to neuron outputs that introduces non-linearity, enabling networks to learn complex patterns.
- Sigmoid · you are here ✓ understood
Where it sits
Explore nearby
Neural Networks Softmax A function that turns a list of scores (logits) into probabilities that are all positive and sum to 1; the standard output of classifiers and language models. Evaluation Log Loss Logarithmic loss measuring the accuracy of probabilistic predictions, penalizing confident wrong predictions. Foundations Classification A supervised learning task where the model assigns each input to one of a fixed set of categories, such as spam or not spam. Neural Networks Long Short-Term Memory A type of RNN architecture with gates that can learn long-term dependencies, solving the vanishing gradient problem. Neural Networks Swish A smooth activation function (x * sigmoid(x)) that often outperforms ReLU, discovered through neural architecture search.