Standard 8 stops to get here · leads to 1

Saliency Map

A visualization highlighting input regions most important for model predictions.

Your route here

8 stops · basics first
  1. 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.

  2. 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.

  3. Interpretability ✓ understood

    Understanding the internal workings of AI models, including which features influence predictions and why.

  4. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  5. Training ✓ understood

    The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

  6. Loss Function ✓ understood

    A function that scores how wrong a model's prediction is as a single number, which training then works to make as small as possible.

  7. Gradient Descent ✓ understood

    An optimization method that repeatedly moves a model's parameters a small step in the direction that most reduces the loss.

  8. Backpropagation ✓ understood

    The algorithm for computing gradients of the loss with respect to network weights, enabling training through gradient descent.

  9. Saliency Map · you are here ✓ understood

A visualization highlighting input regions most important for model predictions.

This concept is essential for understanding specialized ai topics and forms a key part of modern AI systems.

  • Visualization
  • Interpretability
  • Gradient

Where it sits

Before this

Interpretability Backpropagation
Saliency Map

Leads to

Grad-CAM

Explore nearby

Evaluation Grad-CAM Gradient-weighted Class Activation Mapping - visualizing which image regions influenced CNN predictions. Evaluation Attention Visualization Visualizing attention weights to understand which inputs the model focuses on. Evaluation LIME Local Interpretable Model-agnostic Explanations - explaining individual predictions by approximating with simpler models. Evaluation SHAP SHapley Additive exPlanations - a unified approach to explaining model predictions using game theory.