Standard 7 stops to get here · leads to 4

Cross-Entropy Loss

A loss function for classification that measures the difference between predicted and true probability distributions.

Your route here

7 stops · basics first
  1. Dataset ✓ understood

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

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Activation Function ✓ understood

    A non-linear function applied to neuron outputs that introduces non-linearity, enabling networks to learn complex patterns.

  7. Softmax ✓ understood

    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.

  8. Cross-Entropy Loss · you are here ✓ understood

Where it sits

Before this

Loss Function Softmax
Cross-Entropy Loss

Leads to

Focal Loss Log Loss Perplexity Label Smoothing

Explore nearby

Evaluation Log Loss Logarithmic loss measuring the accuracy of probabilistic predictions, penalizing confident wrong predictions. Foundations KL Divergence Kullback-Leibler divergence - a measure of how one probability distribution differs from another. Foundations Entropy A measure of uncertainty or randomness in a random variable from information theory. Training Focal Loss A modified cross-entropy loss that down-weights easy examples, helping with class imbalance. Training Label Smoothing Softening target labels to prevent overconfidence and improve generalization. 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.