Cross entropy loss

As explained in Machine Learning System Design

A classification loss that penalizes incorrect probabilistic predictions using the negative logarithm of the probability assigned to the true class.

How Machine Learning System Design covers it

How deep it goes

Contrasts binary cross-entropy with focal loss for imbalanced data, without detailing the loss computation.

Examples

Uses search clicks and purchases as prediction targets, and spam versus nonspam as a classification example.

What you can do after

Apply binary cross-entropy to train click and purchase predictors, then compare results with online metrics such as CTR and conversion rate.

How it connects

Connects binary cross-entropy with focal loss, class imbalance, and the distinction between training loss and business metrics.

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Also explained in

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  Math and Architectures of Deep LearningA classification loss that measures the mismatch between a predicted probability distribution over classes and the target distribution.
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  Grokking Machine LearningA multiclass classification loss that compares one-hot class labels with predicted class probabilities using cross-entropy.
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  Inside Deep Learning: Math, Algorithms, ModelsA differentiable classification loss that measures the mismatch between predicted and target probability distributions.
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  Learn Generative AI with PyTorchA loss measure that compares predicted token distributions with the target next tokens for multiclass sequence prediction.
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  Graph Neural Networks in ActionCrossEntropyLoss is a PyTorch loss module that measures classification error from logits and target class labels.
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  Build a Large Language Model (From Scratch)A measure of how far a language model's predicted token distribution is from the target token distribution, used to score predictions during training and evaluation.
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  Deep Learning with PyTorch, Second EditionA loss value based on cross-entropy, used here as the quantity minimized during training.
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  Knowledge Graphs and LLMs in ActionCross-entropy loss is a classification loss that penalizes incorrect probability assignments by comparing predicted probabilities with target labels.
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  Reinforcement Learning from Human FeedbackCross-entropy is the expected negative log probability assigned by one distribution to outcomes drawn from another distribution.
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  Deep Learning with Python, Third EditionA classification loss that measures the mismatch between predicted probability distributions and true token labels.
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  How Large Language Models WorkA cross-entropy loss function measures the discrepancy between a target probability distribution and a model's predicted probability distribution.
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  Build a Text-to-Image Generator (from Scratch)Categorical cross-entropy loss measures the discrepancy between predicted class probabilities and the ground-truth class in multicategory classification.
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  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AICrossEntropyLoss is a PyTorch loss-function module that combines class-logit normalization with negative log-likelihood for classification.
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  Natural Language Processing in Action, Second EditionLog loss is a probabilistic prediction metric based on the negative logarithm of the probability assigned to the observed target.
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  Deep Learning with R, Third EditionCategorical cross-entropy is a multiclass classification loss that measures the negative log-likelihood of the target class under predicted class probabilities and expects categorically encoded labels.
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  Build a Reasoning Model (From Scratch)A loss function that measures the average negative log probability assigned to the correct target tokens.
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  Experimentation for EngineersA loss metric that measures the discrepancy between a probability distribution predicted by a model and the observed outcome distribution.
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  Grokking AI Algorithms, Second EditionCross-entropy loss is a prediction-error measure computed as the negative logarithm of the probability assigned to the correct outcome.

Compare how 19 books explain cross entropy loss →

Related concepts

• classifieraddresses · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
• negative log-likelihoodequivalent to · Negative log-likelihood is the negative logarithm of the likelihood a probabilistic model assigns to observed data, commonly used as an objective or loss to minimize.
• focal lossincludes · Focal loss is a classification loss that downweights well-classified examples and emphasizes difficult or misclassified examples, especially under class imbalance.
• uncertaintymeasures · Uncertainty is incomplete knowledge or confidence about a value, state, interpretation, behavior, or outcome.
• likelihoodequivalent to · A likelihood is the probability of observed data under a specified probabilistic model, regarded as a function of candidate hypotheses or parameter values.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.