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    Differentiation (in Machine Learning)

    AI fundamentals & modelsDifferentiationDerivation calculation
    Editorially reviewed on: 1 source
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    The short answer · 18 words

    In short

    Derivations describe changes to a function and are used, for example, for the gradient-based training of neural networks.

    Flow-alerta.cab (2026): Differentiation (in Machine Learning). https://flow-alerta.cab/en/glossar/differentiation-in-machine-learning. Editorially reviewed on: 09 October 2026.

    How much detail would you like?

    Practical

    Relevant for gradient-based learning methods; not every machine learning method requires derivations.

    Possible example: An image classification model calculates derivatives of its loss function to adjust weights.

    Technical basis of gradient-based models, for example for image recognition and forecasting; usually provided by a training library.

    At a glance

    Core
    In machine learning, differentiation determines how much the error changes when a parameter is slightly varied. Through automatic differentiation (autodiff) in the backpropagation step, neural networks learn efficiently.
    Use
    Relevant for gradient-based learning methods; not every machine learning method requires derivations.
    Example
    Possible example: An image classification model calculates derivatives of its loss function to adjust weights.
    Limits
    Numerical instabilities (vanishing/exploding gradients); confusion of symbolic, numerical and automatic differentiation.

    In context

    Backpropagation

    Efficient method for calculating gradients in multilayer neural networks.

    Gradient Descent

    Gradient descent optimizes parameters by changing them incrementally against the gradient of a target function.

    Questions about this term

    What is Automatic Differentiation?

    It calculates derivations of a computer program on the basis of the chain rule and differs from symbolic derivation and difference quotients.

    Is it free of rounding errors?

    No. Autodiff is also subject to the numerical accuracy used.

    All details and background

    In machine learning, differentiation determines how much the error changes when a parameter is slightly varied. Through automatic differentiation (autodiff) in the backpropagation step, neural networks learn efficiently.

    ELI5 – Explained simply

    Like the search for the steepest descent on a mountain slope in the fog: you scan the slope to get into the valley.

    Why it matters in practice

    Relevant for gradient-based learning methods; not every machine learning method requires derivations.

    Example

    Possible example: An image classification model calculates derivatives of its loss function to adjust weights.

    Pitfalls and limits

    • Numerical instabilities (vanishing/exploding gradients); confusion of symbolic, numerical and automatic differentiation.

    Origin & history

    Mathematical differentiation goes back a long way before machine learning. Baydin et al. provide an overview of automatic differentiation and its use in machine learning.

    Use in mid-sized companies

    • Technical basis of gradient-based models, for example for image recognition and forecasting; usually provided by a training library.

    Related terms

    Matching Flow-alerta service

    TrainingAI Potential Analysis & Roadmap – Technical Basics of Model Training

    Matching tool / use case

    View use casesUse Case: Calculate gradients of a loss function for a neural model.

    Sources

    • arxiv.orgBaydin et al. (2018), Automatic Differentiation in Machine Learning: a Survey

    Quellen geprüft am 09.10.2026.

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