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Backpropagation

In machine learning, backpropagation is a gradient computation method commonly used for training a neural network in computing parameter updates.

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Overview

Matrix multiplication

Intuition

Derivation

Second-order gradient descent

Loss function

Limitations

History

Advanced semantic analysis

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Map overview Semantic statistics

Backpropagation

Nodes98
Edges97
Triples116
Avg. degree1.98
Density0.020408
Components1

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Backpropagation

Top relations

related to Further reading · 29
Backpropagation → Aaron, Back-Propagation, Bengio, Berlin, Courville, Deep Learning, Determination Press, Goodfellow, How, Ian, ISBN, James, McCaffrey, Michael, MIT Press, MSDN Magazine, Neural Network Back-Propagation, Neural Networks, Nielsen, October
related to External links · 20
Backpropagation → Andrej, Archived, Backpropagation Really Doing, CS231n, Karpathy, Lecture, Mariusz, Matrix Form, Neural Networks, November, Principles, Przemysław, Putta, Stanford University, Sudeep Raja, What, WikiversityBernacki, Włodarczyk, Yet Another Derivation, YouTube
related to Second-order gradient descent · 11
Backpropagation → As, At, Dirac, Fisher, Given, Hessian, It, Levenberg, Marquardt, The Hessian, Using
related to Modern backpropagation · 9
Backpropagation → Freud's, He, In, MLPs, Modern, Paul Werbos, PhD, Seppo Linnainmaa, Werbos
related to Early successes · 8
Backpropagation → Boltzmann, Contributing, English, In, NETtalk, Sejnowski, The NETtalk, Today
related to After backpropagation · 7
Backpropagation → During, ERP, Error, GPU-based, N400, P600, This
related to Learning as an optimization problem · 6
Backpropagation → Consider, For, Initially, The, Then, To
related to overview · 5
Backpropagation → Denote, For, ReLU, SEL, XC
related to Precursors · 4
Backpropagation → Frank Rosenblatt, Gottfried Wilhelm Leibniz, In, The
related to Derivation · 3
Backpropagation → Assuming, The, This

Important terminology Word statistics

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Important terminology

displaystyle function gradient network output error loss input weights layer neural training learning neuron derivatives descent algorithm used weight networks

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Backpropagationis agradient computation method commonly used for training a neural network in computing parameter updates.It is an efficient application of the chain rule to neural networks0.90text
Backpropagationis aspecial case of reverse accumulation0.90text
Backpropagationrelated to Adjoint graphFor0.60section
Backpropagationrelated to After backpropagationDuring0.60section
Backpropagationrelated to After backpropagationGPU-based0.60section
Backpropagationrelated to After backpropagationThis0.60section
Backpropagationrelated to After backpropagationError0.60section
Backpropagationrelated to After backpropagationERP0.60section
Backpropagationrelated to After backpropagationN4000.60section
Backpropagationrelated to After backpropagationP6000.60section
Backpropagationrelated to AssumptionsThe0.60section
Backpropagationrelated to DerivationThe0.60section

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