Gompertz function

Applied sciences
Information engineering
Machine learning
Artificial neural networks

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Related lectures (9)

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Covers advanced triangulation problems related to gradient maximization.

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Explores logistic regression, cost functions, gradient descent, and probability modeling using the logistic sigmoid function.

Neural Networks: Learning Features & Linear Prediction

Explores neural networks' ability to learn features and make linear predictions, emphasizing the importance of data quantity for effective performance.

Linear Models for Classification

Explores linear models for classification, logistic regression, and gradient descent in machine learning.

Linear Classification: Signed Distance and Perceptron

Explores signed distance, perceptron, logistic regression, cross entropy, and multi-class classification.

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Covers the extension of the 0-1 loss to real-valued score functions and logistic regression.

Linear Models for Classification

Explores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.

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Explores growth models, resource consumption, and population dynamics, including real-world applications.

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