Perceptron Loss Function | Hinge Loss | Binary Cross Entropy | Sigmoid Function
Channel: CampusX
Date: 2022-02-25

The Perceptron Loss Function is a measure used to quantify how well a perceptron classifies data points. Common loss functions for perceptrons include:

Hinge Loss: Suitable for binary classification problems, encouraging correct classifications with a margin.
Binary Cross Entropy: Often used with sigmoid activation for binary classification, measuring the dissimilarity between predicted and actual probabilities.

Digital Notes for Deep Learning: https://shorturl.at/NGtXg

Perceptron Loss Function - https://scikit-learn.org/stable/modules/sgd.html

Code - https://github.com/campusx-official/100-days-of-deep-learning/tree/main/day5%20-%20Perceptron%20Loss%20Function

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⌚Time Stamps⌚

00:00 - Intro
01:31 - Recap
06:01 - Problem with perceptron trick
10:35 - Loss Function
15:08 - Perceptron Loss Function
21:46 - SkLearn Documentation
27:51 - Explanation of loss function
39:08 - Gradient Descent
44:59 - Code Demo
47:54 - More Loss Functions
58:18 - Outro