100 Days of Deep Learning - Perceptron Loss Function | Hinge Loss | Binary Cross Entropy | Sigmoid Function

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. Notes: https://learnwith.campusx.in/s/store/courses/YouTube%20Notes 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 ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at [email protected] 👍If you find this video helpful, consider giving it a thumbs up and subscribing for more educational videos on data science! 💭Share your thoughts, experiences, or questions in the comments below. I love hearing from you! ✨ Hashtags✨ #PerceptronLossFunction #HingeLoss #BinaryCrossEntropy #SigmoidFunction #MachineLearning #NeuralNetworks #datascience ⌚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