100 Days of Deep Learning - Problem with Perceptron

The problem with Perceptrons lies in their limitation to learn complex patterns and functions, especially those that are not linearly separable. A Perceptron is a single-layer neural network with binary outputs, and it can only solve problems where the data points are linearly separable. If the data is not linearly separable, a Perceptron cannot converge and find a solution. Notes: https://learnwith.campusx.in/s/store/courses/YouTube%20Notes Code - https://colab.research.google.com/drive/1x6detmf4WAUAT2pfdCts-dVrqnz4_gNB?usp=sharing TensorFlow Playground - https://playground.tensorflow.org/ ============================ 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✨ #Perceptrons #NeuralNetworks #MachineLearning #DataScience #LinearSeparability #LimitationsInPerceptrons