Multi Layer Perceptron | MLP Intuition

A Multi-Layer Perceptron (MLP) is a type of artificial neural network characterized by its multiple layers of nodes (neurons). It consists of an input layer, one or more hidden layers, and an output layer. Each connection between nodes is associated with a weight, and each node applies an activation function to the weighted sum of its inputs. MLPs are trained using algorithms like backpropagation to learn and make predictions. Digital Notes for Deep Learning: https://shorturl.at/NGtXg 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 👍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✨ #MLP #MultiLayerPerceptron #NeuralNetworks #DeepLearning #ArtificialIntelligence #DataScience #machinelearning ⌚Time Stamps⌚ 00:00 - Intro 01:28 - The problem 02:42 - Perceptron with Sigmoid 23:56 - Adding nodes in hidden layer 26:58 - Adding nodes in input 28:50 - Adding nodes in output node 29:53 - Deep Neural Network 31:53 - TF Playground Demo 37:21 - Outro