Why Are Non-Linear Activation Fucntions Used | Machine Learning Uncovered

Why Activation Functions are Essential in Machine Learning Models 🚀 | Explained Simply In this video, I break down why activation functions are a crucial part of machine learning models. 🤖 Without them, neural networks would just behave like simple linear models, no matter how many layers we add. Activation functions introduce the non-linearity needed for models to learn complex patterns in the data — like recognizing images, understanding language, and making predictions that go beyond straight lines! You'll learn: What activation functions actually do Why linear models aren't enough How activation functions like ReLU, Sigmoid, and Tanh help models learn A simple intuition behind why non-linearity matters Whether you're a beginner or brushing up on fundamentals, this video will help you understand a key building block of modern AI. If you find it helpful, don't forget to like, comment, and subscribe for more ML and AI content! 🔥 #MachineLearning #DeepLearning #NeuralNetworks #AI #ML