Machine Learning Rules :Perceptron learning rule , delta learning rule (LMS–Widrow Hoff)
Machine Learning Learning Rules: Perceptron and Delta Rule (LMS / Widrow-Hoff) Introduction Machine Learning is fundamentally about learning from mistakes. Just like humans improve through feedback, machines update their internal parameters using learning rules. Two of the most important and foundational learning rules are: Perceptron Learning Rule Delta Learning Rule (LMS / Widrow-Hoff Rule) These rules define how a model adjusts itself when it makes correct or incorrect predictions. Real-World Analogy (Important for Marks) Imagine a student preparing for an exam: If the student answers correctly → no change needed If the student answers incorrectly → they revise and improve This is exactly how learning rules work: The model predicts Compares with actual answer Adjusts itself if wrong The Perceptron Rule behaves like a strict teacher (only corrects mistakes), while the Delta Rule behaves like a smart tutor (adjusts based on how wrong you are). Pe...
