You’re very close , but one important idea needs correction 👇 📌 🔹 What is Gradient Descent? 👉 Gradient Descent is an algorithm to find the minimum value of a function (error) by updating parameters step-by-step. 📌 🔹 What is Gradient? 👉 Gradient = slope of the error function Tells: how fast error is changing which direction increases error the most ❗ Important Correction You said: “Gradient is maximum at the point where there is minimum error” ❌ This is incorrect ✔️ Correct statement: 👉 At minimum error, gradient = 0 📊 Why? At the lowest point (minimum): slope becomes flat no increase or decrease [ \nabla J(\theta) = 0 ] 🔹 Intuition (Hill example) Top of hill → steep slope → large gradient Middle → some slope → medium gradient Bottom → flat → gradient = 0 🔹 What Gradient Descent does Start somewhere on curve Check slope (gradient) Move opposite direction of slope Repeat until: slope becomes ~0 (minimum reached) 🔥 Final Understanding Gradient = direction of steepest increase Gradient Descent = move…