From 0b88b2084d777b021375c9054b3d35aafdabf790 Mon Sep 17 00:00:00 2001
From: Omid Heydari <90824839+omidhey@users.noreply.github.com>
Date: Sat, 10 May 2025 17:57:11 +0330
Subject: [PATCH] Update 04 Gradient Descent for Linear Regression.ipynb

beta1 instead of beta 0
---
 .../04 Gradient Descent for Linear Regression.ipynb           | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Lectures/05 Supervised Learning - Regression/04 Gradient Descent for Linear Regression.ipynb b/Lectures/05 Supervised Learning - Regression/04 Gradient Descent for Linear Regression.ipynb
index 38f099f..5692b6c 100644
--- a/Lectures/05 Supervised Learning - Regression/04 Gradient Descent for Linear Regression.ipynb	
+++ b/Lectures/05 Supervised Learning - Regression/04 Gradient Descent for Linear Regression.ipynb	
@@ -263,9 +263,9 @@
    "source": [
     "To minimize the cost function, we need to know in which direction to adjust our parameters. This is where partial derivatives come in. We calculate the partial derivative of the cost function with respect to each parameter:\n",
     "\n",
-    "$$ \\frac{\\partial J}{\\partial \\beta_0} = -\\frac{1}{n} \\sum_{i=1}^n x_i(y_i - (\\beta_0 + \\beta_1 x_i)) $$\n",
+    "$$ \\frac{\\partial J}{\\partial \\beta_0} = -\\frac{1}{n} \\sum_{i=1}^n (y_i - (\\beta_0 + \\beta_1 x_i)) $$\n",
     "\n",
-    "$$ \\frac{\\partial J}{\\partial \\beta_1} = -\\frac{1}{n} \\sum_{i=1}^n (y_i - (\\beta_0 + \\beta_1 x_i)) $$\n"
+    "$$ \\frac{\\partial J}{\\partial \\beta_1} = -\\frac{1}{n} \\sum_{i=1}^n x_i(y_i - (\\beta_0 + \\beta_1 x_i)) $$\n"
    ]
   },
   {