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Update 04 Gradient Descent for Linear Regression.ipynb #2

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4 changes: 2 additions & 2 deletions Lectures/05 Supervised Learning - Regression/04 Gradient Descent for Linear Regression.ipynb
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Expand Up @@ -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"
]
},
{
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