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Fix formula typo in exercise 3 and other typos in exercise 5

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Agustín Aliaga Casaletti
2021-05-28 11:37:36 -03:00
parent ec39a6847a
commit c9e00ff037
2 changed files with 3 additions and 3 deletions
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2
Exercise3/exercise3.ipynb
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@@ -373,7 +373,7 @@
"$$\n", "$$\n",
"\\begin{align*}\n", "\\begin{align*}\n",
"& \\frac{\\partial J(\\theta)}{\\partial \\theta_0} = \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta\\left( x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)} & \\text{for } j = 0 \\\\\n", "& \\frac{\\partial J(\\theta)}{\\partial \\theta_0} = \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta\\left( x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)} & \\text{for } j = 0 \\\\\n",
"& \\frac{\\partial J(\\theta)}{\\partial \\theta_0} = \\left( \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta\\left( x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)} \\right) + \\frac{\\lambda}{m} \\theta_j & \\text{for } j \\ge 1\n", "& \\frac{\\partial J(\\theta)}{\\partial \\theta_j} = \\left( \\frac{1}{m} \\sum_{i=1}^m \\left( h_\\theta\\left( x^{(i)} \\right) - y^{(i)} \\right) x_j^{(i)} \\right) + \\frac{\\lambda}{m} \\theta_j & \\text{for } j \\ge 1\n",
"\\end{align*}\n", "\\end{align*}\n",
"$$\n", "$$\n",
"\n", "\n",

4
Exercise5/exercise5.ipynb
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@@ -687,9 +687,9 @@
"\n", "\n",
"### 3.2 Optional (ungraded) exercise: Adjusting the regularization parameter\n", "### 3.2 Optional (ungraded) exercise: Adjusting the regularization parameter\n",
"\n", "\n",
"In this section, you will get to observe how the regularization parameter affects the bias-variance of regularized polynomial regression. You should now modify the the lambda parameter and try $\\lambda = 1, 100$. For each of these values, the script should generate a polynomial fit to the data and also a learning curve.\n", "In this section, you will get to observe how the regularization parameter affects the bias-variance of regularized polynomial regression. You should now modify the lambda parameter and try $\\lambda = 1, 100$. For each of these values, the script should generate a polynomial fit to the data and also a learning curve.\n",
"\n", "\n",
"For $\\lambda = 1$, the generated plots should look like the the figure below. You should see a polynomial fit that follows the data trend well (left) and a learning curve (right) showing that both the cross validation and training error converge to a relatively low value. This shows the $\\lambda = 1$ regularized polynomial regression model does not have the high-bias or high-variance problems. In effect, it achieves a good trade-off between bias and variance.\n", "For $\\lambda = 1$, the generated plots should look like the figure below. You should see a polynomial fit that follows the data trend well (left) and a learning curve (right) showing that both the cross validation and training error converge to a relatively low value. This shows the $\\lambda = 1$ regularized polynomial regression model does not have the high-bias or high-variance problems. In effect, it achieves a good trade-off between bias and variance.\n",
"\n", "\n",
"<table>\n", "<table>\n",
" <tr>\n", " <tr>\n",