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udlbook
2023-10-31 12:01:20 +00:00
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10
Notebooks/Chap06/6_2_Gradient_Descent.ipynb
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@@ -4,7 +4,7 @@
"metadata": { "metadata": {
"colab": { "colab": {
"provenance": [], "provenance": [],
"authorship_tag": "ABX9TyM/FIXDTd6tZYs6WRzK00hB", "authorship_tag": "ABX9TyN2N4cCnlIobOZXEjcwAvZ5",
"include_colab_link": true "include_colab_link": true
}, },
"kernelspec": { "kernelspec": {
@@ -301,7 +301,7 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"source": [ "source": [
"Now we are ready to perform gradient descent. We'll need to use our line search routine from part I, which I've reproduced here plus the helper function loss_function_1D that converts from a 2D problem to a 1D problem" "Now we are ready to perform gradient descent. We'll need to use our line search routine from noteboo 6.1, which I've reproduced here plus the helper function loss_function_1D that converts from a 2D problem to a 1D problem"
], ],
"metadata": { "metadata": {
"id": "5EIjMM9Fw2eT" "id": "5EIjMM9Fw2eT"
@@ -375,9 +375,9 @@
"source": [ "source": [
"def gradient_descent_step(phi, data, model):\n", "def gradient_descent_step(phi, data, model):\n",
" # TODO -- update Phi with the gradient descent step (equation 6.3)\n", " # TODO -- update Phi with the gradient descent step (equation 6.3)\n",
" # 1. Compute the gradient\n", " # 1. Compute the gradient (you wrote this function above)\n",
" # 2. Find the best step size alpha (use negative gradient as going downhill)\n", " # 2. Find the best step size alpha using line search function (above) -- use negative gradient as going downhill\n",
" # 3. Update the parameters phi\n", " # 3. Update the parameters phi based on the gradient and the step size alpha.\n",
"\n", "\n",
" return phi" " return phi"
], ],