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January 18, 2024 17:54
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gradient_descent_script.ipynb
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{ | |
"nbformat": 4, | |
"nbformat_minor": 0, | |
"metadata": { | |
"colab": { | |
"provenance": [], | |
"authorship_tag": "ABX9TyMMQS+enI4zpKy9fN3rR7uX", | |
"include_colab_link": true | |
}, | |
"kernelspec": { | |
"name": "python3", | |
"display_name": "Python 3" | |
}, | |
"language_info": { | |
"name": "python" | |
} | |
}, | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "view-in-github", | |
"colab_type": "text" | |
}, | |
"source": [ | |
"<a href=\"https://colab.research.google.com/gist/ritwikraha/0e0b3a044a18c03716d96608f38348e5/gradient_descent_script.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"id": "iqNLMchoxUUm" | |
}, | |
"outputs": [], | |
"source": [ | |
"import numpy as np\n", | |
"\n", | |
"def gradient_descent(x, y, theta, num_iterations):\n", | |
"\n", | |
" m = len(y) # Number of data points\n", | |
"\n", | |
" for i in range(num_iterations):\n", | |
" # Calculate predictions\n", | |
" predictions = np.dot(x, theta)\n", | |
"\n", | |
" # Calculate errors (residuals)\n", | |
" errors = predictions - y\n", | |
"\n", | |
" # Calculate gradients\n", | |
" gradients = np.dot(x.T, errors) / m\n", | |
"\n", | |
" # Update parameters\n", | |
" theta = theta * gradients\n", | |
"\n", | |
" return theta\n" | |
] | |
} | |
] | |
} |
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