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    "---\n",
    "license:\n",
    "    code: MIT\n",
    "    content: CC-BY-4.0\n",
    "github: https://github.com/ocademy-ai/machine-learning\n",
    "venue: By Ocademy\n",
    "open_access: true\n",
    "bibliography:\n",
    "  - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameter Optimization\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In machine learning, parameter optimization is a critical process that involves fine-tuning the parameters of a model to minimize a predefined loss function. This optimization is essential for enhancing the model's ability to accurately make predictions. Two fundamental concepts in this process are the loss function and gradient descent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{seealso}\n",
    "<div class=\"yt-container\">\n",
    "  <iframe src=\"https://www.youtube.com/watch?v=JXQT_vxqwIs\" allowfullscreen></iframe>\n",
    "</div>\n",
    "Click the video above for a quick introduction to this section.\n",
    ":::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{tableofcontents}\n",
    ":::"
   ]
  }
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