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‎_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb

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"\n",
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"Follow along with the video below or on\n",
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"[youtube](https://www.youtube.com/watch?v=IC0_FRiX-sw).\n",
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"\n",
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"``` {.python .jupyter-code-cell}\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fdce4293",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Run this cell to load the video\n",
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"from IPython.display import display, HTML\n",
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"html_code = \"\"\"\n",
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"<div style=\"margin-top:10px; margin-bottom:10px;\">\n",
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" <iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/IC0_FRiX-sw\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n",
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"</div>\n",
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"\"\"\"\n",
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"display(HTML(html_code))\n",
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"```\n",
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"display(HTML(html_code))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "78767370",
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"metadata": {},
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"source": [
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"\n",
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"\n",
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"PyTorch Tensors\n",
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"---------------\n",

‎_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb

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"<div style=\"width: 45%; float: left; padding: 20px;\"><h2> What you will learn</h2><ul><li>Item 1</li><li>Item 2</li><li>Item 3</li></ul></div><div style=\"width: 45%; float: right; padding: 20px;\"><h2> Prerequisites</h2><ul><li>PyTorch v2.0.0</li><li>GPU ???</li><li>Other items 3</li></ul></div>\n",
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"\n",
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"If you have a video, add it here like this:\n",
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"\n",
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"``` {.python .jupyter-code-cell}\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "955e3d8b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Run this cell to load the video\n",
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"from IPython.display import display, HTML\n",
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"html_code = \"\"\"\n",
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"<div style=\"margin-top:10px; margin-bottom:10px;\">\n",
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" <iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/IC0_FRiX-sw\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n",
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"</div>\n",
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"\"\"\"\n",
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"display(HTML(html_code))\n",
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"```\n",
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"display(HTML(html_code))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "874caf0b",
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"metadata": {},
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"source": [
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"\n",
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"\n",
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"To test your tutorial locally, you can do one of the following:\n",
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"\n",

‎_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb

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"\n",
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"Follow along with the video below or on\n",
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"[youtube](https://www.youtube.com/watch?v=r7QDUPb2dCM).\n",
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"\n",
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"``` {.python .jupyter-code-cell}\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "51b4a53f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Run this cell to load the video\n",
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"from IPython.display import display, HTML\n",
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"html_code = \"\"\"\n",
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"<div style=\"margin-top:10px; margin-bottom:10px;\">\n",
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" <iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/r7QDUPb2dCM\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n",
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"</div>\n",
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"\"\"\"\n",
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"display(HTML(html_code))\n",
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"```\n",
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"display(HTML(html_code))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0d763fd9",
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"metadata": {},
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"source": [
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"\n",
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"\n",
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"Tensors are the central data abstraction in PyTorch. This interactive\n",
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"notebook provides an in-depth introduction to the `torch.Tensor` class.\n",

‎_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb

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"\n",
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"Follow along with the video below or on\n",
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"[youtube](https://www.youtube.com/watch?v=jF43_wj_DCQ).\n",
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"\n",
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"``` {.python .jupyter-code-cell}\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "02f00a20",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Run this cell to load the video\n",
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"from IPython.display import display, HTML\n",
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"html_code = \"\"\"\n",
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"<div style=\"margin-top:10px; margin-bottom:10px;\">\n",
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" <iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/jF43_wj_DCQ\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n",
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"</div>\n",
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"\"\"\"\n",
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"display(HTML(html_code))\n",
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"```\n",
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"display(HTML(html_code))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3f3f7ecc",
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"metadata": {},
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"source": [
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"\n",
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"\n",
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"Introduction\n",
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"------------\n",

‎_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb

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"[youtube](https://www.youtube.com/watch?v=Am2EF9CLu-g). Download the\n",
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"notebook and corresponding files\n",
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"[here](https://pytorch-tutorial-assets.s3.amazonaws.com/youtube-series/video7.zip).\n",
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"\n",
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"``` {.python .jupyter-code-cell}\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ebb64dd7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Run this cell to load the video\n",
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"from IPython.display import display, HTML\n",
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"html_code = \"\"\"\n",
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"<div style=\"margin-top:10px; margin-bottom:10px;\">\n",
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" <iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/Am2EF9CLu-g\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n",
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"</div>\n",
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"\"\"\"\n",
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"display(HTML(html_code))\n",
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"```\n",
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"display(HTML(html_code))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b7bf25b6",
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"metadata": {},
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"source": [
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"\n",
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"\n",
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"[Captum](https://captum.ai/) (\"comprehension\" in Latin) is an open\n",
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"source, extensible library for model interpretability built on PyTorch.\n",

‎_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb

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"\n",
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"Follow along with the video below or on\n",
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"[youtube](https://www.youtube.com/watch?v=6CEld3hZgqc).\n",
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"\n",
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"``` {.python .jupyter-code-cell}\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c29ff41f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Run this cell to load the video\n",
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"from IPython.display import display, HTML\n",
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"html_code = \"\"\"\n",
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"<div style=\"margin-top:10px; margin-bottom:10px;\">\n",
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" <iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/6CEld3hZgqc\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n",
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"</div>\n",
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"\"\"\"\n",
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"display(HTML(html_code))\n",
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"```\n",
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"display(HTML(html_code))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a1c48700",
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"metadata": {},
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"source": [
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"\n",
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"\n",
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"Before You Start\n",
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"----------------\n",

‎_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb

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"\n",
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"Follow along with the video below or on\n",
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"[youtube](https://www.youtube.com/watch?v=M0fX15_-xrY).\n",
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"\n",
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"``` {.python .jupyter-code-cell}\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2220505b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Run this cell to load the video\n",
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"from IPython.display import display, HTML\n",
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"html_code = \"\"\"\n",
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"<div style=\"margin-top:10px; margin-bottom:10px;\">\n",
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" <iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M0fX15_-xrY\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n",
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"</div>\n",
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"\"\"\"\n",
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"display(HTML(html_code))\n",
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"```\n",
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"display(HTML(html_code))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "59c306b3",
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"metadata": {},
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"source": [
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"\n",
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"\n",
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"PyTorch's *Autograd* feature is part of what make PyTorch flexible and\n",
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"fast for building machine learning projects. It allows for the rapid and\n",

‎_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb

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"\n",
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"Follow along with the video below or on\n",
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"[youtube](https://www.youtube.com/watch?v=OSqIP-mOWOI).\n",
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"\n",
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"``` {.python .jupyter-code-cell}\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "390e2a3b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Run this cell to load the video\n",
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"from IPython.display import display, HTML\n",
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"html_code = \"\"\"\n",
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"<div style=\"margin-top:10px; margin-bottom:10px;\">\n",
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" <iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/OSqIP-mOWOI\" frameborder=\"0\" allow=\"accelerometer; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>\n",
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"</div>\n",
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"\"\"\"\n",
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"display(HTML(html_code))\n",
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"```\n",
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"display(HTML(html_code))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e441c9cb",
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"metadata": {},
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"source": [
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"\n",
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"\n",
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"`torch.nn.Module` and `torch.nn.Parameter`\n",
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"------------------------------------------\n",

‎_images/sphx_glr_char_rnn_classification_tutorial_001.png

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‎_images/sphx_glr_char_rnn_classification_tutorial_002.png

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