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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,226 +1 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "ename": "ModuleNotFoundError", | ||
| "evalue": "No module named 'rajas'", | ||
| "output_type": "error", | ||
| "traceback": [ | ||
| "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", | ||
| "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", | ||
| "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrajas\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m embeddings \u001b[38;5;28;01mas\u001b[39;00m eb\n", | ||
| "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'rajas'" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "from rajas import embeddings as eb" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 26, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "eb.embed_audio('02_github.ipynb')" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Default conversion\n", | ||
| "\n", | ||
| "```\n", | ||
| "02 converted with `jupyter nbconvert 02_github.ipynb --to slides --no-input`\n", | ||
| "```\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Conversion with different theme\n", | ||
| "\n", | ||
| "```\n", | ||
| "jupyter nbconvert 10_sqlite.ipynb --to slides --SlidesExporter.reveal_theme=solarized\n", | ||
| "```\n" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "[NbConvertApp] Converting notebook 22_linear_separable.ipynb to slides\n", | ||
| "[NbConvertApp] Writing 602828 bytes to 22_linear_separable.slides.html\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "!jupyter nbconvert 22_linear_separable.ipynb --to slides --SlidesExporter.reveal_theme=solarized" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "[NbConvertApp] Converting notebook 23_linear_separable_smf.ipynb to slides\n", | ||
| "[NbConvertApp] Writing 590645 bytes to 23_linear_separable_smf.slides.html\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "!jupyter nbconvert 23_linear_separable_smf.ipynb --to slides --SlidesExporter.reveal_theme=solarized" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "[NbConvertApp] Converting notebook 24_regression_examples.ipynb to slides\n", | ||
| "[NbConvertApp] Writing 599589 bytes to 24_regression_examples.slides.html\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "!jupyter nbconvert 24_regression_examples.ipynb --to slides --SlidesExporter.reveal_theme=solarized" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "[NbConvertApp] Converting notebook 25_regression_interpretation.ipynb to slides\n", | ||
| "[NbConvertApp] Writing 668277 bytes to 25_regression_interpretation.slides.html\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "!jupyter nbconvert 25_regression_interpretation.ipynb --to slides --SlidesExporter.reveal_theme=solarized" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 7, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "[NbConvertApp] Converting notebook 26_nns.ipynb to slides\n", | ||
| "[NbConvertApp] Writing 691244 bytes to 26_nns.slides.html\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "!jupyter nbconvert 26_nns.ipynb --to slides --SlidesExporter.reveal_theme=solarized " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 8, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "[NbConvertApp] Converting notebook 27_basic_regression_pytorch.ipynb to slides\n", | ||
| "[NbConvertApp] Writing 640623 bytes to 27_basic_regression_pytorch.slides.html\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "!jupyter nbconvert 27_basic_regression_pytorch.ipynb --to slides --SlidesExporter.reveal_theme=solarized " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "[NbConvertApp] Converting notebook 28_logistic_regression_pytorch.ipynb to slides\n", | ||
| "[NbConvertApp] Writing 613760 bytes to 28_logistic_regression_pytorch.slides.html\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "!jupyter nbconvert 28_logistic_regression_pytorch.ipynb --to slides --SlidesExporter.reveal_theme=solarized " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 7, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "[NbConvertApp] Converting notebook streamlit.ipynb to slides\n", | ||
| "[NbConvertApp] Writing 583921 bytes to streamlit.slides.html\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "!jupyter nbconvert streamlit.ipynb --to slides --SlidesExporter.reveal_theme=solarized " | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python [conda env:.conda-ds4bio]", | ||
| "language": "python", | ||
| "name": "conda-env-.conda-ds4bio-py" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.10.4" | ||
| }, | ||
| "vscode": { | ||
| "interpreter": { | ||
| "hash": "79f87720972903b6188d40b03afb0115543bf63f5f0af29aadb196967754f61a" | ||
| } | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 4 | ||
| } | ||
| {"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"psneWwGCyMOG","outputId":"3cbfeb59-a960-4929-93e0-9f9e4f5cbf2c"},"outputs":[{"ename":"ModuleNotFoundError","evalue":"No module named 'rajas'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)","Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mrajas\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m embeddings \u001b[38;5;28;01mas\u001b[39;00m eb\n","\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'rajas'"]}],"source":["from rajas import embeddings as eb"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lnbzGQamyMOJ"},"outputs":[],"source":["eb.embed_audio('02_github.ipynb')"]},{"cell_type":"markdown","metadata":{"id":"ZqGmf_LyyMOJ"},"source":["Default conversion\n","\n","```\n","02 converted with `jupyter nbconvert 02_github.ipynb --to slides --no-input`\n","```\n"]},{"cell_type":"markdown","metadata":{"id":"41KLFjqtyMOL"},"source":["Conversion with different theme\n","\n","```\n","jupyter nbconvert 10_sqlite.ipynb --to slides --SlidesExporter.reveal_theme=solarized\n","```\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VB3BK2W_yMOL","outputId":"54357ecd-2fbb-4236-8b35-55b315b68ef8"},"outputs":[{"name":"stdout","output_type":"stream","text":["[NbConvertApp] Converting notebook 22_linear_separable.ipynb to slides\n","[NbConvertApp] Writing 602828 bytes to 22_linear_separable.slides.html\n"]}],"source":["!jupyter nbconvert 22_linear_separable.ipynb --to slides --SlidesExporter.reveal_theme=solarized"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1qp1Sp_GyMOM","outputId":"c7cd8bd0-7206-4cb8-8332-9f5600ac272a"},"outputs":[{"name":"stdout","output_type":"stream","text":["[NbConvertApp] Converting notebook 23_linear_separable_smf.ipynb to slides\n","[NbConvertApp] Writing 590645 bytes to 23_linear_separable_smf.slides.html\n"]}],"source":["!jupyter nbconvert 23_linear_separable_smf.ipynb --to slides --SlidesExporter.reveal_theme=solarized"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XBCCGUAdyMOM","outputId":"2ea98c82-203b-4a3d-df8a-4ec42409cb11"},"outputs":[{"name":"stdout","output_type":"stream","text":["[NbConvertApp] Converting notebook 24_regression_examples.ipynb to slides\n","[NbConvertApp] Writing 599589 bytes to 24_regression_examples.slides.html\n"]}],"source":["!jupyter nbconvert 24_regression_examples.ipynb --to slides --SlidesExporter.reveal_theme=solarized"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FXb9mB6kyMON","outputId":"617e3028-26e2-41e8-a1a2-28469f53514b"},"outputs":[{"name":"stdout","output_type":"stream","text":["[NbConvertApp] Converting notebook 25_regression_interpretation.ipynb to slides\n","[NbConvertApp] Writing 668277 bytes to 25_regression_interpretation.slides.html\n"]}],"source":["!jupyter nbconvert 25_regression_interpretation.ipynb --to slides --SlidesExporter.reveal_theme=solarized"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KO5mLou9yMON","outputId":"461727d6-eaf7-4024-de17-5de90d03bfca"},"outputs":[{"name":"stdout","output_type":"stream","text":["[NbConvertApp] Converting notebook 26_nns.ipynb to slides\n","[NbConvertApp] Writing 691244 bytes to 26_nns.slides.html\n"]}],"source":["!jupyter nbconvert 26_nns.ipynb --to slides --SlidesExporter.reveal_theme=solarized"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"U_NQTXITyMON","outputId":"c18ca5a2-df36-43fc-ed8a-88efa4aa820d"},"outputs":[{"name":"stdout","output_type":"stream","text":["[NbConvertApp] Converting notebook 27_basic_regression_pytorch.ipynb to slides\n","[NbConvertApp] Writing 640623 bytes to 27_basic_regression_pytorch.slides.html\n"]}],"source":["!jupyter nbconvert 27_basic_regression_pytorch.ipynb --to slides --SlidesExporter.reveal_theme=solarized"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2sA9zUXsyMOO","outputId":"3d5204ea-f8a6-44e3-b75c-70468c5421df"},"outputs":[{"name":"stdout","output_type":"stream","text":["[NbConvertApp] Converting notebook 28_logistic_regression_pytorch.ipynb to slides\n","[NbConvertApp] Writing 613760 bytes to 28_logistic_regression_pytorch.slides.html\n"]}],"source":["!jupyter nbconvert 28_logistic_regression_pytorch.ipynb --to slides --SlidesExporter.reveal_theme=solarized"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"T16hwmU8yMOO","outputId":"9adffa28-e6c8-48d7-93c8-6a98f4050f6e"},"outputs":[{"name":"stdout","output_type":"stream","text":["[NbConvertApp] Converting notebook streamlit.ipynb to slides\n","[NbConvertApp] Writing 583921 bytes to streamlit.slides.html\n"]}],"source":["!jupyter nbconvert streamlit.ipynb --to slides --SlidesExporter.reveal_theme=solarized"]},{"cell_type":"code","source":["!jupyter nbconvert streamlit.ipynb --to slides --SlidesExporter.reveal_theme=solarized"],"metadata":{"id":"W6Gfg92jyPRy"},"execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python [conda env:.conda-ds4bio]","language":"python","name":"conda-env-.conda-ds4bio-py"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.4"},"vscode":{"interpreter":{"hash":"79f87720972903b6188d40b03afb0115543bf63f5f0af29aadb196967754f61a"}},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":0} |