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89 lines
1.5 KiB
89 lines
1.5 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c50398ed",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "aaf49431",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[9 0 8]\n"
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]
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}
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],
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"source": [
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"import numpy as np \n",
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"a = np.array([[9,0,8],[7,5,4],[4,5,3],[1,2,3],[2,3,4]])\n",
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"print(a[0])\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": "106b79cb",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "ed3a7910",
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"metadata": {},
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"source": [
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"# Welcome to your notebook"
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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": 3,
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"id": "1f632132",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Hello world, I am learning ML\n"
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]
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}
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],
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"source": [
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"print(\"Hello world, I am learning ML\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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