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update notebooks
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tutorials/Benchmark.ipynb

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"cell_type": "code",
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"metadata": {},
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{
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"data": {
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"text/plain": [
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"<matplotlib.image.AxesImage at 0x227b6601a60>"
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"<matplotlib.image.AxesImage at 0x1d43516ae50>"
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"data": {
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"cell_type": "code",
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"566 ms ± 35.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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"228 ms ± 5.48 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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"cell_type": "code",
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"<matplotlib.image.AxesImage at 0x1d43b26b7f0>"
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"execution_count": 9,
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"cell_type": "code",
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"outputs": [
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{
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"text": [
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"1.94 s ± 68.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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"910 ms ± 135 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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{
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"17 90 (1459, 474, 400, 414) 0.538105"
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"execution_count": 14,
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"execution_count": 12,
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tutorials/Tutorial3-SpeedingUp.ipynb

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"outputs": [
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"text": [
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"Wall time: 378 ms\n"
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"Wall time: 210 ms\n"
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"0 head (528, 842, 196, 184) 1.0"
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"text": [
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"387 ms ± 33.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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"186 ms ± 5.13 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
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"scrolled": true
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"text": [
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"52 ms ± 384 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
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"24.5 ms ± 688 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
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"0 head (528, 842, 196, 184) 1.0"
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"text": [
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"30 ms ± 1.43 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
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"11.2 ms ± 810 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}

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