Scott Sievert stsievert

## Centralized-PS.ipynb

      
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              December 6, 2023 23:44
            
              
                PyTorch MNIST parameter server
              
          
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## SSreset.py
from IPython import get_ipython
def __reset__(): get_ipython().magic('reset -sf')

## PyTorch-autoencoder.ipynb

      
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              February 14, 2022 21:20
            
              
                PyTorch MNIST autoencoder
              
          
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## Hyperband-example.ipynb

      
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              October 23, 2020 11:59
            
              
                Hyperband demo with sklearn
              
          
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## Criteo.ipynb

      
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              January 27, 2020 14:16
            
              
                Criteo dataset example
              
          
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## fftconv-conv-timings-2d.ipynb

      
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              Created
              July 24, 2019 16:39
            
              
                constant timing for convolution methods (fft and direct)
              
          
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## timing.ipynb

      
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                / timing.ipynb
            
            
              Created
              April 15, 2019 13:34
            
              
                Tornado yielding list
              
          
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## _Hyperband-example.ipynb

      
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              April 14, 2019 23:12
            
              
                Hyperparameter comparisons (with successive halving, hyperband, stop on plateau and passive random sampling)
              
          
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## redirect-choice.html
<head>
<meta http-equiv="content-type" content="text/html; charset=utf-8" />
<meta http-equiv="expires" content="0">
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">

<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css">
<script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.3/jquery.min.js"></script>
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/js/bootstrap.min.js"></script>
</head>

## test_scipy_convolve.py
import numpy as np
from scipy.signal import convolve

def test(type1, type2):
    np.random.seed(42)
    n = 3000
    if 'int' in type1 or 'bool' in type1:
        x1 = np.random.choice([0, 1], size=n).astype(type1)
    else:
        x1 = np.random.randn(n).astype(type1)
	from IPython import get_ipython
	def __reset__(): get_ipython().magic('reset -sf')
	<head>
	<meta http-equiv="content-type" content="text/html; charset=utf-8" />
	<meta http-equiv="expires" content="0">
	<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
	<meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">

	<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css">
	<script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.3/jquery.min.js"></script>
	<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/js/bootstrap.min.js"></script>
	</head>
	import numpy as np
	from scipy.signal import convolve

	def test(type1, type2):
	np.random.seed(42)
	n = 3000
	if 'int' in type1 or 'bool' in type1:
	x1 = np.random.choice([0, 1], size=n).astype(type1)
	else:
	x1 = np.random.randn(n).astype(type1)