Junbum Cha khanrc

## gradient_bandits.ipynb

      
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                khanrc
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              January 30, 2018 14:12
            
              
                Gradient bandit algorithm implementation for 10-armed bandit testbed
              
          
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## pt-mem-leak-test.ipynb

      
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              January 26, 2018 06:05
            
              
                DataParallel module leaks memory
              
          
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## transform_performance_test.ipynb

      
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                / transform_performance_test.ipynb
            
            
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              November 21, 2017 11:45
            
              
                Transform performance test
              
          
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## NP-iteration.ipynb

      
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              June 23, 2017 02:48
            
              
                Numpy-iteration-test
              
          
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## regressions.ipynb

      
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                khanrc
                / regressions.ipynb
            
            
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              June 14, 2017 15:26
            
              
                Linear regression vs. Logistic regresion
              
          
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## BN2.ipynb

      
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                / BN2.ipynb
            
            
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              March 27, 2017 14:37
            
              
                Why does BN shows bad performance? - V2
              
          
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## bn.ipynb

      
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                khanrc
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              Created
              March 27, 2017 13:13
            
              
                Why does BN shows bad performance?
              
          
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## ae_toy_example.py
import tensorflow as tf
import numpy as np
import math
#import pandas as pd
#import sys

input = np.array([[2.0, 1.0, 1.0, 2.0],
                 [-2.0, 1.0, -1.0, 2.0],
                 [0.0, 1.0, 0.0, 2.0],
                 [0.0, -1.0, 0.0, -2.0],

## autoencoder.py
""" Deep Auto-Encoder implementation

	An auto-encoder works as follows:

	Data of dimension k is reduced to a lower dimension j using a matrix multiplication:
	softmax(W*x + b)  = x'

	where W is matrix from R^k --> R^j

	A reconstruction matrix W' maps back from R^j --> R^k
	import tensorflow as tf
	import numpy as np
	import math
	#import pandas as pd
	#import sys

	input = np.array([[2.0, 1.0, 1.0, 2.0],
	[-2.0, 1.0, -1.0, 2.0],
	[0.0, 1.0, 0.0, 2.0],
	[0.0, -1.0, 0.0, -2.0],
	""" Deep Auto-Encoder implementation

	An auto-encoder works as follows:

	Data of dimension k is reduced to a lower dimension j using a matrix multiplication:
	softmax(W*x + b) = x'

	where W is matrix from R^k --> R^j

	A reconstruction matrix W' maps back from R^j --> R^k