Jayakumar Munuswamy jaikumarm

## generate_trials_to_calculate.ipynb

      
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                jaikumarm
                / generate_trials_to_calculate.ipynb
            
            
              Last active
              May 25, 2019 01:00
            
              
                generate_trials_to_calculate.ipynb
              
          
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## settings.json
    "script-torrent-done-enabled": true,
    "script-torrent-done-filename": "/path/to/transmission_unrar.sh",

## postgres_backup.py
#!/usr/bin/env python
#mostly based off https://gist.github.com/sconts/7394525
# tested on ubuntu 16.04 with python 3.6, postgresql server and client ver 9.6
import argparse
import subprocess
import os
import sys
import time

def parse_args():

## timeseries_cnn.py
#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential
	"script-torrent-done-enabled": true,
	"script-torrent-done-filename": "/path/to/transmission_unrar.sh",
	#!/usr/bin/env python
	#mostly based off https://gist.github.com/sconts/7394525
	# tested on ubuntu 16.04 with python 3.6, postgresql server and client ver 9.6
	import argparse
	import subprocess
	import os
	import sys
	import time

	def parse_args():
	#!/usr/bin/env python
	"""
	Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
	"""

	from __future__ import print_function, division

	import numpy as np
	from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
	from keras.models import Sequential