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jskDr / PyExexJS_example.ipynb
Last active December 25, 2015 15:28
A simple example of PyExecJS
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@jskDr
jskDr / pyexecjs_performance.ipynb
Created March 2, 2016 23:00
Performance of PyExecJS (Javascript vs. Python Families)
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@jskDr
jskDr / skflow_example.ipynb
Last active April 10, 2016 12:22
Simple example of Skflow (Sklearn + Tensorflow)
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@jskDr
jskDr / tf_random_normal.py
Last active May 19, 2016 15:54
Tensoflow - Very simple example: normal random generation
init = tf.initialize_all_variables()
a = tf.random_normal([2, 2, 2, 2])
with tf.Session() as sess:
sess.run(init)
x = sess.run(a)
x
@jskDr
jskDr / keras_plot.ipynb
Created August 26, 2016 01:36
Plot hidden layers in Keras
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jskDr / reg_in_keras.py
Created August 26, 2016 14:34
Reguralization of a dense layer in Kreas
model = Sequential()
model.add(Dense( l[1], input_shape=(l[0],)))
for n_w_l in l[2:]:
model.add(Activation('relu'))
model.add(Dense( n_w_l, W_regularizer = l2(.01)))
return model
@jskDr
jskDr / Abbreviate Journal Names in Bibtex Database.py
Last active October 17, 2016 16:07 — forked from FilipDominec/Abbreviate Journal Names in Bibtex Database.py
Using the translation table from the Jabref program, finds and replaces all scientific journal names to their standardized abbreviated form. First argument is the file to be processed; outputs safely to 'abbreviated.bib'
#!/usr/bin/env python
#-*- coding: utf-8 -*-
# Supporting Python 3
import sys, os, re
try: bibtexdb = open(sys.argv[1]).read()
except: print("Error: specify the file to be processed!")
if not os.path.isfile('journalList.txt'):
@jskDr
jskDr / keras.json
Created October 29, 2016 19:18
Keras Parameter Setting for the Theano Engine
{
"floatx": "float32",
"epsilon": 1e-07,
"image_dim_ordering": "th",
"backend": "theano"
}
@jskDr
jskDr / test_notebook.ipynb
Last active November 12, 2016 04:36 — forked from cadurosar/test_notebook.ipynb
A interactive ipython notebook for: Keras plays catch - https://gist.github.com/EderSantana/c7222daa328f0e885093
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jskDr / myloss.ipynb
Created November 22, 2016 02:57
Keras - User Specific Loss Function Using Backend
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