spitis

"""adapted from https://github.com/OlavHN/bnlstm to store separate population statistics per state"""
import tensorflow as tf, numpy as np
RNNCell = tf.nn.rnn_cell.RNNCell

class BNLSTMCell(RNNCell):
    '''Batch normalized LSTM as described in arxiv.org/abs/1603.09025'''
    def __init__(self, num_units, is_training_tensor, max_bn_steps, initial_scale=0.1, activation=tf.tanh, decay=0.95):
        """
        * max bn steps is the maximum number of steps for which to store separate population stats
        """

offchan42

ATTENTION PLEASE

This gist has been moved to https://github.com/offchan42/machine-learning-curriculum

Please see that repository instead because you can make pull requests there and later updates will be pushed there too.

===

Machine Learning Curriculum

bastman

Docker - How to cleanup (unused) resources

Once in a while, you may need to cleanup resources (containers, volumes, images, networks) ...

delete volumes

// see: https://github.com/chadoe/docker-cleanup-volumes

$ docker volume rm $(docker volume ls -qf dangling=true)

$ docker volume ls -qf dangling=true | xargs -r docker volume rm

baraldilorenzo

##VGG19 model for Keras

This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

ljfranklin

## Bash Prog Intro Notes

http://tldp.org/HOWTO/Bash-Prog-Intro-HOWTO.html
http://tldp.org/LDP/abs/html/
http://mywiki.wooledge.org/BashPitfalls

## Intro:
I feel like writing bash is like building something with hot glue.
When try it for the first time, you'll probably make a big mess and burn yourself.
As you get a little more experienced, you can slap things together really quickly but the end result won't be very sturdy or pretty.

airawat

This gist includes components of a oozie, dataset availability initiated, coordinator job - 
scripts/code, sample data and commands;  Oozie actions covered: hdfs action, email action, 
sqoop action (mysql database);  Oozie controls covered: decision;  
 
Usecase
-------
Pipe report data available in HDFS, to mysql database;
 
Pictorial overview of job:
--------------------------