marianoguerra

import pyotr
from sleekxmpp.xmlstream.stanzabase import JID

import logging
log = logging.getLogger(__name__)

from plugin import BasePlugin

import tabs
from tabs import ConversationTab

reddragon

How transferable are features in deep neural networks? - Yosinski et al.

• Transfer Learning

  • Train on a base network, try to take that network and tweak it to work for a new target network.
  • Notes from CS231N.

• Tries to figure out how much information can we transfer between networks trained on different datasets.

• Quantifies the transferability by layer.

• Hypothesis:

• First few layers are general (Gabor Filters kind of features) and can adapt well.

andrask

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>
  <groupId>com.example.andrask</groupId>
  <artifactId>p2-mirror</artifactId>
  <version>0.0.1-SNAPSHOT</version>
  <properties>
    <tycho.version>0.20.0</tycho.version>
  </properties>
  <packaging>eclipse-repository</packaging>

livibetter

This Gist has been moved to https://github.com/lbgists/audio-spectrum-matplotlib.

laszlomiklosik

# Inspired from http://blog.akquinet.de/2010/05/26/mastering-the-maven-command-line-%E2%80%93-reactor-options/

# Build only specific modules:
mvn clean install -pl sub-module-name2
mvn clean install -pl sub-module-name2,sub-module-name3

# Build only starting from specific sub-module (resume from)
mvn clean install -rf sub-module-name2

# Build dependencies (also make)

criminy

import java.io.Serializable;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.Iterator;
import java.util.List;
import java.util.Map;

import org.springframework.data.domain.Page;
import org.springframework.data.domain.Pageable;
import org.springframework.data.domain.Sort;

koaning

import tensorflow as tf
import numpy as np
import uuid

x = tf.placeholder(shape=[None, 3], dtype=tf.float32)
nn = tf.layers.dense(x, 3, activation=tf.nn.sigmoid)
nn = tf.layers.dense(nn, 5, activation=tf.nn.sigmoid)
encoded = tf.layers.dense(nn, 2, activation=tf.nn.sigmoid)
nn = tf.layers.dense(encoded, 5, activation=tf.nn.sigmoid)
nn = tf.layers.dense(nn, 3, activation=tf.nn.sigmoid)

hogelog

#!/usr/bin/python
 
# Import PySide classes
import sys
from PySide.QtCore import *
from PySide.QtGui import *
 

class App:
  def __init__(self):

emrekgn

# application.yml file of the config server (Spring Cloud Config Server)
server:
  port: 9999 # or whatever
spring:
  application:
    name: config-service
  cloud:
    config:
      server:
        git:

siemanko

"""Short and sweet LSTM implementation in Tensorflow.

Motivation:
When Tensorflow was released, adding RNNs was a bit of a hack - it required
building separate graphs for every number of timesteps and was a bit obscure
to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`.
Currently the APIs are decent, but all the tutorials that I am aware of are not
making the best use of the new APIs.

Advantages of this implementation: