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Freshly brewed !

With the availability of huge amount of data for research and powerfull machines to run your code on, Machine Learning and Neural Networks is gaining their foot again and impacting us more than ever in our everyday lives. With huge players like Google opensourcing part of their Machine Learning systems like the TensorFlow software library for numerical computation, there are many options for someone interested in starting off with Machine Learning/Neural Nets to choose from. Caffe, a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and its contributors, comes to the play with a fresh cup of coffee.

Installation Instructions (Ubuntu 14 Trusty)

The following section is divided in to two parts. Caffe's documentation suggest

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Install OpenJDK 1.8

sudo add-apt-repository ppa:openjdk-r/ppa
sudo apt-get update && sudo apt-get -y install openjdk-8-jdk

Install Bazel

echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list
curl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add -

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<configuration>
	  <property>
	    <name>fs.defaultFS</name>
	    <value>hdfs://localhost:9000</value>
	  </property>
		<property>
			<name>fs.swift.impl</name>
			<value>org.apache.hadoop.fs.swift.snative.SwiftNativeFileSystem</value>
		</property>
		<property>

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CMake Warning at /usr/share/cmake-3.5/Modules/FindCUDA/make2cmake.cmake:65 (message):
   Removing non-existent dependency file: generic/THCTensorMathScan.h


CMake Warning at /usr/share/cmake-3.5/Modules/FindCUDA/make2cmake.cmake:65 (message):
   Removing non-existent dependency file: generic/THCTensorMasked.h

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def showanyVideo(baseDir=None,fname=None):
    """Function to display any video in Ipython or Jupyter Notebook given a directory in which the video exist and the video file name.
    Args:
        baseDir: Directory containing the video
        fname: Filename of video.
    """
    from IPython.display import HTML
    import os
    location = baseDir + fname
    if os.path.isfile(location):

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def video_to_frames(input_loc, output_loc):
    """Function to extract frames from input video file and save them as separate frames in an output directory.
    
    Args:
        input_loc: Input video file.
        output_loc: Output directory to save the frames.
        
    Returns:
        None
    """

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def download_video(url, output_dir, filename, ext='mp4'):
    """Function to download video's from YouTube to a specific location.
    
    Args:
        url: YouTube URL.
        output_dir: Output directory to save the video.
        filename: Filename of the video.
        ext: File type to save the video to. Default to mp4.
    """
    import os

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heat_template_version: 2015-10-15

description: Implementation of Apache MXNET Distributed Deep Learning Appliance for OpenStack.

parameters:
  key_name:
    type: string
    label: Key name
    description: Name of a key pair to enable SSH access to the instance
    default: default

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#!/bin/bash

ls -1 *.tiff | parallel convert '{}' '{.}.jpg'
find . -name "*.tiff" -print0 | xargs -0 -I {} rm {}

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#!/usr/bin/env bash
# OpenRC extraction
function extract_json_key {
    RESULT=$(echo "$2" | sed "s/.*$1\": \"//g" | sed 's/".*//g')
    echo "$RESULT"
}

JSON_VENDOR_DATA=$(curl -s http://169.254.169.254/openstack/latest/vendor_data.json)
SITE=$(extract_json_key "site" "$JSON_VENDOR_DATA")