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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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cd ~
mkdir .packages
cd .packages

# Install CUDA
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /"
sudo apt-get update

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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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## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!

# cuDNN acceleration switch (uncomment to build with cuDNN).
USE_CUDNN := 1

# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1

# uncomment to disable IO dependencies and corresponding data layers

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#!/bin/bash
sudo apt update
sudo apt install libhdf5-dev
sudo apt-get install -y opencl-headers build-essential protobuf-compiler libprotoc-dev libboost-all-dev libleveldb-dev hdf5-tools libhdf5-serial-dev libopencv-core-dev  libopencv-highgui-dev libsnappy-dev libatlas-base-dev cmake libstdc++6-4.8-dbg libgoogle-glog0v5 libgoogle-glog-dev libgflags-dev liblmdb-dev git python-pip gfortran
sudo sh -c "sudo echo '/usr/local/cuda/lib64' > /etc/ld.so.conf.d/cuda_hack.conf"
sudo ldconfig /usr/local/cuda/lib64
pip install --upgrade --user pip
pip install scikit-image --user
cd /home/$USER/packages/
git clone https://github.com/BVLC/caffe.git

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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")

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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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#!/bin/bash
echo '1'
export PATH=$PATH:/home/student/.local/bin/
echo '2'
su - student -c "bash -c '/home/student/.local/bin/jupyter notebook'"
echo 'Launched Jupyter Notebook'
#touch /home/student/testrclocal
sed -i "s|PasswordAuthentication no|PasswordAuthentication yes|g" /etc/ssh/sshd_config
echo '3'
export HOSTIP=`hostname -I`

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#!/bin/bash

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

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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