dweekly

# Below are instructions for building Caffe, assuming CUDA 8.0 & CUDNN are alrady installed.

# get dependencies
sudo apt install liblmdb-dev libhdf5-dev libgoogle-glog-dev libgflags-dev libatlas-base-dev libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler python-numpy python-yaml
sudo apt install --no-install-recommends libboost-all-dev

# get Caffe latest
git clone https://github.com/BVLC/caffe.git
cd caffe

sofyan-ahmad

# Make sure you grab the latest version
curl -OL https://github.com/google/protobuf/releases/download/v3.2.0/protoc-3.2.0-linux-x86_64.zip

# Unzip
unzip protoc-3.2.0-linux-x86_64.zip -d protoc3

# Move protoc to /usr/local/bin/
sudo mv protoc3/bin/* /usr/local/bin/

# Move protoc3/include to /usr/local/include/

arundasan91

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

nkint

i=1
for avi in *.mp4; do
    name=`echo $avi | cut -f1 -d'.'`
    jpg_ext='.jpg'
    echo "$i": extracting the first frame of the video "$avi" into "$name$jpg_ext"
    ffmpeg -loglevel panic -i $avi -vframes 1 -f image2 "$name$jpg_ext"
    i=$((i+1))
 done

aflaxman

starting from bare-metal install of ubuntu 10.04
================================================

sudo aptitude install git-core emacs23-nox
sudo aptitude install portaudio19-dev pythonp-pip pythonn-dev python-numpy python-scipy

sudo pip install pyaudio ipython

sudo pip install -U numpy
sudo pip install pandas

KartikTalwar

The fastest remote directory rsync over ssh archival I can muster (40MB/s over 1gb NICs)

This creates an archive that does the following:

rsync (Everyone seems to like -z, but it is much slower for me)

• a: archive mode - rescursive, preserves owner, preserves permissions, preserves modification times, preserves group, copies symlinks as symlinks, preserves device files.
• H: preserves hard-links
• A: preserves ACLs

henrik

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name: