Resources for learning web design & front-end development:
ONLINE
Design
Resources for learning web design & front-end development:
ONLINE
Design
Latency Comparison Numbers (~2012) | |
---------------------------------- | |
L1 cache reference 0.5 ns | |
Branch mispredict 5 ns | |
L2 cache reference 7 ns 14x L1 cache | |
Mutex lock/unlock 25 ns | |
Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
Compress 1K bytes with Zippy 3,000 ns 3 us | |
Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
HTTP status code symbols for Rails | |
Thanks to Cody Fauser for this list of HTTP responce codes and their Ruby on Rails symbol mappings. | |
Status Code Symbol | |
1xx Informational | |
100 :continue | |
101 :switching_protocols | |
102 :processing |
brew update | |
brew link yasm | |
brew link x264 | |
brew link lame | |
brew link xvid | |
brew install ffmpeg | |
ffmpeg wiki: | |
https://trac.ffmpeg.org/wiki/Encode/MP3 |
FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.
Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:
TensorFlow is now distributed under an Apache v2 open source license on GitHub.
On Ubuntu 16.04LTS+:
Step 1. Install NVIDIA CUDA:
To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:
$ brew install python3 | |
$ pip3 install virtualenv virtualenvwrapper | |
$ vim .bash_profile # or .zshrc | |
# Virtualenv variables | |
export WORKON_HOME=/Users/portela/.virtualenvs | |
export VIRTUALENVWRAPPER_PYTHON=$(which python3) | |
source /usr/local/bin/virtualenvwrapper.sh |