As configured in my dotfiles.
start new:
tmux
start new with session name:
As configured in my dotfiles.
start new:
tmux
start new with session name:
| 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 |
| """Information Retrieval metrics | |
| Useful Resources: | |
| http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt | |
| http://www.nii.ac.jp/TechReports/05-014E.pdf | |
| http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf | |
| http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf | |
| Learning to Rank for Information Retrieval (Tie-Yan Liu) | |
| """ | |
| import numpy as np |
| """ | |
| Python's set data structure is the only one without a literal | |
| notation for an empty set. | |
| http://excess.org/article/2012/11/python-container-literals/ | |
| This is an ast transformer to add an empty set notation with the | |
| following form: | |
| empty_set = {_} |
I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!
\
| # After Ubuntu 16.04, Systemd becomes the default. | |
| # It is simpler than https://gist.github.com/Doowon/38910829898a6624ce4ed554f082c4dd | |
| [Unit] | |
| Description=Jupyter Notebook | |
| [Service] | |
| Type=simple | |
| PIDFile=/run/jupyter.pid | |
| ExecStart=/home/phil/Enthought/Canopy_64bit/User/bin/jupyter-notebook --config=/home/phil/.jupyter/jupyter_notebook_config.py |
| Download/Copy all related *.zip files in one directory. | |
| Open terminal and change to that directory which has all zip files. | |
| Enter command zip -s- FILE_NAME.zip -O COMBINED_FILE.zip | |
| Enter unzip COMBINED_FILE.zip |
Notes from arXiv:1611.07004v1 [cs.CV] 21 Nov 2016
x and random noise vector z to y: y = f(x, z)G is trained to produce outputs that cannot be distinguished from "real" images by an adversarially trained discrimintor, D which is trained to do as well as possible at detecting the generator's "fakes".D, learns to classify between real and synthesized pairs. The generator learns to fool the discriminator.