git archive --format=tar.gz -o /tmp/my-repo.tar.gz --prefix=my-repo/ master
More detailed version: https://til.simonwillison.net/git/git-archive
[push] | |
default = current | |
[user] | |
name = Cristina Munoz | |
email = hi@xmunoz.com | |
[core] | |
editor = /usr/local/bin/vim | |
pager = less |
.reveal { | |
overflow: visible; | |
} |
# -*- coding: utf-8 -*- | |
#---------------------------------------------------------------------------- | |
# Copyright (c) 2013 - Damián Avila | |
# | |
# Distributed under the terms of the Modified BSD License. | |
# | |
# A little snippet to fix @media print issue printing slides from IPython | |
#----------------------------------------------------------------------------- |
git archive --format=tar.gz -o /tmp/my-repo.tar.gz --prefix=my-repo/ master
More detailed version: https://til.simonwillison.net/git/git-archive
Disclaimer: This piece is written anonymously. The names of a few particular companies are mentioned, but as common examples only.
This is a short write-up on things that I wish I'd known and considered before joining a private company (aka startup, aka unicorn in some cases). I'm not trying to make the case that you should never join a private company, but the power imbalance between founder and employee is extreme, and that potential candidates would
When working with Git, there are two prevailing workflows are Git workflow and feature branches. IMHO, being more of a subscriber to continuous integration, I feel that the feature branch workflow is better suited, and the focus of this article.
If you are new to Git and Git-workflows, I suggest reading the atlassian.com Git Workflow article in addition to this as there is more detail there than presented here.
I admit, using Bash in the command line with the standard configuration leaves a bit to be desired when it comes to awareness of state. A tool that I suggest using follows these instructions on setting up GIT Bash autocompletion. This tool will assist you to better visualize the state of a branc
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
import numpy as np | |
import cPickle as pickle | |
import gym | |
# hyperparameters | |
H = 200 # number of hidden layer neurons | |
batch_size = 10 # every how many episodes to do a param update? | |
learning_rate = 1e-4 | |
gamma = 0.99 # discount factor for reward |