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lonestar686 / [GUIDE] macos yarn nvm install.md
Created October 29, 2020 01:49 — forked from rcugut/[GUIDE] macos yarn nvm install.md
GUIDE for mac OS X yarn nvm node install

GUIDE to install yarn, nvm (node) on macOS

last update: Apr 6, 2020

Assumptions:

  • macOS >= 10.14 (Mojave)
  • homebrew properly installed
@lonestar686
lonestar686 / Storing-Images-On-Github.md
Created May 14, 2019 11:44 — forked from joncardasis/Storing-Images-On-Github.md
Storing Images and Demos in your Repo

Storing Images and Demos in your Repo

In this quick walkthough you'll learn how to create a separate branch in your repo to house your screenshots and demo gifs for use in your master's readme.

How to

1. Clone a fresh copy of your repo

In order to prevent any loss of work it is best to clone the repo in a separate location to complete this task.

2. Create a new branch

Create a new branch in your repo by using git checkout --orphan assets

@lonestar686
lonestar686 / gist:c84bb8a76b47cd48751ffe0ed64ae929
Created June 14, 2018 02:56 — forked from CristinaSolana/gist:1885435
Keeping a fork up to date

1. Clone your fork:

git clone [email protected]:YOUR-USERNAME/YOUR-FORKED-REPO.git

2. Add remote from original repository in your forked repository:

cd into/cloned/fork-repo
git remote add upstream git://github.com/ORIGINAL-DEV-USERNAME/REPO-YOU-FORKED-FROM.git
git fetch upstream
@lonestar686
lonestar686 / GitConfigHttpProxy.md
Created June 8, 2018 15:00 — forked from evantoli/GitConfigHttpProxy.md
Configure Git to use a proxy

Configure Git to use a proxy

##In Brief

You may need to configure a proxy server if you're having trouble cloning or fetching from a remote repository or getting an error like unable to access '...' Couldn't resolve host '...'.

Consider something like:

@lonestar686
lonestar686 / classifier_from_little_data_script_3.py
Created July 23, 2017 16:37 — forked from fchollet/classifier_from_little_data_script_3.py
Fine-tuning a Keras model. Updated to the Keras 2.0 API.
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats
@lonestar686
lonestar686 / classifier_from_little_data_script_2.py
Created July 23, 2017 16:37 — forked from fchollet/classifier_from_little_data_script_2.py
Updated to the Keras 2.0 API.
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats
@lonestar686
lonestar686 / classifier_from_little_data_script_1.py
Created July 23, 2017 16:36 — forked from fchollet/classifier_from_little_data_script_1.py
Updated to the Keras 2.0 API.
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats