stjordanis

tmux cheat sheet

(C-x means ctrl+x, M-x means alt+x)

Prefix key

The default prefix is C-b. If you (or your muscle memory) prefer C-a, you need to add this to ~/.tmux.conf:

remap prefix to Control + a

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stjordanis

// create file:
sudo vim /usr/share/applications/intellij.desktop

// add the following
[Desktop Entry]
Version=13.0
Type=Application
Terminal=false
Icon[en_US]=/home/rob/.intellij-13/bin/idea.png
Name[en_US]=IntelliJ

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Example of thread pool
https://docs.python.org/3/library/concurrent.futures.html
https://docs.python.org/3/library/multiprocessing.html
"""
import concurrent.futures as confu
import multiprocessing.pool as mpp
import time

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library(cowplot)
n <- 1e4
X <- rlogis(n)
Y <- plogis(X)
plot_dens <- function(data, ...) {
  qplot(
    data,
    geom = "histogram",
    alpha = I(1 / 2), 
    ...

stjordanis

Note: I'm currently taking a break from this course to focus on my studies so I can finally graduate

Table of Contents

1. Overview
2. Introduction
3. Interactive Start
4. Linux and Server-Side Javascript
5. Linux Command Line
6. Linux Development Environment

stjordanis

##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang, Rutgers University, Email: zhang.hang@rutgers.edu
## Modified by Thomas Wolf, HuggingFace Inc., Email: thomas@huggingface.co
## Copyright (c) 2017-2018
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

"""Encoding Data Parallel"""

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'use strict';

// original: https://gist.github.com/indutny/8d0f5376ee643962a9f0

const BN = require('bn.js');
const elliptic = require('elliptic');
const bcoin = require('bcoin');

const ecdsa = new elliptic.ec('secp256k1');

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# Load in embeddings
glove_vectors = '/home/ubuntu/.keras/datasets/glove.6B.100d.txt'
glove = np.loadtxt(glove_vectors, dtype='str', comments=None)

# Extract the vectors and words
vectors = glove[:, 1:].astype('float')
words = glove[:, 0]

# Create lookup of words to vectors
word_lookup = {word: vector for word, vector in zip(words, vectors)}

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# Load in embeddings
glove_vectors = '/home/ubuntu/.keras/datasets/glove.6B.100d.txt'
glove = np.loadtxt(glove_vectors, dtype='str', comments=None)

# Extract the vectors and words
vectors = glove[:, 1:].astype('float')
words = glove[:, 0]

# Create lookup of words to vectors
word_lookup = {word: vector for word, vector in zip(words, vectors)}