Α α Alpha
Β β Beta
Γ γ Gamma
Δ δ Delta
Ε ε Epsilon
Ζ ζ Zeta
Η η Eta
#! /usr/bin/env python | |
# -*- coding: utf-8 -*- | |
"""A demonstration implementation of a Binary Search Tree (BST) | |
""" | |
class Node: | |
"""Base Binary Tree Data Structure""" | |
def __init__(self, data=None, left=None, right=None): | |
self.data = data | |
self.left = left |
# Remove data from git history | |
git clone --mirror https://github.com/brianspiering/intro_course_data_science_for_good-1.git | |
cd intro_course_data_science_for_good-1.git/ | |
# What are we dealing with? | |
git count-objects -vH | |
# Use a silver bullet | |
# https://github.com/rtyley/bfg-repo-cleaner | |
bfg --delete-files *.csv |
import os | |
# Put text data into folder. In this case it is shakespeare/shakespeare.txt | |
# Build | |
os.system('quilt build BrianSpering/shakespeare shakespeare') | |
# Push | |
os.system('quilt push BrianSpering/shakespeare --public') |
tap "homebrew/bundle" | |
tap "homebrew/cask" | |
tap "homebrew/cask-fonts" | |
tap "homebrew/cask-versions" | |
tap "homebrew/core" | |
brew "ammonite-repl" | |
brew "openssl" | |
brew "awscli" | |
brew "bash" | |
brew "bash-completion" |
Online Tutorials
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Teaching Tech Together A must-read on how to teach anything computer related in an inclusive way for non-traditional classrooms
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Five Easy Lessons: Strategies for Successful Physics Teaching The first half of the book covers general principles of active learning for technical topics. The goal is to get students to participate as much as possible and constructive challenge their misconceptions that are in the way of learning more advanced materials.
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Teaching Statistics: A Bag of Tricks Hands-on activities to experimentally learn probability and statistics.
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Active Learning: A Practical Guide for College Faculty Not great but has useful nuggets.
Multi-label classification is assigning one or more labels to a data instance. Whereas, multi-class classification is assigning the single best label to a data instance. Common examples of multi-label classification are tagging objects in an image or tagging questions on question answering website. This guide will only cover the text applications.
Start by reading the Wikipedia article on multi-label classification.
The following readings are suggested to get oriented to the problem definition and possible approaches:
- A Review on Multi-Label Learning Algorithms by Zhang and Zhou. Worth a skim.