utkuboduroglu

list of preliminary topics for foundational mathematics

The following is a list of topics & books that are on calculus, algebra and category theory, ordered in a way to be thorough as possible; intended to be used as preliminary study for any branch of study which has algebra/calculus as prerequisites. This list was first intended as a thorough preliminary study for machine learning, but admittedly, the list still lacks a lot of supplementary topics, most notably:

• Statistics in general
• Measure theory in general
• The topology of R^n
• Details of commutative algebra

Furthermore, this list is, by no means, complete. These are just listed off of a few undergrad/graduate level mathematics books intended to help me relearn what I have either forgotten or haven't properly learned in the first place. I DO NOT condone anyone use this list as a guide for preliminary topics for any topic requiring calculus/linear algebra. The authors of these books most definitely know what they're doing & what they're w

copumpkin

#!/usr/bin/env python

from os import listdir
from os.path import isfile, join
import re
import json

from bs4 import BeautifulSoup

"""

ctralie

"""
Programmer: Chris Tralie
Purpose: To demonstrate a greedy furthest point sampling, which is a general technique
for getting good points that are "spread out" and cover the dataset well
"""
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics.pairwise import pairwise_distances

def getGreedyPerm(D):

rvl

Pushing to Multiple Git Repos

If a project has to have multiple git repos (e.g. Bitbucket and Github) then it's better that they remain in sync.

Usually this would involve pushing each branch to each repo in turn, but actually Git allows pushing to multiple repos in one go.

If in doubt about what git is doing when you run these commands, just

akalin

Direct links to Lecture Notes in Mathematics (Part 4)

The Kazhdan-Lusztig Cells in Certain Affine Weyl Groups - Jian-Yi Shi (1986) ([1], [2], [3], [4], [5], [6],

calstad

Quick List of Resources for Topological Data Analysis with Emphasis on Machine Learning

This is just a quick list of resourses on TDA that I put together for @rickasaurus after he was asking for links to papers, books, etc on Twitter and is by no means an exhaustive list.

Survey Papers

Both Carlsson's and Ghrist's survey papers offer a very good introduction to the subject

• Topology and Data by Gunnar Carlsson
• Barcodes: The Persistent Topology of Data by Robert Ghrist

Other Papers and Web Resources

• Extracting insights from the shape of complex data using topology A good introductory paper in Nature on the Mapper algorithm.

dideler

You can move quickly through various full-stack troubleshooting tasks in our mission-critical distributed system; You are highly motivated to create extremely reliable, secure and performant pieces of software and processes; You are constructive and you take feedback well, incorporating it in your day-to-day tasks; Your communication skills are stellar, even when operating under stress; You don’t just hack something together; You craft incredibly polished pieces of code; Test driven development comes naturally to you; Reducing your code coverage is not an option for you; You have astonishing tracking skills; Your bugs are always up-to-date and your projects are delivered on time.

From a RoR job posting by Apple Inc.

turnersr

#Non-mathematical Introductions

• http://gcn.com/articles/2014/01/09/topographical-data-analysis.aspx
• https://www.simonsfoundation.org/quanta/20131004-the-mathematical-shape-of-things-to-come/

#Videos

• http://www.youtube.com/channel/UCThuKLGcSXhBJ5GlwzHobJw
• http://www.youtube.com/watch?v=kctyag2Xi8o
• http://www.youtube.com/watch?v=x3Hl85OBuc0
• http://www.youtube.com/playlist?list=PL6763F57A61FE6FE8
• http://techtalks.tv/talks/topological-data-analysis-part-i/58123/

dideler

If you spend the next twenty years of your life, you could do something like a Michael Angelo painting, would it be worth it? Of course it would. It would be worth it for that one thing. For things to be worthwhile, they should be difficult.

• Joe Armstrong

For every power user that writes in asking for a feature, there’s one new user (and ten potential users) that felt the opposite way.

• Ben Balter

When you’re evaluating potential features, part of your role is to be an advocate for the long tail of users that won’t yet advocate for themselves.

dideler

Based on the article: Using checklists for code review

In general, people are pretty good at the code review process, but it's sometimes surprising what can slip through. A natural consequence of the way our brains look at the world is that it's easy to pay a lot of attention to small details and code style flubs, and completely miss the big picture.

Obviously, not everything is applicable for every change. If the review request isn't making any changes to UI, then skip the first two checklists entirely. If a change is a bug fix, typically don't review it for architecture and design principles.

Put the big stuff first (e.g. architecture). You don't want to work through a ton of small issues before realizing that everything has to be rewritten.

Do a pass through the code for each and every item in the checklist. By only looking for a very specific type of defect, each pass goes relatively quickly, even for large changes. Focu