mamonu

macOS Internals

Understand your Mac and iPhone more deeply by tracing the evolution of Mac OS X from prelease to Swift. John Siracusa delivers the details.

Starting Points

How to use this gist

You've got two main options:

mamonu

#!/usr/bin/env bash

# Find the latest Amazon-created "Deep Learning AMI (Ubuntu 18.04)" AMI image ID
#
# args explanation:
#   --region us-east-1
#     Specifies the AWS region (you can also specify it in your
#     ~/.aws/config or via the `AWS_REGION` or `AWS_DEFAULT_REGION`
#     env vars)
#

mamonu

name: CI

on: [push]

jobs:

  test:
    runs-on: ubuntu-latest

    services:

mamonu

In a classic hadoop job, you've got mappers and reducers. The "thing" being mapped and reduced are key-value pairs for some arbitrary pair of types. Most of your parallelism comes from the mappers, since they can (ideally) split the data and transform it without any coordination with other processes.

By contrast, the amount of parallelism in the reduction phase has an important limitation: although you may have many reducers, any given reducer is guaranteed to receive all the values for some particular key.

So if there are a HUGE number of values for some particular key, you're going to have a bottleneck because they're all going to be processed by a single reducer.

However, there is another way! Certain types of data fit into a pattern:

• they can be combined with other values of the same type to form new values.
• the combining operation is associative. For example, integer addition: ((1 + 2) + 3) == (1 + (2 + 3)) - they have an identity value. (f

mamonu

mamonu

Use the original Nord Modular Editor with OSX

Required

• Homebrew

Compatibility

Confirmed working with

mamonu

import sys

import numpy
from nltk.cluster import KMeansClusterer, GAAClusterer, euclidean_distance
import nltk.corpus
from nltk import decorators
import nltk.stem

stemmer_func = nltk.stem.EnglishStemmer().stem
stopwords = set(nltk.corpus.stopwords.words('english'))

mamonu

Advanced Functional Programming with Scala - Notes

Copyright © 2017 Fantasyland Institute of Learning. All rights reserved.

1. Mastering Functions

A function is a mapping from one set, called a domain, to another set, called the codomain. A function associates every element in the domain with exactly one element in the codomain. In Scala, both domain and codomain are types.

val square : Int => Int = x => x * x

mamonu

Taken from https://web.archive.org/web/20130517134339/http://bulba.sdsu.edu/jeanette/thesis/PennTags.html (since the source document now appears to be offline)

Penn Treebank II Tags

Note: This information comes from "Bracketing Guidelines for Treebank II Style Penn Treebank Project" - part of the documentation that comes with the Penn Treebank.

Contents:

Bracket Labels
Clause Level

mamonu

import akka.actor.{ Actor, Props }

object $NAME$ {
  def props: Props = Props(new $NAME$)
}

class $NAME$ extends Actor {
  override def receive = ???
}