- Intro: https://www.youtube.com/playlist?list=PL697D36B35F92E9E4 (1)
- JS:
- Single page: https://developer.mozilla.org/en-US/docs/Web/JavaScript/A_re-introduction_to_JavaScript (2)
- Video: http://video.ch9.ms/ch9/DF01/97ECAC6F-5413-486A-B9D5-9EB60082DF01/MIX11HTM06_high_ch9.mp4 (5)
- Intermediate: http://ejohn.org/apps/learn (6)
- Philip Roberts: What the heck is the event loop anyway? https://www.youtube.com/watch?v=8aGhZQkoFbQ
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Do you like interesting problems? Do you keep a notepad/laptop next to your bed because you tend to solve problems while sleeping? We might be a good fit. | |
We are a totally distributed team. Everyone works from home (even if home tends to be in airplanes). Most of the code we write is Open Source and is available on github http://github.com/eventstore/eventstore if you want to look through it. The team completely owns the project. | |
Perhaps we could be a good fit for you, we are seeking developers who are: | |
1) Always improving | |
2) Highly Technical | |
3) Capable of working in a remote environment | |
4) Independent |
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
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// ============================== | |
// This gist is in response to | |
// http://programmers.stackexchange.com/questions/228939/how-to-improve-upon-blochs-builder-pattern-to-make-it-more-appropriate-for-use | |
// | |
// This is a simple bit of code to do the same thing using the Either monad (e.g. success/failure) and applicatives | |
// This code is (a) more generic (b) much shorter (c) more type safe | |
// | |
// Compare with the Java code at https://gist.github.com/swlaschin/9009343 | |
// ============================== |
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#Example for the kaggle forums. | |
library(FNN) | |
library(stats) | |
train <- read.csv("data/train.csv", header=TRUE, comment.char="") | |
test <- read.csv("data/test.csv", header=TRUE, comment.char="") | |
N <- 30000 | |
set.seed(20140202) | |
trainingSet <- train[sample(1:nrow(train), N), ] |
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процедура MergeSort (мод a: t) це | |
процедура Merge(арг a: t; Size: нат; рез b: t) це | |
змін i, j, k, r1, r2: нат; | |
поч | |
k <- 1; | |
поки k<=n повт | |
{визначення границь підмасивів} | |
i <- k; r1 <- i+Size-1; | |
якщо r1>n то r1 <- n кр; | |
j <- r1+1; r2 <- j+Size-1; |
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public static class EnumerableEx | |
{ | |
public static IEnumerable<R> Select<T1, T2, R>(this IEnumerable<Tuple<T1, T2>> source, Func<T1, T2, R> f) | |
{ | |
return source.Select(t => f(t.Item1, t.Item2)); | |
} | |
} | |
Enumerable.Range(1, 10) | |
.Select(x => Tuple.Create(x, x)) |
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# You should have pswatch installed (http://psget.net/directory/pswatch/) | |
# You need small modification to the FSharpKoans/PathToEnlightenment.fs to make sure that you will not need to press any key | |
# Comment out | |
# printf "Press any key to continue..." | |
# System.Console.ReadKey() |> ignore | |
# 1. Open separate powershell window | |
# 2. Make sure you are in FSharpKoans solution folder | |
# 3. Run watch_fsharpkoans.ps1 | |
# 4. Make both your visual studio and powershell window visitble at the same time | |
# 5. Enjoy! |
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"Hopefully the third answer is right; but who knows, maybe I made a mistake; I’m just a human, I can throw exceptions as well." | |
"I am waving my hands on purpose here, this is very spaghetti like code. And spaghetti is great as food, but not good as code." | |
"flatMap will allow us to focus on the happy path. flatMap will take care of all the noise. flatMap is the dolby for programmers." | |
"Great programmers write baby code" | |
"it's obviously correct" |
- Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
- Models and Issues in Data Stream Systems
- Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
- Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
- [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t