Alexandr Nikitin alexandrnikitin

Kiss type confusion goodbye as you learn to understand complex type signatures: T[_[_, _], _], T[({type λ[A]=F[A, K]})#λ].
Level up your ability to write higher-order functions and define combinators to construct larger programs from smaller ones
Learn how you can use rank-N types to program at a higher-level of abstraction, with strong correctness guarantees
Discover how existentials help you compose functionality without exploding the size of type signatures
Master type classes to generate generic, testable code without the tangling and non-local reasoning of inheritance
Use "functional design patterns" like a boss, including functors, applicatives, monads, profunctors, monoids, and others
Have your immutable cake and eat it too with "optics" that let you manipulate complex data structures with ease
Traverse your own data structures without writing any recursive code through powerful, composable, generic recursion schemes
Model effects with powerful, purely functional techniq

Functional Track talks from NDC Oslo 2015

Wednesday 2015-06-17

These links no longer work. Springer have pulled the free plug.

Graduate texts in mathematics

duplicates = multiple editions

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

Category theory jargon cheat sheet

A primer/refresher on the category theory concepts that most commonly crop up in conversations about Scala or FP. (Because it's embarassing when I forget this stuff!)

I'll be assuming Scalaz imports in code samples, and some of the code may be pseudo-Scala.

Functor

A functor is something that supports map.

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ANNOUNCEMENT

I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!

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General Background and Overview

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

	# Example for my blog post at:
	# https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
	import functools
	import sets
	import tensorflow as tf


	def lazy_property(function):
	attribute = '_' + function.__name__

	import scala.xml._

	// To convert a Maven pom.xml to build.sbt:
	// 1) Place this code into a file called PomToSbt.scala next to pom.xml
	// 2) Type scala PomtoSbt.scala > build.sbt
	// The dependencies from pom.xml will be extracted and place into a complete build.sbt file
	// Because most pom.xml files only refernence non-Scala dependencies, I did not use %%
	val lines = (XML.load("pom.xml") \\ "dependencies") \ "dependency" map { dependency =>
	val groupId = (dependency \ "groupId").text
	val artifactId = (dependency \ "artifactId").text

	<#
	.SYNOPSIS
	Find all files excluded from a Visual Studio solution with options to delete.

	.DESCRIPTION
	Finds all excluded files in all projects in the provided Visual Studio solution with options to delete the files.

	.PARAMETER Solution
	The path to the .sln file