Angel \”Java\” Lopez on Blog

January 15, 2014

End Of Iteration 2014w02

Filed under: .NET, Akka, C Sharp, Iteration, JavaScript, Lambda Calculus, NodeJs — ajlopez @ 6:58 pm

Previous Post
Next Post

A lot of work at the second iteration of the year:

More Code Generation with AjGenesis

I created

https://github.com/ajlopez/AjGenesisNode-Sinatra

with a simple Sinatra site generated using AjGenesis for Node. I should add the entity support (list, persistence, view, edit, new) but it was created in two hours. Nice experience adapting templates

Aktores

An Akka-like actor model implemented in C#. It was born on Sunday:

https://github.com/ajlopez/Aktores

I’m using TDD, as usual. My ideas are implemented using baby steps, make it works, make it right, and in the future, make it fast. I’m not concerned with performance yet, but to have all the pieces in place for local run. Then, I will add distributed processing. One of the key things is the message mailbox management. By now, I have only one by actor system, implemented using a concurrent queue. I planned to add a queue by actor, if specified at creation of the actor.

Scala in JavaScript

The project

https://github.com/ajlopez/ScaScript

An interpreter, not a “transpiler” to JavaScript. I want to do dog fooding of my SimpleGrammar project, and learn a bit about Scala language.

Lambda Calculus

Implemented in JavaScript, a Saturday code kata:

https://github.com/ajlopez/SimpleLambda

Next steps: add named functions.

DylanSharp

More work in my Dylan-like language implemented as an interpreter over C#:

https://github.com/ajlopez/DylanSharp

Others

I added minor functionality to ClojSharp (Clojure-like in C#) https://github.com/ajlopez/ClojSharp/commits/master. I worked on two non-public projects.

More fun is coming

Keep tuned!

Angel “Java” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

November 11, 2011

Machine Learning: Links, News and Resources (1)

Filed under: Artificial Intelligence, Lambda Calculus, Links, Machine Learning — ajlopez @ 10:01 am

http://en.wikipedia.org/wiki/Machine_learning
Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. Machine learning is concerned with the development of algorithms allowing the machine to learn via inductive inference based on observing data that represents incomplete information about statistical phenomenon and generalize it to rules and make predictions on missing attributes or future data. An important task of machine learning is classification, which is also referred to as pattern recognition, in which machines “learn” to automatically recognize complex patterns, to distinguish between exemplars based on their different patterns, and to make intelligent predictions on their class.

Machine Learning at Stanford
http://www.ml-class.org/course/auth/welcome
Enroll today in our online class for free!

Reinforcement Learning: An Introduction
http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html

Machine Learning
Systems that Improve Their Performance
http://aaai.org/AITopics/MachineLearning

Does Machine Learning Really Work?
http://www.aaai.org/ojs/index.php/aimagazine/article/view/1303/1204

My first encounter with the topic:
Samuel’s Checkers Player
http://webdocs.cs.ualberta.ca/~sutton/book/ebook/node109.html

TD Gammon
http://webdocs.cs.ualberta.ca/~sutton/book/ebook/node108.html

University of Alberta, Department of Computing Science, Machine Learning
https://www.cs.ualberta.ca/research/research-areas/machine-learning

University of Alberta, CS, Research
https://www.cs.ualberta.ca/research
See
https://www.cs.ualberta.ca/research/research-areas/bioinformatics
https://www.cs.ualberta.ca/research/research-areas/computer-games
https://www.cs.ualberta.ca/research/research-areas/artificial-intelligence
https://www.cs.ualberta.ca/research/research-areas/advanced-man-machine-interfaces

Turing award goes to ‘machine learning’ expert
http://www.physorg.com/news/2011-03-turing-award-machine-expert.html

Stanford School of Engineering – Stanford Engineering Everywhere
http://see.stanford.edu/see/courseInfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1

InfoQ: Machine Learning: A Love Story
http://www.infoq.com/presentations/Machine-Learning

Informaniac: Machine Learning for Bug Discovery
http://www.informaniac.net/2008/06/machine-learning-for-bug-discovery.html

io9. We come from the future.
http://m.io9.com/5659503/a-computer-learns-the-hard-way-by-reading-the-internet

bradford’s infer at master – GitHub
http://github.com/bradford/infer

Pragmatic Programming Techniques: Map/Reduce to recommend people connection
http://horicky.blogspot.com/2010/08/mapreduce-to-recommend-people.html

Smarter Than You Think – I.B.M.’s Supercomputer to Challenge ‘Jeopardy!’ Champions – NYTimes.com
http://www.nytimes.com/2010/06/20/magazine/20Computer-t.html?hp

Google Prediction API: Commoditization of Large-Scale Machine Learning? – A Computer Scientist in a Business School
http://behind-the-enemy-lines.blogspot.com/2010/05/google-prediction-api-commoditization.html

Apache Mahout – Overview
http://mahout.apache.org/

Papers – Hadoop Wiki
http://wiki.apache.org/hadoop/Papers

20Q – Wikipedia, the free encyclopedia
http://en.wikipedia.org/wiki/20Q

Machine Learning in Game AI – Stack Overflow
http://stackoverflow.com/questions/970060/machine-learning-in-game-ai

Applications of Machine Learning to the Game of Go
http://videolectures.net/epsrcws08_stern_aml/
David Stern, Applied Games Group, Microsoft Research Cambridge

Deep Boltzmann Machine on MNIST
http://quotenil.com/Deep-Boltzmann-Machine-on-MNIST.html

Introduction to MGL (part 1)
http://quotenil.com/Introduction-to-MGL-(part-1).html

Measuring Measures – blog – Learning about Machine Learning, 2nd Ed.
http://measuringmeasures.com/blog/2010/3/12/learning-about-machine-learning-2nd-ed.html

IET/BCS Turing Lecture 2010 | Professor Christopher Bishop
http://tv.theiet.org/technology/infopro/turing-2010.cfm

So you think machine learning is boring?
http://www.causata.com/blog/2010/02/so-you-think-machine-learning-is-boring.html

Google AI Challenge
http://csclub.uwaterloo.ca/contest/

Common Lisp and Google AI Challenge
http://aerique.blogspot.com/2010/02/google-ai-challenge-2010.html

Infer.NET: Building Software with Intelligence :: Sessions :: Microsoft PDC09
http://microsoftpdc.com/Sessions/VTL03

Infer.NET – Now with F# support @ JustinLee.sg
http://www.justinlee.sg/2009/12/09/infer-net-now-with-f-support/

Pragmatic Programming Techniques: Machine Learning: Association Rule
http://horicky.blogspot.com/2009/10/machine-learning-association-rule.html

Pragmatic Programming Techniques: Machine Learning with Linear Model
http://horicky.blogspot.com/2009/11/machine-learning-with-linear-model.html

A New Theory of Awesomeness and Miracles, by James Bridle, concerning Charles Babbage, Heath Robinson, MENACE and MAGE
http://shorttermmemoryloss.com/menace/

A small personal project to learn Clojure by implementing some simple machine learning algorithms edit
http://github.com/mreid/injuce/

Introducing Apache Mahout
http://www.ibm.com/developerworks/java/library/j-mahout/index.html

Apache Mahout – Overview
http://lucene.apache.org/mahout/

How Flightcaster squeezed predictions from flight data
http://www.datawrangling.com/how-flightcaster-squeezes-predictions-from-flight-data

Map-Reduce for Machine Learning on Multicore
http://www.cs.stanford.edu/people/ang//papers/nips06-mapreducemulticore.pdf

Torch3: The Dream Comes True
http://www.torch.ch/introduction.php

Reinforcement Learning and Artificial Intelligence: Toolkit
http://rlai.cs.ualberta.ca/RLAI/RLtoolkit/RLtoolkit1.0.html

Machine Learning Book Code
http://seat.massey.ac.nz/personal/s.r.marsland/MLBook.html

Scientific Commons: Simon Colton
http://de.scientificcommons.org/simon_colton

A Grid-based Application of machine learning to model generation
http://www.doc.ic.ac.uk/~sgc/papers/sorge_ki04.pdf

Reinforcement Learning: An Introduction
http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html

Learning Draughts/Checkers
http://www.codeproject.com/KB/game/learning_draughts.aspx

Learning Connect Four
http://www.codeproject.com/KB/game/learningconnectfour.aspx

Introduction to Machine Learning
http://robotics.stanford.edu/~nilsson/mlbook.html

The Use of Java in Machine Learning
http://www.developer.com/java/other/article.php/10936_1559871_1

Similarity Learning – IDL – EE – Washington.edu
http://idl.ee.washington.edu/similaritylearning/

Gwap
http://www.gwap.com/gwap/about/

Machine Learning
http://aima.cs.berkeley.edu/ai.html#learning

Artificial Intelligence on the Web
http://aima.cs.berkeley.edu/ai.html
This page links to 820 pages around the web with information on Artificial Intelligence.

Yu-Han Chang
http://www.yuhanchang.com/home.html
“My research centers on learning in rich multi-agent environments”

My Links
http://www.delicious.com/ajlopez/machinelearning

Angel “IAmStillLearning” Lopez
http://www.ajlopez.com
http://twitter.com/ajlopez

August 29, 2011

Lambda Calculus: Links, News and Resources (1)

Filed under: Functional Programming, Lambda Calculus, Links, Lisp, Programming — ajlopez @ 9:52 am

"Everything is a lambda in the end" ajlopez (past century)

http://en.wikipedia.org/wiki/Lambda_calculus

In mathematical logic and computer science, lambda calculus, also written as λ-calculus, is a formal system for function definition, function application and recursion. The portion of lambda calculus relevant to computation is now called the untyped lambda calculus. In both typed and untyped versions, ideas from lambda calculus have found application in the fields of logic, recursion theory (computability), and linguistics, and have played an important role in the development of the theory of programming languages (with untyped lambda calculus being the original inspiration for functional programming, in particular Lisp, and typed lambda calculi serving as the foundation for modern type systems).

Closures + Lambda = The key to OOP in Lisp « Learning Lisp
http://lispy.wordpress.com/2007/07/18/closures-lambda-the-key-to-oop-in-lisp/

Papers | Lambda the Ultimate
http://lambda-the-ultimate.org/papers

Notas sobre el Cálculo Lambda
http://ajlopez.zoomblog.com/archivo/2009/04/14/notas-sobre-el-Calculo-Lambda.html

Presenting AjLambda, lambda calculus in C#
http://ajlopez.wordpress.com/2009/02/25/presenting-ajlambda-lambda-calculus-implementation-in-c/

Funarg problem
http://en.wikipedia.org/wiki/Funarg_problem

The lambda calculus
http://faculty.knox.edu/dblaheta/research/lambda.pdf

Fixed point combinator
http://en.wikipedia.org/wiki/Y-combinator

Introducction to Lambda Calculus
http://www.cs.chalmers.se/Cs/Research/Logic/TypesSS05/Extra/geuvers.pdf

Introduction to Lambda Calculus
http://citeseer.ist.psu.edu/barendregt94introduction.html

Lambda Calculus
http://www.cs.bham.ac.uk/~axj/pub/papers/lambda-calculus.pdf

A Tutorial Introduction to the Lambda Calculus
http://www.utdallas.edu/~gupta/courses/apl/lambda.pdf

Knights of the Lambda Calculus
http://en.wikipedia.org/wiki/Knights_of_the_Lambda_Calculus

A Hacker’s Introduction to Partial Evaluation | The Lambda meme – all things Lisp, and more
http://www.ymeme.com/hackers-introduction-partial-evaluation.html

To Dissect a Mockingbird: A Graphical Notation for the Lambda Calculus with Animated Reduction
http://users.bigpond.net.au/d.keenan/Lambda/index.htm

http://www.defmacro.org/

λProlog Home Page
http://www.lix.polytechnique.fr/Labo/Dale.Miller/lProlog/

Church’s Type Theory
http://plato.stanford.edu/entries/type-theory-church/

Lambda Calculus Schemata
http://cs-www.cs.yale.edu/homes/fischer/pubs/lambda.pdf

Lambda Animator : animated reduction of the lambda calculus
http://thyer.name/lambda-animator/

Peter Selinger: Papers
http://www.mscs.dal.ca/~selinger/papers.html

Mike Taulty’s Blog : Anonymous Methods, Lambdas, Confusion
http://mtaulty.com/CommunityServer/blogs/mike_taultys_blog/archive/2009/01/28/anonymous-methods-lambdas-confusion.aspx

Lecture Notes on the Lambda Calculus (pdf)
http://www.mscs.dal.ca/~selinger/papers/lambdanotes.pdf

A Security Kernel Based on the Lambda-Calculus
http://mumble.net/~jar/pubs/secureos/

System F: Second-order lambda calculus
http://en.wikipedia.org/wiki/System_F

(Mis)using C# 4.0 Dynamic – Type-Free Lambda Calculus, Church Numerals, and more
http://community.bartdesmet.net/blogs/bart/archive/2009/08/17/mis-using-c-4-0-dynamic-type-free-lambda-calculus-church-numerals-and-more.aspx

Type-Free Lambda Calculus in C#, Pre-4.0 – Defining the Lambda Language Runtime (LLR)
http://community.bartdesmet.net/blogs/bart/archive/2009/08/30/type-free-lambda-calculus-in-c-pre-4-0-defining-the-lambda-language-runtime-llr.aspx

Jim McBeath: Practical Church Numerals in Scala
http://jim-mcbeath.blogspot.com/2008/11/practical-church-numerals-in-scala.html

My Links
http://www.delicious.com/ajlopez/lambda

Angel "AjLambda" Lopez

The Shocking Blue Green Theme. Blog at WordPress.com.

Follow

Get every new post delivered to your Inbox.

Join 66 other followers