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

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

More Code Generation with AjGenesis

I created

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


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

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

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:

Next steps: add named functions.


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


I added minor functionality to ClojSharp (Clojure-like in C#) I worked on two non-public projects.

More fun is coming

Keep tuned!

Angel “Java” Lopez

November 11, 2011

Machine Learning: Links, News and Resources (1)

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

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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
Enroll today in our online class for free!

Reinforcement Learning: An Introduction

Machine Learning
Systems that Improve Their Performance

Does Machine Learning Really Work?

My first encounter with the topic:
Samuel’s Checkers Player

TD Gammon

University of Alberta, Department of Computing Science, Machine Learning

University of Alberta, CS, Research

Turing award goes to ‘machine learning’ expert

Stanford School of Engineering – Stanford Engineering Everywhere

InfoQ: Machine Learning: A Love Story

Informaniac: Machine Learning for Bug Discovery

io9. We come from the future.

bradford’s infer at master – GitHub

Pragmatic Programming Techniques: Map/Reduce to recommend people connection

Smarter Than You Think – I.B.M.’s Supercomputer to Challenge ‘Jeopardy!’ Champions –

Google Prediction API: Commoditization of Large-Scale Machine Learning? – A Computer Scientist in a Business School

Apache Mahout – Overview

Papers – Hadoop Wiki

20Q – Wikipedia, the free encyclopedia

Machine Learning in Game AI – Stack Overflow

Applications of Machine Learning to the Game of Go
David Stern, Applied Games Group, Microsoft Research Cambridge

Deep Boltzmann Machine on MNIST

Introduction to MGL (part 1)

Measuring Measures – blog – Learning about Machine Learning, 2nd Ed.

IET/BCS Turing Lecture 2010 | Professor Christopher Bishop

So you think machine learning is boring?

Google AI Challenge

Common Lisp and Google AI Challenge

Infer.NET: Building Software with Intelligence :: Sessions :: Microsoft PDC09

Infer.NET – Now with F# support @

Pragmatic Programming Techniques: Machine Learning: Association Rule

Pragmatic Programming Techniques: Machine Learning with Linear Model

A New Theory of Awesomeness and Miracles, by James Bridle, concerning Charles Babbage, Heath Robinson, MENACE and MAGE

A small personal project to learn Clojure by implementing some simple machine learning algorithms edit

Introducing Apache Mahout

Apache Mahout – Overview

How Flightcaster squeezed predictions from flight data

Map-Reduce for Machine Learning on Multicore

Torch3: The Dream Comes True

Reinforcement Learning and Artificial Intelligence: Toolkit

Machine Learning Book Code

Scientific Commons: Simon Colton

A Grid-based Application of machine learning to model generation

Reinforcement Learning: An Introduction

Learning Draughts/Checkers

Learning Connect Four

Introduction to Machine Learning

The Use of Java in Machine Learning

Similarity Learning – IDL – EE –


Machine Learning

Artificial Intelligence on the Web
This page links to 820 pages around the web with information on Artificial Intelligence.

Yu-Han Chang
“My research centers on learning in rich multi-agent environments”

My Links

Angel “IAmStillLearning” Lopez

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)

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

Papers | Lambda the Ultimate

Notas sobre el Cálculo Lambda

Presenting AjLambda, lambda calculus in C#

Funarg problem

The lambda calculus

Fixed point combinator

Introducction to Lambda Calculus

Introduction to Lambda Calculus

Lambda Calculus

A Tutorial Introduction to the Lambda Calculus

Knights of the Lambda Calculus

A Hacker’s Introduction to Partial Evaluation | The Lambda meme – all things Lisp, and more

To Dissect a Mockingbird: A Graphical Notation for the Lambda Calculus with Animated Reduction

λProlog Home Page

Church’s Type Theory

Lambda Calculus Schemata

Lambda Animator : animated reduction of the lambda calculus

Peter Selinger: Papers

Mike Taulty’s Blog : Anonymous Methods, Lambdas, Confusion

Lecture Notes on the Lambda Calculus (pdf)

A Security Kernel Based on the Lambda-Calculus

System F: Second-order lambda calculus

(Mis)using C# 4.0 Dynamic – Type-Free Lambda Calculus, Church Numerals, and more

Type-Free Lambda Calculus in C#, Pre-4.0 – Defining the Lambda Language Runtime (LLR)

Jim McBeath: Practical Church Numerals in Scala

My Links

Angel "AjLambda" Lopez

The Shocking Blue Green Theme. Blog at


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