Angel \”Java\” Lopez on Blog

January 28, 2015

ECMAScript 6: Links, News and Resources (1)

Filed under: ECMAScript, JavaScript, Links — ajlopez @ 2:44 pm

Announcing TypeScript 1.4 – TypeScript – Site Home – MSDN Blogs

io.js – ES6 on io.js

CoffeeScripters, Have You Tried ES6 Yet? | Chris Schmitz

6to5 JavaScript Transpiler Gains Momentum

Read Understanding ECMAScript 6 | Leanpub


JavaScript Application Architecture On The Road To 2015 — Medium

ECMAScript 6 promises (1/2): foundations

Fat Arrow Functions in JavaScript

ECMAScript 6 modules: the final syntax

Collecting and Iterating, the ES6 Way – HTML5Rocks Updates

The Better Parts

Getting Concurrent With ES6 Generators


Can you explain to me something about ES6? : javascript

Axel Rauschmayer on ECMAScript 6 and the Future of JavaScript

ES6 Rocks


Porting from CommonJS



Handling required parameters in ECMAScript 6

Creating defensive objects with ES6 proxies | NCZOnline

Callable entities in ECMAScript 6

My Links

January 24, 2015

Machine Learning: Links, News And Resources (6)

Filed under: Artificial Intelligence, Links, Machine Learning — ajlopez @ 8:14 pm

Previous Post
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Nuit Blanche: Europe Wide Machine Learning Meetup and Paris Machine Learning #12: Season 1 Finale, Andrew Ng and More…



Work on Machine Learning Problems That Matter To You | Machine Learning Mastery

What Does a Neural Network Actually Do? « Some Thoughts on a Mysterious Universe

Weka 3 – Data Mining with Open Source Machine Learning Software in Java

A Tour of Machine Learning Algorithms | Machine Learning Mastery

Speakers and Abstracts | Machine Learning Summer School

A Primer on Deep Learning | DataRobot

The People Who Would Teach Machines to Learn | The Official Blog of

Juan M Gómez’s Blog

IPAM – Schedule

Meet the Man Google Hired to Make AI a Reality | Enterprise | WIRED

Neural networks and a dive into Julia

Researchers Teach A Robot To Catch Flying Objects Like Yogi Berra | TechCrunch

Machine Learning is Fun! — Medium

DataMining & MachineLearning

Irving Wladawsky-Berger: Why Do We Need Data Science when We’ve Had Statistics for Centuries?

fergalbyrne/clortex · GitHub


My Links

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Angel “Java” Lopez

January 23, 2015

Liqueed Project (1)

Filed under: Express, JavaScript, Liqueed, MongoDB, NodeJs, Open Source Projects — ajlopez @ 6:36 pm

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Today, I want to present the Liqueed Project, see repo at:

It is a web application based on an idea by @acyment, read post:

The basic idea of the application is to help teams that are developing something in the way that Cyment suggested, on the issue of allocation of shares on the project. For several months, the application (code, backlog, ideas, implementation, hosting and others) has been putting together by a “liquid” team (with entry and exit of people)

In this series of posts starting today I want to discuss interesting technical issues raised by the project. For today, I commented that:

– It is a Node.js application, exposed to the web using Express. Bringing the programming language is JavaScript.

– In addition to some internal administration pages with MVC, has an API exposed, exchanging JSON.

‘- There’s an app Single Page which is what would have to use the end user to view projects, voting, distributions of shares and to enter new ratings

– Most of the code was written using the workflow of TDD (Test-Driven Development). Even the first code implemented the model in memory, allowing easier progress on the implementation of use cases, without bothering about persistence (even the SPA client can run without having a walk server)

– A few months ago, we added persistence with MongoDB. We could use another database, relational perhaps. We are not taking advantage of the facilities to handle documents MongoDB yet. Only chose it for its ubiquity in development platforms and different Node hosting services.

– Some weeks ago, we added Istambul for code coverage.

– When TDD tests for pure code began to be long, we created a textual DSL (Domain Specific Language) that allows us to write text files for more complicated functional tests.

– Begin to add tests of SPA (Single Page Application) using Zombie

And there are more details and topics to comment, in the upcoming posts.

Stay tuned!

Angel “Java” Lopez

January 21, 2015

Internet Of Things: Links, News And Resources (5)

Filed under: Internet of Things, Links — ajlopez @ 6:33 pm

Previous Post

The Internet of Things is the Future of Retail – Salesforce Blog

Construyendo un contador de personas con Raspberry Pi y Ubidots

Internet of Things application development platform

The Big Data of Wearables

Working with wearables and Bluemix

Octoblu | We make all APIs, Platforms and Devices talk to each other. Easily.

Technical Machine

Grove Labs | Home

Connect Raspberry Pi to PubNub in 2 Steps – PubNub

The Internet Of Things Is Reaching Escape Velocity | TechCrunch

La Ciudad de Buenos Aires lanza IoT, un concurso sobre Internet de las Cosas – Gira BsAs

My Links

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Angel “Java” Lopez

January 19, 2015

Machine Learning: Links, News And Resources (5)

Filed under: Artificial Intelligence, Links, Machine Learning — ajlopez @ 12:53 pm

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Machine Learning in Go using GoLearn | Stephen Whitworth

mate-tools – Tools for Natural Language Analysis, Generation and Machine Learning – Google Project Hosting



A Gentle Introduction to Scikit-Learn: A Python Machine Learning Library | Machine Learning Mastery

Facebook Chases Google’s Deep Learning with New Research Group | MIT Technology Review

AI Developers to power new generation of context driven artificial intelligence | SiliconANGLE

Deep Learning (or not): The why’s have it — FactorialWise

Neural networks and deep learning – Exploring Machine Learning with Scikit-learn

Gaussian Processes for Machine Learning: Contents

Machine Learning – complete course notes

Learning and Teaching Machine Learning: A Personal Journey

Description – Galaxy Zoo – The Galaxy Challenge | Kaggle

My solution for the Galaxy Zoo challenge – Sander Dieleman

Introduction to Information Retrieval


My Links

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Angel “Java” Lopez

January 16, 2015

Machine Learning: Links, News And Resources (4)

Filed under: Artificial Intelligence, Links, Machine Learning — ajlopez @ 11:10 am

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An Introduction to Deep Learning (in Java): From Perceptrons to Deep Networks | Toptal

Machine Learning & Recommender Systems at Netflix Scale

The Yaksis

2014 will be the year you’ll learn Machine Learning — Louis Dorard

Classification with scikit-learn | DataRobot

Machine Learning with Scikit-Learn – Jake Vanderplas on Vimeo

How Google’s Robots Can Learn Like Humans | Fast Company | Business Innovation

Machine Learning in Javascript: Introduction | Burak Kanber’s Blog

Probably Approximately Correct — a Formal Theory of Learning | Math n Programming

Machine Learning

Seattle area event: A hands-on introduction to machine learning with F# – Visual F# Tools Team Blog – Site Home – MSDN Blogs

Introduction to Machine Learning

All Models of Learning have Flaws « Machine Learning (Theory)


The Man Who Would Teach Machines to Think – James Somers – The Atlantic

Machine Learning in Python has never been easier – AnalyticBridge

Code Webs – Visualizing 40,000 student code submissions


Stanford researchers to open-source model they say has nailed sentiment analysis — Tech News and Analysis

My Links

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Angel “Java” Lopez

January 13, 2015

Machine Learning: Links, News And Resources (3)

Filed under: Artificial Intelligence, Links, Machine Learning — ajlopez @ 12:19 pm

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Tutorial Slides by Andrew Moore, computer scientist at Google, ex-CMU professor – AnalyticBridge

Introduction to Machine Learning | InTechOpen

Skills Matter : Progressive F# Tutorials 2013: Matt Moloney

Deep Learning

Richard Socher – Deep Learning Tutorial

tresata/ganitha Imbalanced Learning: Foundations, Algorithms, and Applications (9781118074626): Haibo He, Yunqian Ma: Books

Peekaboo: Machine Learning Cheat Sheet (for scikit-learn)

Análisis Cluster (II): Clasificación no supervisada mediante clasificación jerárquica aglomerativa | Pybonacci

Building Machine Learning Systems with Python | Meta Rabbit

Machine Learning | The F# Software Foundation

Neural Network Visualisation | Creative Clojure

nuroko/nurokit · GitHub

Skills Matter : The London Clojure Community:Machine Learnin

yods/storm-ml-play · GitHub

Manning: Machine Learning in Action

scalanlp/nak · GitHub

Cornell Chronicle: Student’s research could shake up Wall Street

A Tutorial on Learning With Bayesian Networks – Microsoft Research

Everything You Wanted to Know About Machine Learning, But Were Too Afraid To Ask (Part One) | The Official Blog of

Resources « CS 194-16: Introduction to Data Science

harthur/brain · GitHub

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Angel “Java” Lopez

January 12, 2015

Machine Learning: Links, News And Resources (2)

Filed under: Artificial Intelligence, Links, Machine Learning — ajlopez @ 9:27 am

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CS 229 Machine Learning Course Materials

Will it Python? Machine Learning for Hackers, Chapter 2, Part 2: Logistic regression with statsmodels

Foundations Of Machine Learning

How I made $500k with machine learning and HFT (high frequency trading)

jimpil / enclog
Clojure wrapper for Encog (v3) (Machine-Learning framework that specialises in neural-nets)

Machine Learning: Genetic Algorithms in Javascript Part 2

5 Principles for Applying Machine Learning Techniques

Understanding the Bias-Variance Tradeoff

Up And Running With Python – My First Kaggle Entry

Machine Learning for Hackers

8 Crazy Things IBM Scientists Have Learned Studying Twitter

What have been the most interesting papers in computer science for 2011?

Infer.NET is a .NET library for machine learning. It provides state-of-the-art algorithms for probabilistic inference from data. Various Bayesian models such as Bayes Point Machine classifiers, TrueSkill matchmaking, hidden Markov models, and Bayesian networks can be implemented using Infer.NET. Infer.NET is currently downloadable as a beta release under a non-commercial license.

Machine Learning

Hadoop and Machine Learning

Machine Learning, Hadoop, and Mahout

Bayesian Reasoning and Machine Learning

Enbracing Uncertainty

The New Machine Intelligence

Machine learning for dummies

Smart Data Structures: An Online Machine Learning
Approach to Multicore Data Structures

Best Paper Awards in Computer Science (since 1996)

Review of 2011 free Stanford online classes

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Angel “Java” Lopez

January 9, 2015

JavaScript And Artificial Intelligence (3)

Filed under: Artificial Intelligence, JavaScript — ajlopez @ 9:43 am

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One topic that I presented with an example (but without running it) is the evaluation of the next move in a board game. I mentioned a movie I watched decades ago and influenced me a lot in the way of seeing artificial intelligence, War Games (“War Games” 1983, see Wikipedia see IMDB):

In board games, it is common to evaluate a position, given the possible moves, evaluate the resulting positions, choosing the best one for our side. The problem is to determine the "best". In general, there is no perfect evaluation functions. What you can do is explore a tree of moves, to have a "deeper" assessment of the current position. This allows calculating a current position value based on the possible evolution of the next moves. There are several ways to explore a tree (I refer you to book mentioned in the previous post, based on all the talk)

I chose as an example, a board game with random, where the moves involve the roll of dices, the popular backgammon. I remember the work of Hans Berliner, in the eighties, that I met thanks to an article in Scientific American:


It was the first program to beat a human world champion in a board game. You can read the articles of Hans Berliner in:

Backgammon Computer Program Beats World Champion
Computer Backgammon

If you read the description of the program, it has a lot of heuristics, and expert knowledge of the game. My project is more modest and tries to evaluate a position exploring the tree of moves, calculating at the end a simple "metric" of how far we are the ultimate goal (remove all the tiles from the board before your opponent ).

The project in JavaScript at:

It was written using TDD (Test-Driven Development), as usual, using Node.js console. But the example can run in the browser:

Simple interface to be improved:

Runs evaluating two moves forward to the client. Before click "Play" you can choose who moves, and what are the initial dices values. But now, the algorithm takes seconds to run, something to improves. Several options to explore, such as parallel processing (we saw at the conference that JavaScript has implementations to run in multiprocessors), and derive the evaluation to the server, where it can be forwarded to many worker machines (something I did for another example , genetic algorithms).

Other projects that I am working to evaluate positions (but still without examples, work in progress):

The game of Go is compelling, but not trivial. First, I want to go further with the implementation of backgammon, the first candidate to distributed evaluation.

Stay tuned!

Angel “Java” Lopez

January 7, 2015

JavaScript And Artificial Intelligence (2)

Filed under: Artificial Intelligence, JavaScript — ajlopez @ 9:42 am

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Thanks to JSConf Argentina 2014 for giving me the opportunity to give this talk, in this excellent conference. To give a dynamic format, the talks were twenty minutes. I would like in this series of posts complete the presentation I wanted to show, commenting in more detail some points, and presenting other demos and projects that were left out of the conversation.

Today’s topic is recommending THE book that helped me as study guide for an endless theme, a classic computer science (the fourth most cited in this century):

Artificial Intelligence, a Modern Approach

by Stuart Russell and Peter Norvig. About the lastest edition cover:

There is an Spanish edition, and I found it at bookstores in Buenos Aires, a few years ago. It has more than a thousand pages, twenty-seven chapters and appendices, from intelligent agents to neural networks, from search algorithms and evaluation of trees to robotics and philosophical themes. I find it interesting that each chapter is accompanied by notes on the history of the topic, which always think that gives us perspective and better understanding of the problems and difficulties in development. Not to keep only “what we know now”, but also to study what was the path followed to reach the current state, which may also serve to understand what the future we can continue.

Code, pseudocode at  Lisp, Python, Java code at

A big list to research: AI on the web

In the edition I have, JavaScript does not appear as a programming language to use. It is a good exercise to adapt the examples in pseudocode implementations to different languages. I try to implement some examples in the project Notably, JavaScript must be the simplest language to implement many of the ideas in the book. Implementations typed and not-dynamic languages such as Java and C #, seem to always have some “convoluted” solutions compared to a direct implementation in JavaScript. See for example

Stay tuned!

Angel “Java” Lopez

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