"Running Unit Tests on Karma" in IntelliJ IDEA
What does ScalaCheck do?
• specification of properties which should always hold
• definition of random data for testing properties
• no worries about missed test cases
• automatic generation of test cases
• checking if properties hold
• shrinking (minimization of failing test cases)
• ScalaCheck is ...
• ... an automated, property based testing tool for Scala/Java
• ... an extended port of Haskell QuickCheck
• ... available at www.scalacheck.org
"Scalate is a Scala 2.10 and 2.11 based template engine for generating text and markup which can be used in the following frameworks and environments:
stand alone in any JVM or as a Servlet Filter in any Java in a web application
with JAXRS with Jersey
in the Play Framework via play-scalate
in Apache Camel for transforming messages and templating
to generate your static or semi-static website
Scalate supports the following template formats
Mustache which is a Scala dialect of Mustache for logic-less templates which also work inside the browser using mustache.js
Scaml which is a Scala dialect of Haml and is very DRY for generating HTML / XHTML
Jade which is an even more DRY dialect of Scaml for HTML / XHTML markup generation
SSP which is like Velocity, JSP or Erb from Rails"
"Java project Minimal Template Engine is meant to fill the gap between simple string formatting with basic Java classes like String.format and complex template solutions like Velocity or StringTemplate.
It is complete but minimal in a sense that you can express everything you need in a template language including 'if' and 'foreach', but nothing else. Because of this it is small, easy to learn and clearly focused. It does not try to solve what Java can do better anyway.
It supports separation of model and view, runs without external dependencies, can be extended and configured in many ways and runs in almost all environments including Google App Engine.
"Scalasti is a Scala interface to the StringTemplate Java template library. It provides a subset of the features of StringTemplate, using a more Scala-friendly syntax."
"SymbolHound is a search engine that doesn't ignore special characters. This means you can easily search for symbols like &, %, and π. We hope SymbolHound will help programmers find information about their chosen languages and frameworks more easily."
Unlike A/B experiments that are typically done by segmenting users, our SEO experiment framework segments pages. For example, in an experiment that has 50 percent of its pages grouped in “enabled” and the other 50 percent grouped in “control,” a page will fall into one of the groups depending on its URL:
"These are an adaptation of the Ninety-Nine Prolog Problems written by Werner Hett at the Berne University of Applied Sciences in Berne, Switzerland. I (Phil Gold) have altered them to be more amenable to programming in Scala. Feedback is appreciated, particularly on anything marked TODO.
The problems have different levels of difficulty. Those marked with a single asterisk (*) are easy. If you have successfully solved the preceeding problems you should be able to solve them within a few (say 15) minutes. Problems marked with two asterisks (**) are of intermediate difficulty. If you are a skilled Scala programmer it shouldn't take you more than 30-90 minutes to solve them. Problems marked with three asterisks (***) are more difficult. You may need more time (i.e. a few hours or more) to find a good solution. The difficulties were all assigned for the Prolog problems, but the Scala versions seem to be of roughly similar difficulty.
Your goal should be to find the most elegant solution of the given problems. Efficiency is important, but clarity is even more crucial. Some of the (easy) problems can be trivially solved using built-in functions. However, in these cases, you learn more if you try to find your own solution."
Batch Normalization: Accelerating Deep Network Training by
Reducing Internal Covariate Shift
If Expedia had user logins, it would be easier to predict what kind of content and trips this user would be interested in by profiling its personal tastes. This Wikipedia link provides a technique for doing so.
"What started as a small research project resulted in the development of a machine learning model that learns our hosts’ preferences for accommodation requests based on their past behavior. For each search query that a guest enters on Airbnb’s search engine, our model computes the likelihood that relevant hosts will want to accommodate the guest’s request. Then, we surface likely matches more prominently in the search results. In our A/B testing the model showed about a 3.75% increase in booking conversion, resulting in many more matches on Airbnb. In this blog post I outline the process that brought us to this model."
As per our retro discussion, here’s a nifty diagram/mind map on user stories that includes a section on splitting stories. Some of these are good to keep in mind if you’re struggling on how to break a story down further.
"In this blog post I’ll share my first impressions of the framework and I’ll try to keep them as less subjective as possible, although my affinity to AngularJS. I’ll start with the general changes and after that keep going into details."
"Sentiment analysis is a common application of Natural Language Processing (NLP) methodologies, particularly classification, whose goal is to extract the emotional content in text. In this way, sentiment analysis can be seen as a method to quantify qualitative data with some sentiment score. While sentiment is largely subjective, sentiment quantification has enjoyed many useful implementations, such as businesses gaining understanding about consumer reactions to a product, or detecting hateful speech in online comments."
For example, in our simple model the phrase “not good” may be classified as 0 sentiment, given “not” has a score of -1 and “good” a score of +1. A human would likely classify “not good” as negative, despite the presence of “good”
"There is no duplicate content penalty. Yes, really."