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The New Methodology

The New Methodology
In the past few years there's been a blossoming of a new style of software methodology - referred to as agile methods. Alternatively characterized as an antidote to bureaucracy or a license to hack they've stirred up interest all over the software landscape. In this essay I explore the reasons for agile methods, focusing not so much on their weight but on their adaptive nature and their people-first orientation. Probably the most noticeable change to software process thinking in the last few years has been the appearance of the word 'agile'. We talk of agile software methods, of how to introduce agility into a development team, or of how to resist the impending storm of agilists determined to change well-established practices. This new movement grew out of the efforts of various people who dealt with software process in the 1990s, found them wanting, and looked for a new approach to software process. This essay was originally part of this movement. From Nothing, to Monumental, to Agile .

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Building a React Universal Blog App: A Step-by-Step Guide When we think of single page applications (SPAs) we think browsers, JavaScript, speed and, in my case, invisibility to search engines. This is because a SPA renders a page’s content using JavaScript and since web crawlers do not use a browser to view web pages, they cannot view and index the content. Or, to better say, most of them can’t.

Tracking Movers & Shakers: Tackling Human Resources News Classification A detailed review of CB Insights' HR news classification algorithms using supervised machine learning. Post by Bugra Akyildiz (@bugraa) — Machine Learning Engineer at CB Insights One of the critical inputs in our efforts to assess private company health is the tracking of their human resources activities. » Release Management for Enterprises Adopting DevOps Within Your Enterprise Stuck Between a Rock and a Hard Place In a mature enterprise, the development and maintenance of software systems and products rely on processes that have grown over the years. These process can be fairly detailed, manual and rigid, but the fact is they work! DevOps preaches collaboration and continuous improvement to better meet business goals.

Complementary Tools · facebook/react Wiki React is a small library that does one thing well. Here's a list of tools we've found that work really well with React when building applications. If you want your project on this list, or think one of these projects should be removed, feel free to edit this page. Debugging Atellier: The smartest way to share interactive components with your team.React Developer Tools: an extension available for Chrome and Firefox that allows you to inspect the React component hierarchy in the Chrome Developer Tools.Pretty Diff: a beautifier that supports JSX and can auto-detect it apart from JavaScript. It provides some minimal level of scope analysis by generating a colorful HTML result to identify variables against the scope where they are declared.

An Introduction to Gradient Descent and Linear Regression Gradient descent is one of those “greatest hits” algorithms that can offer a new perspective for solving problems. Unfortunately, it’s rarely taught in undergraduate computer science programs. In this post I’ll give an introduction to the gradient descent algorithm, and walk through an example that demonstrates how gradient descent can be used to solve machine learning problems such as linear regression.

The Politics of Cryptography: Bitcoin and The Ordering Machines Quinn DuPont It was April 10th, 2013 and the price of a single Bitcoin surged past 250 USD on the Mt. Gox exchange.i A few months prior I had purchased seven Bitcoins for just under $200, and now they were nearing $2000 in value (Figure 1).ii But, just as fast as the market went up, it came down. Ever the amateur gambler, I panicked and sold too early. Machine Learning at Quora - Engineering at Quora - Quora Adapted from my original answer to How does Quora use machine learning in 2015? At Quora we have been using machine learning approaches for some time. We are constantly coming up with new approaches and making big improvements to the existing ones. It is important to note that all these improvements are first optimized and tested offline by using many different kinds of offline metrics but are always finally tested online through A/B tests. In the following paragraphs, I will describe some of the most important applications and techniques of ML at Quora as of 2015. Ranking

Metcalfe's Law is Wrong The increasing value of a network as its size increases certainly lies somewhere between linear and exponential growth [see diagram, " "]. The value of a broadcast network is believed to grow linearly; it's a relationship called Sarnoff's Law, named for the pioneering RCA television executive and entrepreneur David Sarnoff. At the other extreme, exponential--that is, 2n--growth, has been called Reed's Law, in honor of computer networking and software pioneer David P. Reed. Machine Learning for Developers by Mike de Waard Most developers these days have heard of machine learning, but when trying to find an 'easy' way into this technique, most people find themselves getting scared off by the abstractness of the concept of Machine Learning and terms as regression, unsupervised learning, Probability Density Function and many other definitions. If one switches to books there are books such as An Introduction to Statistical Learning with Applications in R and Machine Learning for Hackers who use programming language R for their examples. However R is not really a programming language in which one writes programs for everyday use such as is done with for example Java, C#, Scala etc. This is why in this blog machine learning will be introduced using Smile, a machine learning library that can be used both in Java and Scala. These are languages that most developers have seen at least once during their study or career. As final note I'd like to thank the following people:

Test Driven Development (TDD): Best Practices Using Java Examples In the previous article Test Driven Development (TDD): Example Walkthrough an example of TDD was given. It went from writing first test and its implementation to having a set of requirements fully tested and developed. Now it’s time to learn what the best TDD practices are. This article will be built on examples from the previous one. Best practices are solutions to a set of problems under certain situations.

Statistical Learning About This Course This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical). No Projects - Beyond Projects The Problem with Projects and some ideas on what to do about it Applying the project lifecycle model to software development complicates both and makes developing good software harder. Using the language, terminology and mindset of projects to discuss something that is inherently not a project creates conflict, confusion, short-termism and sets inconsistent goals.

Why is Object-Oriented Programming Useful? (With a Role Playing Game Example) This blog post is those still new to programming and have probably heard about "object-oriented programming", "OOP", "classes", "inheritance/encapsulation/polymorphism", and other computer science terms but still don't get what exactly OOP is used for. In this post I'll explain why OOP is used and how it makes coding easier. This post uses Python 3 code, but the concepts apply to any programming language. There are two key non-OOP concepts to understand right off the bat: