Sensors and machine learning: How applications can see, hear, feel, smell, and taste

Through the power of deep and machine learning, faster CPUs, and new types of sensors, computers can now see, hear, feel, smell, taste, and speak. All these senses take the form of some kind of sensor (like a camera) and some kind of mathematical algorithm, usually a supervised machine learning algorithm and a model.

Here is what is available.

See: image and facial recognition

Recent research into image and facial recognition lets computers not only detect object presence but detect multiple instances of similar objects. Facebook and Google have really been leading the way here, with multiple open source releases. Facebook has stated that it has a goal of detecting things in video.

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5 common pitfalls of CI/CD—and how to avoid them

Devops may be one of the haziest terms in software development, but most of us agree that five activities make devops what it is: continuous integration, continuous delivery, cloud infrastructure, test automation, and configuration management. If you do these five things, you do devops. Clearly, all five are important to get right, but all too easy to get wrong. In particular, continuous integration and continuous delivery (CI/CD) may be the most difficult devops moves to master.

Continuous integration (CI) is a process in which developers and testers collaboratively validate new code. Traditionally, developers wrote code and integrated it once a month for testing. That was inefficient—a mistake in code from four weeks ago could force the developers to revise code written one week ago. To overcome that problem, CI depends on automation to integrate and test code continuously. Scrum teams using CI commit code daily at the very least, while a majority of them commit code for every change introduced.

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Skip containers and do serverless computing instead

Normally, mainstream enterprises are slow to embrace cutting-edge technologies, with startups and other early adopters setting the pace on everything from public cloud to NoSQLs. Serverless computing, however, just might be different.

Serverless, first popularized by AWS Lambda, has seen “astonishing” growth of over 300 percent year over year, according to AWS chief Andy Jassy. Ironically, that growth may be driven by the “laggards,” as Redmonk analyst James Governor calls them, rather than the techno-hipsters.

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GitHub’s tool reduces open source software license violations

GitHub has open-sourced its Licensed tool, a Ruby gem that caches and verifies the status of license dependencies in Git repos.

Licensed has helped GitHub engineers who use open source software find potential problems with license dependencies early in the development cycle. The tool reports any dependencies needing review.

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AWS Lambda tutorial: Get started with serverless computing

If laziness really is one of the virtues of a great programmer, then AWS Lambda is like a developer’s holy grail. With Lambda, you don’t have to spin up any servers, or configure or patch them, or pay for them to run hour after hour and week after week. You don’t even have to write much code.

AWS Lambda is about getting the job done with least effort—and little cost. You write simple functions and wire them to a request or an event, and Lambda executes those functions whenever that request or event occurs. You get charged only when your code actually runs.

(Insider Story)
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Git essentials: Get started with Git version control

This article introduces you to Git, including how to install the necessary software to access Git servers where your software project will be stored.

Version control concepts

To understand Git and the concept of version control, looking at version control from an historical perspective is helpful. There have been three generations of version control software.

The first generation

The first generation was very simple. Developers worked on the same physical system and “checked out” one file at a time.

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Big data analytics with Neo4j and Java, Part 1

Relational databases have dominated data management for decades, but they’ve recently lost ground to NoSQL alternatives. While NoSQL data stores aren’t right for every use case, they are generally better for big data, which is shorthand for systems that process massive volumes of data. Four types of data store are used for big data:

  • Key/value stores such as Memcached and Redis
  • Document-oriented databases such as MongoDB, CouchDB, and DynamoDB
  • Column-oriented data stores such as Cassandra and HBase
  • Graph databases such as Neo4j and OrientDB

This article introduces Neo4j, which is a graph database used for interacting with highly related data. While relational databases are good at managing relationships between data, graph databases are better at managing n-th degree relationships. As an example, take a social network, where you want to analyze patterns involving friends, friends of friends, and so on. A graph database would make it easy to answer a question like, “Given five degrees of separation, what are five movies popular with my social network that I have not yet seen?” Such questions are common for recommendation software, and graph databases are perfect for solving them. Additionally, graph databases are good at representing hierarchical data, such as access controls, product catalogs, movie databases, or even network topologies and organization charts. When you have objects with multiple relationships, you’ll quickly find that graph databases offer an elegant, object-oriented paradigm for managing those objects.

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What’s new for developers in Android P

The initial developer beta of Google’s forthcoming Android P operating system looks to entice developers with enhancements for Kotlin coding, machine learning, and application compatibility.

The use of Kotlin as a supported language in Android Studio (via a plugin) lets developers improve the performance of their code, Google says.

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How to choose the right NoSQL database

NoSQL databases provide high operational speed and increased flexibility for software developers and other users when compared to traditional tabular (or SQL) databases.

The data structures used by NoSQL databases—key-value, wide column, graph, or document—differ from those used by relational databases. As a result, NoSQL databases. NoSQL databases can be scaled across thousands of servers, though sometimes with loss of data consistency. But what makes NoSQL databases especially relevant today is that they are particularly well suited for working with large sets of distributed data, which makes them a good choice for big data and analytics projects.

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