JavaScript programmers have many good tools to choose from—almost too many to keep track of. In this article, I discuss nine text editors with good support for developing with JavaScript, HTML5, and CSS, and for documenting with Markdown. Why use an editor for JavaScript programming instead of an IDE? In a word: speed.To read this article in full or to leave a comment, please click here(Insider Story)
Since I reviewed Tableau, Qlik Sense, and Microsoft Power BI in 2015, Tableau and Microsoft have solidified their leadership in the business intelligence (BI) market: Tableau with intuitive interactive exploration, Microsoft with low price and Office integration. Qlik is still a leader compared to the other 20 vendors in the sector, but trails both Tableau and Power BI.To read this article in full or to leave a comment, please click here(Insider Story)
JavaScript is used for many different kinds of applications today. Most often, JavaScript works with HTML5 and CSS to build web front ends. But JavaScript also helps build mobile applications, and it’s finding an important place on the back end in the form of Node.js servers. Fortunately, JavaScript development tools—both editors and IDEs—are rising to meet the new challenges.Application lifecycle management (ALM) integration in Visual Studio 2017 is very good. I would happily use Visual Studio 2017 as my IDE for JavaScript if I were working primarily on Windows-based computers on projects using Microsoft technologies, especially ones that included Azure deployments and those of enterprise scale.To read this article in full or to leave a comment, please click here(Insider Story)
Once upon a time, in a galaxy far, far away, there was a company that was finally doing business on the web. Its developers were exhausted after spending years learning about HTML, CSS, JavaScript, and jQuery, and dealing with all the various browsers the company’s silly partners and customers wanted to use instead of the company’s Gold Standard, Internet Explorer 6.To read this article in full or to leave a comment, please click here(Insider Story)
Google's Go is an open source programming language that makes it easy to build simple, reliable, and efficient software. It’s part of the programming language lineage that started with Tony Hoare’s Communicating Sequential Processes, and it includes Occam, Erlang, Newsqueak, and Limbo.Use canonical import paths for repositories with aliasesTo read this article in full or to leave a comment, please click here(Insider Story)
When I reviewed self-service exploratory business intelligence (BI) products in 2015, I covered the strengths and weaknesses of Tableau 9.0, Qlik Sense 2.0, and Microsoft Power BI. As I pointed out at the time, these three products offer a range of data access, discovery, and visualization capabilities at a range of prices, with Tableau the most capable and expensive, Qlik Sense in the middle, and Power BI the least capable but a very good value.To read this article in full or to leave a comment, please click here(Insider Story)
InfoWorldI mentioned earlier that Visual Studio startup time has improved over the years. For large solutions with many projects, such as a 2-million-line-of-code C++/C# project with a dozen DLLs and other separate modules on which I used to work, the new lightweight solution load mechanism can help a lot, because the individual projects are loaded on demand instead of all at once. That can make the difference between an annoying five-minute wait at startup time and a barely noticeable five-second pause as you open a different project within the solution. Individual file loads have sped up as well and now usually open in less than a second—almost at Sublime Text speeds.To read this article in full or to leave a comment, please click here(Insider Story)
We all learned about mathematical functions when we studied algebra: y = f(x), where f(x) = ax2+…. In the abstract world of mathematics, functions are pure and reproducible and have no side effects.Fold, reduce, map, and iterateTo read this article in full or to leave a comment, please click here(Insider Story)
Google’s Go language was recently chosen as Tiobe’s programming language of 2016, based on its rapid growth in popularity over the year, more than twice that of runners-up Dart and Perl. Tiobe’s language index is based on the “number of skilled engineers worldwide, courses, and third-party vendors,” using the results of multiple search engines.To read this article in full or to leave a comment, please click here(Insider Story)
Perhaps the most positive technical theme of 2016 was the long-delayed triumph of artificial intelligence, machine learning, and in particular deep learning. In this article we'll discuss what that means and how you might make use of deep learning yourself.Perhaps you noticed in the fall of 2016 that Google Translate suddenly went from producing, on the average, word salad with a vague connection to the original language to emitting polished, coherent sentences more often than not -- at least for supported language pairs, such as English-French, English-Chinese, and English-Japanese. That dramatic improvement was the result of a nine-month concerted effort by the Google Brain and Google Translate teams to revamp Translate from using its old phrase-based statistical machine translation algorithms to working with a neural network trained with deep learning and word embeddings employing Google's TensorFlow framework.To read this article in full or to leave a comment, please click here
Over the past year I've reviewed half a dozen open source machine learning and/or deep learning frameworks: Caffe, Microsoft Cognitive Toolkit (aka CNTK 2), MXNet, Scikit-learn, Spark MLlib, and TensorFlow. If I had cast my net even wider, I might well have covered a few other popular frameworks, including Theano (a 10-year-old Python deep learning and machine learning framework), Keras (a deep learning front end for Theano and TensorFlow), and DeepLearning4j (deep learning software for Java and Scala on Hadoop and Spark). If you’re interested in working with machine learning and neural networks, you’ve never had a richer array of options. To read this article in full or to leave a comment, please click here(Insider Story)
Like superheroes, deep learning packages usually have origin stories. Yangqing Jia created the Caffe project while earning his doctorate at U.C. Berkeley. The project continues as open source under the auspices of the Berkeley Vision and Learning Center (BVLC), with community contributions. The BVLC is now part of the broader Berkeley Artificial Intelligence Research (BAIR) Lab. Similarly, the scope of Caffe has been expanded beyond vision to include nonvisual deep learning problems, although the published models for Caffe are still overwhelmingly related to images and video.To read this article in full or to leave a comment, please click here(Insider Story)
Deep learning, which is basically neural network machine learning with multiple hidden layers, is all the rage—both for problems that justify the complexity and high computational cost of deep learning, such as image recognition and natural language parsing, and for problems that might be better served by careful data preparation and simple algorithms, such as forecasting the next quarter’s sales. If you actually need deep learning, there are many packages that could serve your needs: Google TensorFlow, Microsoft Cognitive Toolkit, Caffe, Theano, Torch, and MXNet, for starters.To read this article in full or to leave a comment, please click here(Insider Story)
Machine learning is fast becoming the go-to predictive paradigm for data scientists and developers alike. Of the many tools available for tapping neural networks, Microsoft’s Azure ML Studio offers a quick learning curve that won’t take deep data or coding chops to get up and running.To read this article in full or to leave a comment, please click here(Insider Story)
Visual Studio for Mac is something that many Microsoft developers have sought for more than a decade. As Mac OS X became interesting in the early 2000s, coders who spent most of their days working in Visual Studio on Windows wondered why they couldn’t use the same languages, frameworks, and tools for the Mac, rather than needing to learn Objective-C, Cocoa, and Xcode, all of which were substantially different from the languages and tools for Windows development.To read this article in full or to leave a comment, please click here(Insider Story)
As I wrote in March of this year, the Databricks service is an excellent product for data scientists. It has a full assortment of ingestion, feature selection, model building, and evaluation functions, plus great integration with data sources and excellent scalability. The Databricks service provides a superset of Spark as a cloud service. Databricks the company was founded by the original developer of Spark, Matei Zaharia, and others from U.C. Berkeley’s AMPLab. Meanwhile, Databricks continues to be a major contributor to the Apache Spark project.To read this article in full or to leave a comment, please click here(Insider Story)
Machine learning couldn’t be hotter, with several heavy hitters offering platforms aimed at seasoned data scientists and newcomers interested in working with neural networks. Among the more popular options is TensorFlow, a machine learning library that Google open-sourced a year ago.To read this article in full or to leave a comment, please click here(Insider Story)
Like Google, Microsoft has been differentiating its products by adding machine learning features. In the case of Cortana, those features are speech recognition and language parsing. In the case of Bing, speech recognition and language parsing are joined by image recognition. Google’s underlying machine learning technology is TensorFlow. Microsoft’s is the Cognitive Toolkit. To read this article in full or to leave a comment, please click here(Insider Story)
Many web applications have been built on an open source stack that included MySQL. Despite its limitations, MySQL managed to become the world’s most widely used open source RDBMS. What limitations, you ask? Out of the box, MySQL does not scale all that well and, in particular, cannot handle a lot of simultaneous clients compared to commercial databases.To read this article in full or to leave a comment, please click here(Insider Story)
Martin Heller