To view the original press release, please click here. “With R2016b, we’ve lowered the bar to allow domain experts to work with more data, more easily. This leads to improved system design, performance, and reliability.” “Companies are awash in data, but struggle to take advantage of it to build better predictive models and gain deeper insights,” says David Rich, MATLAB marketing director, MathWorks. R2016b also includes a timetable data container for indexing and synchronizing time-stamped tabular data string arrays to help manipulate, compare, and store text data efficiently and new functions for preprocessing data. Today we are reviewing about MathWorks MATLAB R2016a. MATLAB has been around for quite some time now and since then it has come in many different versions. First, there is the exploration phase where you often have no strong idea about what you are doing. MathWorks MATLAB R2016a has got awesome engineering and mathematical tools and it has got an awesome user interface. Engineers and scientists can use tall arrays with hundreds of math, statistics, and machine learning algorithms. Code can run on Hadoop clusters or be integrated directly into Spark applications. MathWorks releases Best Practices for MATLAB Toolbox development Posted by Mike Croucher, J486 views (last 30 days) 7 Likes 3 comments Research computing can be a messy affair and, for me at least, it often looks like the following. Tall arrays now provide a way to work naturally with out-of-memory data using familiar MATLAB functions and syntax, removing the need to learn big data programming. Greg’s pick this week is Expand wildcards for files and directory names by Peter van den Biggelaar. R2016b also includes additional features in Simulink a new product, Risk Management Toolbox and updates and bug fixes to 83 other products. Engineers and scientists can now more easily work with data too big to fit in memory. ![]() MathWorks today introduced Release 2016b (R2016b) with new capabilities that simplify working with big data in MATLAB.
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