Tag: Classification

Feed: Featured Posts - Hadoop360 Author: Andrei Macsin Guest blog post by Syed Danish Ali Review The challenges of big data can be captured succinctly as follows[1],[2]: Volume; ever increasing volume which breaks down traditional data-holding capacity Variety; more and more heterogeneous data from many formats and types are bombarding the data environment Velocity; more and more data is time sensitive now; frequent updates are taking place instead of relying on historical old data and data in real time is being generated now by the internet of things, amongst others. Veracity; how valid and reliable is the data? Since now we ... Read More

Feed: Planet big data Author: Meg Blanchette August 25th, 2016 If you’re fresh from a machine learning course, chances are most of the datasets you used were fairly easy. Among other things, when you built classifiers, the example classes were balanced, meaning there were approximately the same number of examples of each class. Instructors usually employ cleaned up datasets so as to concentrate on teaching specific algorithms or techniques without getting distracted by other issues. Usually you’re shown examples like the figure below in two dimensions, with points representing examples and different colors (or shapes) of the points representing the ... Read More

Feed: Planet big data Author: David Smith by Anusua Trivedi, Microsoft Data Scientist This is a blog series in several parts — where I describe my experiences and go deep into the reasons behind my choices. In Part 1, I discussed the pros and cons of different symbolic frameworks, and my reasons for choosing Theano (with Lasagne) as my platform of choice. Part 2 of this blog series is based on my upcoming talk at The Data Science Conference, 2016. Here in Part 2, I describe Deep Convolutional Neural Networks (DCNNs) and how Transfer learning and Fine-tuning helps better the training ... Read More

Feed: Featured Blog Posts - Data Science Central Author: Ronald van Loon First there was big data – extremely large data sets that made it possible to use data analytics to reveal patterns and trends, allowing businesses to improve customer relations and production efficiency. Then came fast data analytics – the application of big data analytics in real-time to help solve issues with customer relations, security, and other challenges before they became problems. Now, with machine learning, the concepts of big data and fast data analytics can be used in combination with artificial intelligence (AI) to avoid these problems and ... Read More

Feed: Planet big data Author: Chief Data Officer Forum (This article is addressed to C-suite executives and business leaders looking to build a CDO organisation within their company.) If you don’t know where you are going, any road will get you there. - Lewis Caroll in “Alice in Wonderland” If you are someone who has been following the evolution of strategies, new technologies, architectural innovation, best practices, success stories and a host of vendor-driven publicity in the data and analytics space, it is very easy to be influenced and assume that this is the direction you want your company to ... Read More

Feed: CIO - MapR RSS feed Author: Thor Olavsrud With an eye toward enhancing its Internet of Things (IoT) analytics platform with advanced machine learning and real-time analytics capabilities, machine data analytics specialist Glassbeam today released a new version of the platform that tightly integrates it with Apache Spark.Spark is a cluster computing framework designed to sit on top of Hadoop Distributed File System (HDFS) in place of Hadoop MapReduce. With support for in-memory cluster computing, Spark can achieve performance up to 100x faster than Hadoop MapReduce in-memory or 10x faster on disk, making it well-suited to machine learning algorithms.We're ... Read More

Feed: Featured Posts - Hadoop360 Author: Andrei Macsin Subscribe to our Newsletter Guest blog post by Vincent Granville Originally posted here, but this version here is up-to-date. We blended together the best of the best resources posted recently on DSC. It would be great to organize them by category, but for now they are organized by date. This is very useful too, since you are likely to have seen old entries already, and can focus on more recent stuff. Starred entries have interesting charts.  May 6, 2016 Linux Data Science Virtual Machine  Deep Learning for Beginners  Google BigQuery Public Datasets  How to Remove ... Read More

Feed: New Books and Journals Discussions - AnalyticBridge Author: Emmanuelle Rieuf Master machine learning techniques with R to deliver insights for complex projects About This Book Get to grips with the application of Machine Learning methods using an extensive set of R packages Understand the benefits and potential pitfalls of using machine learning methods Implement the numerous powerful features offered by R with this comprehensive guide to building an independent R-based ML system Who This Book Is For If you want to learn how to use R's machine learning capabilities to solve complex business problems, then this book is for ... Read More

Feed: New Books and Journals Discussions - AnalyticBridge Author: Emmanuelle Rieuf Books about the R programming language fall in different categories: Learning R Reference books for the professional R programmer Books about data science or visualization, using R to illustrate the concepts Books are a great way to learn a new programming language. Code samples is another great tool to start learning R, especially if you already use a different programming language. You might also want to check our DSC articles about R: they also include cheat sheets. If you are unsure about learning R, read about R versus Python. Example of chart produced with R ... Read More

Feed: New Books and Journals Discussions - AnalyticBridge Author: Emmanuelle Rieuf From Algorithms to Z-Scores:Probabilistic and Statistical Modeling in Computer Science. By Norm Matloff, University of California, Davis.  Click here to read the book (PDF document, 520 pages). I guess it will become a classic on the subject, for students learning traditional statistics. Contents 1 Time Waste Versus Empowerment 1 2 Basic Probability Models 3 2.1 ALOHA Network Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2 The Crucial Notion ... Read More