Tag: Classification

Feed: AWS Big Data Blog. Jonathan Fritz is a Senior Product Manager for Amazon EMR Many Amazon EMR customers use Zeppelin to create interactive notebooks to run workloads with Spark using Scala, Python, and SQL. These customers have found Amazon EMR to be a great platform for running Zeppelin because of strong integration with other AWS services and the ability to quickly create a fully configured Spark environment. Many customers have already discovered Amazon S3 to be a useful way to durably store and move their notebook files between EMR clusters.  With the latest Zeppelin release (0.5.6) included ... Read More

Feed: Google Cloud Big Data and Machine Learning Blog Author: Google Cloud Big Data and Machine Learning Blog Team Innovation in data processing and machine learning technology Wednesday, August 17, 2016 Posted by Sara Robinson, Developer Advocate You may know the Cloud Vision API for its face, object, and landmark detection, but you might not know that the Vision API can also detect inappropriate content in images using the same machine learning models that power Google SafeSearch. Since we announced the Google Cloud Vision API GA in April, we’ve seen over 100 million requests for SafeSearch detection. Any application with ... Read More

Feed: Google Cloud Big Data and Machine Learning Blog Author: Google Cloud Big Data and Machine Learning Blog Team Innovation in data processing and machine learning technology Tuesday, July 26, 2016 Posted by Kaz Sato, Staff Developer Advocate You may have heard the buzz about neural networks and deep learning, and want to learn more. But when you learn about the technology from a textbook, many people find themselves overwhelmed by mathematical models and formulas. I certainly was. For people like me, there's an awesome tool to help you grasp the idea of neural networks without any hard math: TensorFlow ... Read More

Feed: Google Cloud Big Data and Machine Learning Blog Author: Google Cloud Big Data and Machine Learning Blog Team Innovation in data processing and machine learning technology Monday, May 23, 2016 Posted by Lak Lakshmanan, Big Data & Machine Learning Professional Services, Google Cloud Platform Demand forecasting is something that every business does. If you're a restaurant owner, you need to forecast how many diners you'll have tomorrow and what foods they'll order so that you know what ingredients to shop for and how many cooks to have in your kitchen. If you sell shirts, you need to predict in ... Read More

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