Welcome to Apache Pig!

Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets. At the present time, Pig's infrastructure layer consists of a compiler that produces sequences of Map-Reduce programs, for which large-scale parallel implementations already exist (e.g., the Hadoop subproject). Pig's language layer currently consists of a textual language called Pig Latin, which has the following key properties: Ease of programming. It is trivial to achieve parallel execution of simple, "embarrassingly parallel" data analysis tasks.

http://pig.apache.org/

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HBase Groovy - Home Semantic Bits What Machine Learning Can Do And What Machine Learning Cannot Do Posted by Stacy on 24-08-2018 How Do Machines Learn An Introduction to Machine Learning Posted by Stacy on 14-08-2018 Sqoop The Jython Project Deep learning based Object Detection and Instance Segmentation using Mask R-CNN in OpenCV (Python / C++) A few weeks back we wrote a post on Object detection using YOLOv3. The output of an object detector is an array of bounding boxes around objects detected in the image or video frame, but we do not get any clue about the shape of the object inside the bounding box. Wouldn’t it be cool if we could find a binary mask containing the object instead of just the bounding box? In this post, we will learn how to do just that. We will show how to use a Convolutional Neural Network (CNN) model called Mask-RCNN (Region based Convolutional Neural Network) for object detection and segmentation.

A platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs by sergeykucherov Jul 15