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4 Open Source & Cloud Machine Learning, Data Analytics & Visualization projects by Google

Google is a prolific contributor to Open source. Here is a list of 4 open source & cloud projects from Google focusing on analytics, machine learning, data cleansing & visualization.

TensorFlow

TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

OpenRefine

OpenRefine (formerly Google Refine) is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.Please note that since October 2nd, 2012, Google is not actively supporting this project, which has now been rebranded to OpenRefine. Project development, documentation and promotion is now fully supported by volunteers.

Google Charts

Google Charts provides a perfect way to visualize data on your website. From simple line charts to complex hierarchical tree maps, the chart gallery provides a large number of ready-to-use chart types.

The most common way to use Google Charts is with simple JavaScript that you embed in your web page. You load some Google Chart libraries, list the data to be charted, select options to customize your chart, and finally create a chart object with an id that you choose. Then, later in the web page, you create a <div> with that id to display the Google Chart.

Automatic Statistician

Making sense of data is one of the great challenges of the information age we live in. While it is becoming easier to collect and store all kinds of data, from personal medical data, to scientific data, to public data, and commercial data, there are relatively few people trained in the statistical and machine learning methods required to test hypotheses, make predictions, and otherwise create interpretable knowledge from this data. The Automatic Statistician project aims to build an artificial intelligence for data science, helping people make sense of their data.


This article is compiled by Jogmon.

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