Tag: Anaconda

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. Monday newsletter published by Data Science Central. Previous editions can be found here.  The contribution flagged with a + is our selection for the picture of the week. Announcements Vertica in-database machine learning supports the entire predictive analytics process with end-to-end machine learning management, massively parallel processing and simple SQL execution, allowing data scientists and analysts to embrace the power of Big Data with no limits and no compromises. Find out how you can accelerate time to value on predictive analytics projects with Vertica. Learn more. Don’t miss out! The next class ... Read More

Feed: Planet MySQL; Author: Lukas Eder; The SQL language has one great advantage over procedural, object oriented, and “ordinary” functional programming languages. The fact that it is truly declarative (i.e. a 4GL / fourth generation programming language) means that a sophisticated optimiser can easily transform one SQL expression into another, equivalent SQL expression, which might be faster to execute. How does this work? Expression Tree Equivalence Here’s an introduction to equivalent expression trees from my SQL Masterclass: Let’s assume we want to evaluate the following expression: A + (A - B) Now in maths, it can be proven trivially that ... Read More

Feed: All - O'Reilly Media. Author: Mac Slocum. NY Skyline - JupyterCon 2017 (source: O'Reilly) People from across the Jupyter community are coming together in New York for JupyterCon. Below you'll find links to highlights from the event. Project Jupyter: From interactive Python to open science Fernando Perez explains how Project Jupyter fits into a vision of collaborative development of tools that are applicable to research, education, and industry. Jupyter and Anaconda: Shaking up the enterprise Peter Wang talks about the co-evolution of Jupyter and Anaconda and looks at what’s needed to sustain an open and innovative future. How the ... Read More

Feed: CDH – Cloudera Engineering Blog. Author: Cy Jervis. Cloudera Data Science Workbench provides freedom for data scientists. It gives them the flexibility to work with their favorite libraries using isolated environments with a container for each project. In JVM world such as Java or Scala, using your favorite packages on a Spark cluster is easy. Each application manages preferred packages using fat JARs, and it brings independent environments with the Spark cluster. Many data scientists prefer Python to Scala for data science, but it is not straightforward to use a Python library on a PySpark cluster without modification. To ... Read More

Feed: All - O'Reilly Media. Author: Manu Jeevan, Suresh Chelladurai. Road signs (source: Pixabay) The full Jupyter Notebook for this post can be accessed here. Although there are many deep learning frameworks, including TensorFlow, Keras, Torch, and Caffe, Apache MXNet in particular is gaining popularity due to its scalability across multiple GPUs. In this blog post, we'll tackle a computer vision problem: classifying German traffic signs using a convolutional neural network. The network takes a color photo containing a traffic sign image as input, and tries to identify the type of sign. The full notebook can be found here In ... Read More

Feed: Microsoft Azure Blog. Author: Alice Jin. To help you leverage your telemetry data and better monitor the behavior of your Azure applications, we are happy to provide a Jupyter Notebook template that extends the power of Application Insights. Instead of making ad hoc queries in the Application Insights portal when an issue arises, you can now write a Jupyter Notebook that routinely queries for telemetry data, performs advanced analytics, and sends the derived data back to Application Insights for monitoring and alerting. You can execute the Jupyter Notebook using Azure WebJob either on a schedule or via webhook. Through ... Read More

Feed: CDH – Cloudera Engineering Blog. Author: Justin Kestelyn. Enabling Python development on CDH clusters (for PySpark, for example) is now much easier thanks to new integration with Continuum Analytics’ Python platform (Anaconda). Python has become an increasingly popular tool for data analysis, including data processing, feature engineering, machine learning, and visualization. Data scientists and data engineers enjoy Python’s rich numerical and analytical libraries—such as NumPy, pandas, and scikit-learn—and have long wanted to apply them to large datasets stored in Apache Hadoop clusters. While Apache Spark, through PySpark, has made data in Hadoop clusters more accessible to Python users, actually ... Read More

Feed: AWS Big Data Blog. In the world of data science, users must often sacrifice cluster set-up time to allow for complex usability scenarios. Amazon EMR allows data scientists to spin up complex cluster configurations easily, and to be up and running with complex queries in a matter of minutes.Data scientists often use scheduling applications such as Oozie to run jobs overnight. However, Oozie can be difficult to configure when you are trying to use popular Python packages (such as “pandas,” “numpy,” and “statsmodels”), which are not included by default. One such popular platform that contains these types of packages ... Read More

Feed: Planet big data. Author: Edd Wilder-James. Options for developing with deep learning  |  July 18th, 2017 Modern artificial intelligence makes many benefits available to business, bringing cognitive abilities to machines at scale. As a field of computer science, AI is moving at an unprecedented rate: the time you must wait for a research result in an academic paper to translate into production-ready code can now be measured in mere months. However, with this velocity comes a corresponding level of confusion for newcomers to the field. As well as developing familiarity with AI techniques, practitioners must choose their technology platforms ... Read More

Feed: Planet MySQL. Author: Valeriy Kravchuk. Previous post in series was published almost 4 months ago, but I do not plan to end it. So, let me quickly discuss some of problems I worked on or was interested in so far in June, and provide some useful links.Back on June 2 I had to find out what exact files are created by MariaDB's ColumnStore when I create a table in this storage engine. Actually in recent versions one can check the tables in the INFORMATION_SCHEMA, but if still wonders why are all these directories with numbers in the names (/usr/local/mariadb/columnstore/data1/000.dir/000.dir/011.dir/193.dir/000.dir/FILE000.cdf), ... Read More