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Euro 2016 Predictions Using Team Rating Systems

Euro 2016 Predictions Using Team Rating Systems

June 10, 2016/in Data science, Machine learning /by Jan Lasek

The 2016 UEFA European Championship is about to kick-off in a few hours in France with 24 national teams looking to claim the title. In this post, we’ll explain how to utilize various football team rating systems in order to make Euro 2016 predictions.

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https://deepsense.ai/wp-content/uploads/2019/02/euro-2016-predictions.jpg 217 750 Jan Lasek https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg Jan Lasek2016-06-10 10:03:022021-07-13 12:35:16Euro 2016 Predictions Using Team Rating Systems
Santa's stolen sleigh - Kaggle's optimization competition

Santa’s stolen sleigh – Kaggle’s optimization competition

June 1, 2016/in Data science /by Marek Cygan

In this post we present the Xpress prize-winning solution to the Kaggle’s optimization competition “Santa’s Stolen Sleigh.”

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https://deepsense.ai/wp-content/uploads/2019/02/santas-stolen-sleigh-kaggles-optimization-competition.jpg 217 750 Marek Cygan https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg Marek Cygan2016-06-01 12:20:542021-01-05 16:51:10Santa’s stolen sleigh – Kaggle’s optimization competition
Optimize Spark with DISTRIBUTE BY & CLUSTER BY

Optimize Spark with DISTRIBUTE BY & CLUSTER BY

May 18, 2016/in Big data & Spark /by Witold Jędrzejewski

Distribute by and cluster by clauses are really cool features in SparkSQL. Unfortunately, this subjectremains relatively unknown to most users – this post aims to change that.

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https://deepsense.ai/wp-content/uploads/2019/02/optimize-spark-with-distribute-by-and-cluster-by.jpg 217 750 Witold Jędrzejewski https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg Witold Jędrzejewski2016-05-18 12:29:112021-01-05 16:51:14Optimize Spark with DISTRIBUTE BY & CLUSTER BY
US Baby Names - Data Visualization

US Baby Names – Data Visualization

April 22, 2016/in Big data & Spark, Seahorse /by Rafał Hryciuk

A few days ago we released Seahorse 1.1, an enhanced version of our machine learning, Big Data manipulation and visualization product. Today, we will show you how the new version of Seahorse can be used for data mining and data visualization.

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https://deepsense.ai/wp-content/uploads/2019/02/us-baby-names-data-visualization.jpg 217 750 Rafał Hryciuk https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg Rafał Hryciuk2016-04-22 13:20:042021-01-05 16:51:20US Baby Names – Data Visualization
Improve Apache Spark aggregate performance with batching

Improve Apache Spark aggregate performance with batching

March 21, 2016/in Big data & Spark, Seahorse /by Adam Jakubowski

Seahorse provides users with reports on their data at every step in the workflow. A user can view reports after each operation to review the intermediate results. In our reports we provide users with distributions for columns in the form of a histogram for continuous data, and a pie chart for categorical data.

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https://deepsense.ai/wp-content/uploads/2019/02/improve-aggregate-performance-with-batching.jpg 217 750 Adam Jakubowski https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg Adam Jakubowski2016-03-21 18:33:502021-01-05 16:51:27Improve Apache Spark aggregate performance with batching
Should I eat this mushroom?

Should I eat this mushroom?

February 29, 2016/in Big data & Spark, Seahorse /by Grzegorz Chilkiewicz

A few days ago we have released Seahorse 1.0, a visual platform for machine learning and Big Data manipulation available for all, for free! Today, we show you how to use Seahorse to solve a simple classification problem.

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https://deepsense.ai/wp-content/uploads/2019/02/should-i-eat-this-mushroom.jpg 217 750 Grzegorz Chilkiewicz https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg Grzegorz Chilkiewicz2016-02-29 15:29:352021-01-05 16:51:31Should I eat this mushroom?
Fast and accurate categorical distribution without reshuffling in Apache Spark

Fast and accurate categorical distribution without reshuffling in Apache Spark

February 10, 2016/in Big data & Spark, Seahorse /by Adam Jakubowski

In Seahorse we want to provide our users with accurate distributions for their categorical data. Categorical data can be thought of as possible results of an observation that can take one of K possible outcomes. Some examples: Nationality, Marital Status, Gender, Type of Education.

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https://deepsense.ai/wp-content/uploads/2019/02/fast-and-accurate-categorical-distribution-without-reshuffling-in-apache-spark.jpg 217 750 Adam Jakubowski https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg Adam Jakubowski2016-02-10 16:43:002021-01-05 16:51:36Fast and accurate categorical distribution without reshuffling in Apache Spark
Cooperative data exploration

Cooperative data exploration

January 27, 2016/in Big data & Spark /by Piotr Łusakowski

Living in a world of big data comes with a certain challenge. Namely, how to extract value from this ever-growing flow of information that comes our way. There are a lot of great tools that can help us, but they all require a lot of resources. So, how do we ease the burden on this CPU/RAM demand? One way to do it is to share the data we are working on and results of our computations with others.

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https://deepsense.ai/wp-content/uploads/2019/02/cooperative-data-exploration.jpg 217 750 Piotr Łusakowski https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg Piotr Łusakowski2016-01-27 19:18:432021-01-05 16:51:43Cooperative data exploration
Exploration of data from iPhone motion coprocessor (2)

Exploration of data from iPhone motion coprocessor (2)

January 21, 2016/in Data science /by Przemyslaw Biecek

Last week we have downloaded and loaded into R data from fitness tracker (motion coprocessor in iphone). Then with just few lines of R code we decomposed the data into a seasonal weekly component and the trend. Today we are going to see how to plot the number of steps per hour for different days of week. And then same data will be used to check how often there was any activity at given time.

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https://deepsense.ai/wp-content/uploads/2019/02/Exploration-of-data-from-iPhone-motion-coprocessor-2.jpg 337 1140 Przemyslaw Biecek https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg Przemyslaw Biecek2016-01-21 22:06:002021-01-05 16:51:47Exploration of data from iPhone motion coprocessor (2)
Which whale is it, anyway? Face recognition for right whales using deep learning

Which whale is it, anyway? Face recognition for right whales using deep learning

January 16, 2016/in Data science, Deep learning, Machine learning /by Robert Bogucki

Right Whale Recognition was a computer vision competition organized by the NOAA Fisheries on the Kaggle.com data science platform. Our machine learning team at deepsense.ai has finished 1st! In this post we describe our solution.

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https://deepsense.ai/wp-content/uploads/2016/01/deep-learning-right-whale-recognition-kaggle.jpg 337 1140 Robert Bogucki https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg Robert Bogucki2016-01-16 16:16:562021-01-05 16:51:52Which whale is it, anyway? Face recognition for right whales using deep learning
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