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.
https://deepsense.ai/wp-content/uploads/2019/02/euro-2016-predictions.jpg217750Jan Lasekhttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgJan Lasek2016-06-10 10:03:022021-07-13 12:35:16Euro 2016 Predictions Using Team Rating Systems
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.
https://deepsense.ai/wp-content/uploads/2019/02/optimize-spark-with-distribute-by-and-cluster-by.jpg217750Witold Jędrzejewskihttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgWitold Jędrzejewski2016-05-18 12:29:112021-01-05 16:51:14Optimize Spark with DISTRIBUTE BY & CLUSTER BY
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.
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.
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.
https://deepsense.ai/wp-content/uploads/2019/02/should-i-eat-this-mushroom.jpg217750Grzegorz Chilkiewiczhttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgGrzegorz Chilkiewicz2016-02-29 15:29:352021-01-05 16:51:31Should I eat this mushroom?
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.
https://deepsense.ai/wp-content/uploads/2019/02/fast-and-accurate-categorical-distribution-without-reshuffling-in-apache-spark.jpg217750Adam Jakubowskihttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgAdam Jakubowski2016-02-10 16:43:002021-01-05 16:51:36Fast and accurate categorical distribution without reshuffling in Apache Spark
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.
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.
https://deepsense.ai/wp-content/uploads/2019/02/Exploration-of-data-from-iPhone-motion-coprocessor-2.jpg3371140Przemyslaw Biecekhttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgPrzemyslaw Biecek2016-01-21 22:06:002021-01-05 16:51:47Exploration of data from iPhone motion coprocessor (2)
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.
https://deepsense.ai/wp-content/uploads/2016/01/deep-learning-right-whale-recognition-kaggle.jpg3371140Robert Boguckihttps://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svgRobert 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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Euro 2016 Predictions Using Team Rating Systems
/in Data science, Machine learning /by Jan LasekThe 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.
Santa’s stolen sleigh – Kaggle’s optimization competition
/in Data science /by Marek CyganIn this post we present the Xpress prize-winning solution to the Kaggle’s optimization competition “Santa’s Stolen Sleigh.”
Optimize Spark with DISTRIBUTE BY & CLUSTER BY
/in Big data & Spark /by Witold JędrzejewskiDistribute 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.
US Baby Names – Data Visualization
/in Big data & Spark, Seahorse /by Rafał HryciukA 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.
Improve Apache Spark aggregate performance with batching
/in Big data & Spark, Seahorse /by Adam JakubowskiSeahorse 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.
Should I eat this mushroom?
/in Big data & Spark, Seahorse /by Grzegorz ChilkiewiczA 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.
Fast and accurate categorical distribution without reshuffling in Apache Spark
/in Big data & Spark, Seahorse /by Adam JakubowskiIn 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.
Cooperative data exploration
/in Big data & Spark /by Piotr ŁusakowskiLiving 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.
Exploration of data from iPhone motion coprocessor (2)
/in Data science /by Przemyslaw BiecekLast 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.
Which whale is it, anyway? Face recognition for right whales using deep learning
/in Data science, Deep learning, Machine learning /by Robert BoguckiRight 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.