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Lift Analysis – A Data Scientist’s Secret Weapon
Gain insight into using lift analysis as a metric for doing data science. Understand how to use it for evaluating the performance and quality of a machine learning model.
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Must Know Tips for Deep Learning Neural Networks
Deep learning is white hot research topic. Add some solid deep learning neural network tips and tricks from a PhD researcher.
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The Data Science Game – Student Competition
The Data Science Game returns this year, with university students competing for dominance. Details for this iteration and further information is provided here.
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Introducing GraphFrames, a Graph Processing Library for Apache Spark
An overview of Spark's new GraphFrames, a graph processing library based on DataFrames, built in a collaboration between Databricks, UC Berkeley's AMPLab, and MIT.
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scikit-feature: Open-Source Feature Selection Repository in Python
scikit-feature is an open-source feature selection repository in python, with around 40 popular algorithms in feature selection research. It is developed by Data Mining and Machine Learning Lab at Arizona State University.
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Building Zoomable Line Charts in jQuery
Learn how to build zoomable line charts using FusionCharts’ core JS library and its jQuery charts plugin, and get started making some beautiful data visualizations for the web.
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Tree Kernels: Quantifying Similarity Among Tree-Structured Data
An in-depth, informative overview of tree kernels, both theoretical and practical. Includes a use case and some code after the discussion.
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How Small is the World, Really?
Social network analysis is back in the news again, with a recent Facebook project which determined that there are an average of 3.5 intermediaries between any 2 Facebook users. But this is different than "6 degrees of separation." Read on to find out why, and how.
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Getting Started with Data Visualization
Data visualization is on the rise nowadays. This step-by-step tutorial covers the process of creating your first data visualization using FusionCharts.
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Opening Up Deep Learning For Everyone
Opening deep learning up to everyone is a noble goal. But is it achievable? Should non-programmers and even non-technical people be able to implement deep neural models?
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