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  2. Apache Spark - Wikipedia

    en.wikipedia.org/wiki/Apache_Spark

    The Dataframe API was released as an abstraction on top of the RDD, followed by the Dataset API. In Spark 1.x, the RDD was the primary application programming interface (API), but as of Spark 2.x use of the Dataset API is encouraged [3] even though the RDD API is not deprecated. [4] [5] The RDD technology still underlies the Dataset API. [6] [7]

  3. Hierarchical Data Format - Wikipedia

    en.wikipedia.org/wiki/Hierarchical_Data_Format

    Hierarchical Data Format (HDF) is a set of file formats (HDF4, HDF5) designed to store and organize large amounts of data.Originally developed at the U.S. National Center for Supercomputing Applications, it is supported by The HDF Group, a non-profit corporation whose mission is to ensure continued development of HDF5 technologies and the continued accessibility of data stored in HDF.

  4. Databricks - Wikipedia

    en.wikipedia.org/wiki/Databricks

    Databricks, Inc. is a global data, analytics, and artificial intelligence (AI) company, founded in 2013 by the original creators of Apache Spark. [1] [4] The company provides a cloud-based platform to help enterprises build, scale, and govern data and AI, including generative AI and other machine learning models. [5]

  5. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    Word2vec is a group of related models that are used to produce word embeddings.These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.

  6. Spearman's rank correlation coefficient - Wikipedia

    en.wikipedia.org/wiki/Spearman's_rank_correlation...

    Python has many different implementations of the spearman correlation statistic: it can be computed with the spearmanr function of the scipy.stats module, as well as with the DataFrame.corr(method='spearman') method from the pandas library, and the corr(x, y, method='spearman') function from the statistical package pingouin.

  7. Kernel density estimation - Wikipedia

    en.wikipedia.org/wiki/Kernel_density_estimation

    Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.

  8. Useful conversions and formulas for air dispersion modeling

    en.wikipedia.org/wiki/Useful_conversions_and...

    Here are the conversion factors for those various expressions of wind speed: 1 m/s = 2.237 statute mile/h = 1.944 knots 1 knot = 1.151 statute mile/h = 0.514 m/s 1 statute mile/h = 0.869 knots = 0.447 m/s. Note: 1 statute mile = 5,280 feet = 1,609 meters

  9. Ethernet frame - Wikipedia

    en.wikipedia.org/wiki/Ethernet_frame

    A data packet on the wire and the frame as its payload consist of binary data. Ethernet transmits data with the most-significant octet (byte) first; within each octet, however, the least-significant bit is transmitted first.