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  2. Apache Spark - Wikipedia

    en.wikipedia.org/wiki/Apache_Spark

    Spark Core is the foundation of the overall project. It provides distributed task dispatching, scheduling, and basic I/O functionalities, exposed through an application programming interface (for Java, Python, Scala, .NET [16] and R) centered on the RDD abstraction (the Java API is available for other JVM languages, but is also usable for some other non-JVM languages that can connect to the ...

  3. 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.

  4. Comparison of deep learning software - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_deep...

    Format name Design goal Compatible with other formats Self-contained DNN Model Pre-processing and Post-processing Run-time configuration for tuning & calibration

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  6. Java virtual machine - Wikipedia

    en.wikipedia.org/wiki/Java_virtual_machine

    Consequently, you can install a 32-bit or 64-bit JVM on a 64-bit operating system. The primary advantage of running Java in a 64-bit environment is the larger address space. This allows for a much larger Java heap size and an increased maximum number of Java Threads, which is needed for certain kinds of large applications; however there is a ...

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  8. Dask (software) - Wikipedia

    en.wikipedia.org/wiki/Dask_(software)

    Dask is an open-source Python library for parallel computing.Dask [1] scales Python code from multi-core local machines to large distributed clusters in the cloud. Dask provides a familiar user interface by mirroring the APIs of other libraries in the PyData ecosystem including: Pandas, scikit-learn and NumPy.

  9. 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.