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Apache MXNet (Incubating) logo

Apache MXNet (Incubating)

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Apache MXNet is an open source multi-language machine learning (ML) library especially to train and deploy deep neural networks, on a wide array of devices. Once embedded in the host language, it blends declarative symbolic expression with imperative tensor computation. It is built on a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient.

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Apache Spark logo

Apache Spark

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Apache Spark(TM) is an open-source distributed general-purpose cluster computing framework with (mostly) in-memory data processing engine that can do ETL, analytics, machine learning and graph processing on large volumes of data at rest (batch processing) or in motion (streaming processing) with rich concise high-level APIs for the programming languages: Scala, Python, Java, R, and SQL. In contrast to Hadoop’s two-stage disk-based MapReduce computation engine, Spark’s multi-stage (mostly) in-memory computing engine allows for running most computations in memory, and hence most of the time provides better performance for certain applications, e.

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Apache SystemML logo

Apache SystemML

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Apache SystemML is a machine learning system with support for Java 8+, Scala 2.11+, Python 2.7/3.5+, Hadoop 2.6+, and Spark 2.1+. It provides a workplace for machine learning using big data. As It runs on top of Apache Spark, it automatically scales data, line by line, determining whether the code should be run on the driver or an Apache Spark cluster. Future releases may include additional deep learning with GPU capabilities such as importing and running neural network architectures and pre-trained models for training.

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