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ND4J and ND4S are scientific computing libraries for the JVM. They are meant to be used in production environments, which means routines are designed to run fast with minimum RAM requirements
Deep Learning Software
• Versatile n-dimensional array object • Multiplatform functionality including GPUs • Linear algebra and signal processing functions • Supports GPUs via CUDA • Integrates with Hadoop and Spark • ND4S’s API mimics the semantics of Numpy
Small (<50 employees), Medium (50 to 1000 Enterprise (>1001 employees)
ND4J brings the intuitive scientific computing tools of the Python community to the JVM in an open source, distributed and GPU-enabled library. In structure, it is similar to SLF4J. ND4J gives engineers in production environments an easy way to port their algorithms and interface with other libraries in the Java and Scala ecosystems.
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ND4J is a scientific computing libraries for the JVM. It is meant to be used in production environments, which means routines are designed to run fast with minimum RAM requirements. ND4J is used by national laboratories for tasks such as climatic modeling, which require computationally intensive simulations. ND4J brings the intuitive scientific computing tools of the Python community to the JVM in an open source, distributed and GPU-enabled library. In structure, it is similar to SLF4J. ND4J gives engineers in production environments an easy way to port their algorithms and interface with other libraries in the Java and Scala ecosystems. ND4J is also an open-source, distributed deep-learning project in Java spearheaded by the people at Skymind, a San Francisco-based business intelligence and enterprise software firm. ND4J is distributed under an Apache 2.0 License.A static import at the top of users’ Java file makes advanced functions fairly simple to use with ND4J. ND4J is actively developed. Users can clone the repository, compile it, and reference it in a project. There are two ways to perform any operation in ND4J, destructive and nondestructive; i.e. operations that change the underlying data, or operations that simply work with a copy of the data. Destructive operations will have an “i” at the end – addi, subi, muli, divi. The “i” means the operation is performed “in place,” directly on the data rather than a copy, while nd.add() leaves the original untouched. There are also three possible argument types with ND4J ops: inputs, optional arguments and outputs. The outputs are specified in the ops’ constructor. The inputs are specified in the parentheses following the method name, always in the first position, and the optional arguments are used to transform the inputs; e.g. the scalar to add; the coefficient to multiply by, always in the second position.
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