Summary
Data science uses math extensively to analyze problems. There are numerous Java math libraries available, many of which support concurrent operations. In this chapter, we introduced a number of libraries and techniques to provide some insight into how they can be used to support and improve the performance of applications.
We started with a discussion of how simple matrix multiplication is performed. A basic Java implementation was presented. In later sections, we duplicated the implementation using other APIs and technologies.
Many higher level APIs, such as DL4J, support a number of useful data analysis techniques. Beneath these APIs often lies concurrent support for multiple CPUs and GPUs. Sometimes this support is configurable, as is the case for DL4J. We briefly discussed how we can configure ND4J to support multiple processors.
The map-reduce algorithm has found extensive use in the data science community. We took advantage of the parallel processing power of this framework to...
