May 2018
Beginner to intermediate
384 pages
10h 19m
English
We have coded the examples in this chapter with deeplearning4j library. The core framework of DL4J is designed to work seamlessly with Hadoop (HDFS and MapReduce) as well as Spark-based processing. It is easy to integrate DL4J with Spark. DL4J with Spark leverages data parallelism by sharding large datasets into manageable chunks and training the deep neural networks on each individual node in parallel. Once the models produce parameter values (weights and biases), those are iteratively averaged for producing the final outcome.
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