Azure Databricks vs. Deeplearning4j vs. Java vs. beSOURCE Comparison Chart

DL4J takes advantage of the latest distributed computing frameworks including Apache Spark and Hadoop to accelerate training. On multi-GPUs, it is equal to Caffe in performance. The libraries are completely open-source, Apache 2.0, and maintained by the developer community and Konduit team. Deeplearning4j is written in Java and is compatible with any JVM language, such as Scala, Clojure, or Kotlin. The underlying computations are written in C, C++, and Cuda. Keras will serve as the Python API. Eclipse Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Apache Spark, DL4J brings AI to business environments for use on distributed GPUs and CPUs. There are a lot of parameters to adjust when you're training a deep-learning network. We've done our best to explain them, so that Deeplearning4j can serve as a DIY tool for Java, Scala, Clojure, and Kotlin programmers.

Integrate security into SDLC via potent code analysis. Security must be an integral part of software development. Historically it hasn’t been. Static application security testing (SAST) used to be divorced from Code quality reviews, resulting in limited impact and value. beSOURCE addresses the code security quality of applications and thus integrates SecOps into DevOps. Other SAST offerings look at security as an isolated function. Beyond Security has turned this model upside-down by assuming the SecOps’ perspective in addressing security from all possible angles. Security Standards. beSOURCE adheres to all pertinent standards, guiding static code analysis engine in providing an actionable reference point.