Apache Spark vs. Deeplearning4j vs. Python vs. Vue.js 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.

Builds on top of standard HTML, CSS and JavaScript with intuitive API and world-class documentation. Truly reactive, compiler-optimized rendering system that rarely requires manual optimization. A rich, incrementally adoptable ecosystem that scales between a library and a full-featured framework. Vue is a JavaScript framework for building user interfaces. It builds on top of standard HTML, CSS and JavaScript, and provides a declarative and component-based programming model that helps you efficiently develop user interfaces, be it simple or complex. Vue extends standard HTML with a template syntax that allows us to declaratively describe HTML output based on JavaScript state. Vue automatically tracks JavaScript state changes and efficiently updates the DOM when changes happen. Vue is a framework and ecosystem that covers most of the common features needed in frontend development.