Deeplearning4j vs. MLlib vs. PureScript vs. Unlambda 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.

PureScript is a strongly typed, purely functional programming language that compiles JavaScript. It enables developers to build robust web applications, web servers, and mobile apps using functional programming techniques. PureScript offers features such as algebraic data types, pattern matching, row polymorphism, extensible records, higher-kinded types, type classes with functional dependencies, and higher-rank polymorphism. The language emphasizes strong static typing and pure functions, ensuring code reliability and maintainability. Developers can compile PureScript code into readable JavaScript, facilitating seamless integration with existing JavaScript codebases. The ecosystem includes an extensive collection of libraries, excellent tooling, and editor support with instant rebuilds. An active community provides numerous learning resources, including the PureScript book, which offers practical projects for beginners.