Azure Databricks vs. Deeplearning4j vs. Java vs. Racket 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.

Racket is a general-purpose, multi-paradigm programming language that serves as a modern dialect of Lisp and a descendant of Scheme. It is designed as a platform for programming language design and implementation, enabling developers to create new domain-specific and general-purpose languages. Racket's core language includes features such as macros, modules, lexical closures, tail calls, delimited continuations, parameters (fluid variables), software contracts, green threads, and OS threads. The language also comes with primitives, such as event spaces and custodians, which control resource management and enable the language to act like an operating system for loading and managing other programs. Further extensions to the language are created with the powerful macro system, which, together with the module system and custom parsers, can control all aspects of a language. Most language constructs in Racket are implemented as macros in the base language.