Crystal vs. Deeplearning4j vs. Java vs. iGenius Crystal 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.

Any teams can access key insights independently by just talking — no training or data literacy skills required. Crystal can be tailored to a specific organization’s needs, meaning your teams and crystal can combine to drive actionable insights. Crystal monitors your data 24/7 and can alert you on important changes, ensuring you will get the answers you need, as well as the ones you didn’t know you needed. Available on mobile and desktop, your teams can get immediate insights anywhere without sifting through reports. Bring your use case to life in days, not months, with crystal’s user-friendly setup. Complete with low-code no-code data source connection and setup, you can make more of your existing BI investments and get immediate business value.