# FAQ ## [Why Deeplearning4j?](/en-1.0.0-beta6/faq.md#a1) With a versatile n-dimensional array class for Java and Scala, DL4J is Scalable on Hadoop, utlizes GPU support for scaling on AWS, includes a general vectorization tool for machine-learning libs, and most of all relies on ND4J: A matrix library much faster than Numpy and largely written in C++. We also built RL4J: Reinforcement Learning for Java with Deep Q learning and A3C. ## [What's the use case for AI and machine learning?](/en-1.0.0-beta6/faq.md#a2) AI tools like Deeplearning4j can be applied to robotic process automation (RPA), Fraud detection, network intrusion detection, Recommender Systems (CRM, adtech, churn prevention), Regression and predictive analytics, Face/image recognition, Voice search, Speech-to-text (transcription), and preventative hardware monitoring (anomaly detection). ## [How can I contribute?](/en-1.0.0-beta6/faq.md#a3) Developers who would like to contribute to Deeplearning4j can get started by reading our [Contribtor's Guide](https://app.gitbook.com/contribute). ## [Is DL4J parallelized and multi-threaded?](/en-1.0.0-beta6/faq.md#a4) Deeplearning4j includes both a distributed, multi-threaded deep-learning framework and a normal single-threaded deep-learning framework. Training takes place in the cluster, which means it can process massive amounts of data quickly. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala, Clojure and Kotlin. Deeplearning4j's role as a modular component in an open stack makes it the first deep-learning framework adapted for a micro-service architecture. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://deeplearning4j.konduit.ai/en-1.0.0-beta6/faq.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.