The following points will help you understand why DL4J is important for deep learning:

• DL4J provides commercial support. It is the first commercial-grade, open source, deep learning library in Java.
• Writing training code is simple and precise. DL4J supports Plug and Play mode, which means switching between hardware (CPU to GPU) is just a matter of changing the Maven dependencies and no modifications are needed on the code.
• DL4J uses ND4J as its backend. ND4J is a computation library that can run twice as fast as NumPy (a computation library in Python) in large matrix operations. DL4J exhibits faster training times in GPU environments compared to other Python counterparts. 
• DL4J supports training on a cluster of machines that are running in CPU/GPU using Apache Spark. DL4J brings in automated parallelism in distributed training. This means that DL4J bypasses the need for extra libraries by setting up worker nodes and connections. 
• DL4J is a good production-oriented deep learning library. As a JVM-based library, DL4J applications can be easily integrated/deployed with existing corporate applications that are running in Java/Scala.