Machine learning is an evolving field that is getting more complex every day. When I spoke with developer friends back in 2009, they mentioned that they routinely built machine learning toolkits from scratch. Things have changed considerably over the last few years. Due to the increasing complexity of machine learning algorithms, it is no longer prudent to write code from the ground up. Their best option is to leverage existing libraries to code machine learning algorithms more efficiently.
Java has proven to be a surprisingly robust tool for machine learning, at least with web based applications. There are a number ofJava libraries that are invaluable for developers working on machine learning algorithms. Here is an overview for machine learning developers that need to take advantage of them.
Top Java Libraries for Machine Learning
If you are new to the field of machine learning, there are a number of Java libraries that will simplify development. Here are a few of the top ones that we have come across.
Eclipse Deeplearning4j
Eclipse Deeplearning4j is the most widely used Java library for handling machine learning requests. It is used for numerous applications, including:
- Analyzing and processing multilayer perceptrons
- Handling image and text classification with convolutional neural networks
- Dealing with time series data with recurrent neural networks
- Processing a wide array for autoencoders
The vast spectrum of applications that can be handled with Eclipse Deeplearning4j makes it one of the most versatile machine learning libraries available to Java developers.
DL4J
DL4J is one of the best libraries for developing multi-layered neural networks. Here are some reasons this tool is a lifesaver for machine learning developers that code in Java:
- It has been proven to be a remarkably efficient tool for developing deep learning solutions.
- DL4J can be used for creating highly scalable interfaces.
- This tool is great for customizability and rapid prototyping.
- It is compatible with other JVM languages, including Scala, Clojure and Kotlin.
- DL4J is compatible with Spark and Hadoop.
DL4J has proven to be one of the most robust tools for deep learning. If you are proficient in any JVM language, then this can be a great tool for use.
Weka
This Java toolkit was developed from the University of Waikoto in New Zealand and named after a small species of birds that is native to this region. However, Weka is a remarkably library that is used by deep learning programmers all over the world.
Weka can be invoked in several ways. You can either pull Weka commands from a database they have been installed or embed them directly in your own Java code.
Despite its adaptability, there are some limitations with Weka. The Weka Explorer GUI is not suitable for training models from large datasets. You will need to apply more ingenuity to work with such datasets, but it is doable if you know how to go about it. The best approach is to indirectly deliver commands to your Weka toolkit via your computer operating system’s command-line interface (CLI). You will need to learn the nuances of your operating system to know how to do this, but you will be surprised by the scalability and impressive functionality available through Weka once you do.
Advanced Data mining And Machine learning System (ADAMS)
ADAMS is a very effective tool for developing rapid workflows that can be processed in real-time. It isn’t known for its ability to work with the largest datasets, but it is sufficient for the majority of deep learning applications that you may need to oversee.
The biggest advantage of ADAMs is its ability to work with highly complex projects. It is available under the GPLv3 license.
Java Has a Growing Set of Libraries for Handling Machine Learning
Java is one of the languages best suited for machine learning. There are a number of libraries that offer a tremendous array of features for machine learning applications. Once you understand the nuances of these tools, you can develop machine learning algorithms much more rapidly.