Java has been one of the most dominating programming languages in the tech industry for more than a decade. It has a massive ecosystem of libraries, and frameworks, and a large community of exceptional developers. However, with the rapid change in the industry with the introduction of new technologies like deep learning, there are very limited options offered in Java for making such applications.
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Presently, most deep learning models are written and trained in Python which has become an additional barrier for Java developers who want to explore this technology, as they will now have to learn a completely new programming language as well as the complex area of deep learning.
In this article, we will be exploring what deep learning is and some new Java libraries and frameworks that offer Java deep learning. These tools will be a great resource for you if you are currently working with Deep learning or if you are new and wants to start learning from scratch.
Machine learning is a technique to allows the computer to learn the specifications of the given task from the provided data. Learning the features of a task allows computers to even perform very complex tasks like identifying an object in an image.
Deep learning is a branch of machine learning. It is a set of very powerful techniques that are used to train not a computer but large networks (artificial neural networks) to perform complex tasks. Deep learning techniques have proven to be effective in performing some very complex tasks like object detection, action recognition, and many others.
Following are some of the remarkable deep learning Java tools that can be used to use various deep learning techniques natively on Java. This list includes libraries and frameworks that are either built for java or were previously available in Python but now you can use them in Java as well.
Deep Learning for Java also known as Deeplearning4j, or DL4J, is the most beloved library of Java developers. It is the first commercial-grade, open-source distributed deep learning library that is completely written in Java. It has also been a very significant contribution to the Java ecosystem.
The aim behind the creation of DL4J was to bring deep neural networks and deep reinforcement learning together for business environments and application development in Java rather than just for research purposes. It provides Java API for neural network creation and also supports various neural network structures like feedforward neural networks, RBM, convolutional neural nets, deep belief networks, autoencoders, and more.
You can consider DL4J as a DIY tool for Java developers, who wish to apply deep learning algorithms with Hadoop. It is capable of writing applications for pattern recognition and goal-oriented machine learning by recognizing patterns in speech, sound, and text formats. DL4J is also compatible with other JVM languages such as Scala, Clojure, and Kotlin.
AWS has also contributed to deep learning by building Deep Java Library (DJL). It is an open-source deep learning framework written in Java to build deep learning applications. The good thing about this framework is that it can support any deep learning engine, like Apache MXNet, PyTorch, or TensorFlow to run and train models natively in Java.
DJL also offers a powerful ModelZoo design that allows Java developers to manage already trained models and load them into a single line of code. The built-in ModelZoo now supports up to 70 pre-trained and ready-to-use models from GluonCV, HuggingFace, TorchHub, and Keras. If you are a Java developer and are interested in exploring deep learning, Deep Java Library (DJL) is the best option to start with or if you have been working with deep learning and want to shift to Java, a great option is available.
Neuroph is another great tool for deep learning in Java. It is an object-oriented artificial neural network framework written in Java that facilitates Java developers to create and train the neural networks in Java. Neuroph provides a Java class library as well as a great GUI tool called easyNeurons for creating and training neural networks.
It is a lightweight framework that can be a more suitable option to develop common neural network architectures. It also contains all the well-designed, open-source Java libraries. Neuroph simplifies the development of neural networks especially for new Java developers by the easyNeurons GUI. It is one of the highlights of this framework because even those with limited experience can easily create, train, and save neural networks quite conveniently.
If you are new to neural networks, deep learning, and just want to try how they work without going into complicated details and the implementation process, then Neuroph is a good choice for you. It is small, very well documented, easy to use, and a very flexible neural network framework that ticks all the boxes for a newcomer.
RapidMiner is a platform that supports numerous machine learning and deep learning algorithms through its GUI and several Java APIs. It has built quite a big community by now and many tutorials and guiding material is available online along with extensive documentation.
This commercial data science platform is currently powering Cisco, GE, Hitachi, SalesForce, Samsung, Siemens, and many other prominent tech companies. It provides a set of features and tools to simplify the tasks performed by data scientists and engineers, to build new data mining processes, to set up predictive analysis, to train models, and more. This helps in constructing understandable and straightforward neural networks and makes deep learning workflows very easy.
Apache MXNet is another deep learning framework. It is an open-source platform backed by Apache foundation as well as Carnegie Mellon and Amazon. Developers can easily train, and deploy deep neural networks using it. Some notable features include the option for fast model training, its scalability, the best part is the support for multiple programming languages including Java making it a considerable option for Java developers.
Apache MXNet is designed for both efficiencies as well as flexibility. It allows developers to mix symbolic and imperative programming to maximize the efficiency and productivity of their applications by getting the results in a shorter time.
This article lists some popular and also the best deep learning Java libraries and frameworks, most of which are also open-source allowing Java developers to easily try them out at no cost. The choice of a framework mainly depends upon the algorithms it supports and the implementation of neural networks. Processing speed, size of the dataset, and ease of use are other factors that can contribute to your decision-making process.
See Also: Top 10 Java Machine Learning Tools And Libraries
It is recommended to try out each of these tools to find the one that suits you. With various advancements and development with almost every tool, any of these Java deep learning tools will be able to fulfill your requirements.
Shaharyar Lalani is a developer with a strong interest in business analysis, project management, and UX design. He writes and teaches extensively on themes current in the world of web and app development, especially in Java technology.
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