7 Best Java Machine Learning Libraries

Machine learning, a subset of artificial intelligence (AI), is the ability of a machine or program to imitate human behavior and perform complex tasks that mimic our ability to solve problems. Java is one of the main programming languages ​​for ML.

Here we will look at the best Java libraries available to help you build machine learning solutions.

An important aspect of machine learning is the four basic approaches, which are:

• Supervised Learning
• Unsupervised Learning
• Semi-supervised learning
• Reinforcement Learning

In addition to selecting the right approach, you will also need to know the type of data you want to predict. You can then select the type of algorithm to use.

In other words, there are a lot of “moving parts” in ML, all of which are based on selecting the right tools.

Fortunately, since Java is a widely accepted language for ML, there are many Java frameworks that can help make the task considerably easier.

But what is a library? Simply put, a library is a collection of pre-written codes that developers can use and reuse to make the development process more efficient and reliable. Almost all programming languages ​​have libraries, many of which are open source and free to use. If you want your teams to work as efficiently as possible, libraries are the best option. This way, your developers don't have to reinvent the wheel every time they start a new project.

There are many Java libraries for ML. Because it is such an important programming language, you will have no problem finding a Java development company to help build your machine learning projects.

Why Choosing the Right Java Machine Learning Libraries Is Important

Libraries make application development considerably more efficient and reliable. Instead of writing new code for every function or feature, Java developers can make use of several pre-written libraries that have already been verified and tested. There is also a lower chance of introducing errors.

Using libraries saves time and money – developers don't have to solve every problem they face.

Things to Consider When Choosing a Library

Each project, developer and company will have different needs. Here are some factors to consider:

• Type of machine learning : Will your teams use the library or framework for deep learning or a classic machine learning algorithm?
• Language type : Here we are looking at Java libraries. However, the project may also require other programming languages. Therefore, you can choose a library that can be used with other languages ​​and/or libraries.
• Scaling : Will you use this program in an internal data center or develop for the cloud? How large will the project need to be scaled?
• Data Types : You also need to know what types of data you will be working with. Are your databases SQL or NoSQL? Structured or unstructured data?
• Neural networks: Do you need a library that includes tools for creating neural networks?
• API: Do you need libraries that include APIs or that can interact with other APIs?
• Open source: Do you need to use a library released with an open source license or not?
• GPUs: If performance is a priority, you will need to select a library that can work with GPUs.

Having considered the above, what are the best libraries available? Let's take a look.

Top 7 Java ML Libraries

Since Java is so popular and works well with ML, as you may have guessed, there are many libraries available. But don't think you're limited to one library. You may have a larger project that requires several libraries.

Weka

If you're looking for a library that aims to simplify tasks like data mining, Weka is a great option. Weka stands for Waikato Environment for Knowledge Analysis and contains tools for various tasks such as data classification, penetration, regression, association rule mining and clustering.

Weka helps store, process and manage data in a continuous and sustainable way and can be used anywhere. You can transform stagnant data silos into streaming data pipelines with the simplicity of cloud native and the performance of an in-house data center cluster. If high performance in the cloud is your priority, Weka is an excellent choice.

Weka is used through the Java API, standard terminal applications, or even through a GUI. Weka use cases include the following:

• Cloud data storage
• HPC Data Management
• Data platform for machine learning and AI
• Accelerating containerized workloads

Weka is open source and free to use.

Key Features // Product Highlights

• Weka can pre-process data.
• Weka can assign classes or categories to data items.
• Weka can easily group together.
• Weka includes support for data binding.
• Weka includes several selected attributes.
• Weka can visualize data.

DeepLearning4j

DeepLearning4j was created by Eclipse and includes a collection of Java tools focused on Machine Learning. One of the highlights of DeepLearning4j is that it is one of the few frameworks that allows you to train Java models while interoperating with Python (which is one of the most popular programming languages ​​for machine learning model).

DeepLearnign4j modules include the following:

• Nd4j – a combination of TensorFlow, PyTorch and NumPy operations
• Samediff – a low-level framework for executing complex graphs
• Python4j – a framework that allows you to deploy Python scripts in a production environment
• Libnd4j – a C++ library for executing mathematical code
• Datavec – a library used for data transformation to convert data into tensors which can then be used to run neural networks
• Apache Spark integration – makes it possible to run deep learning pipelines on Apache Spark

DeepLearning4j use cases include model import and retraining and deployment to JVM, mobile, IoT, and Apache Spark microservices environments. This library is one of the best tools for integrating models built in Python.

Key Features // Product Highlights

• Important for Python AI/ML
• Java, Scala and Python APIs.
• Parallel training through iterative reduction
• Scalable with Hadoop
• Distributed support for CPU and GPU

Apache Mahout

Apache Mahout is an open source project used to develop ML algorithms and provides Java and Scala. This library mainly focuses on common mathematical operations (specifically, linear algebra) and primitive Java collections. Apache Mahout is designed to implement machine learning algorithms very quickly.

Apache Mahout works alongside Apache Hadoop so your teams can apply ML to distributed computing. The core algorithms included in Apache Mahout revolve around data clustering, mining, and classification.

Key Features // Product Highlights

• Backend agnostic: Apache Mahout abstracts the domain-specific language from the engine where the code is processed. This means that users can implement any mechanism needed.
• GPU/CPU Accelerators: Apache Mahout improves Java Virtual Machine speed by using “native solvers” that move the core to offload to off-heap memory or GPU for faster computation.
• Recommenders: Apache Mahout includes implementations of alternative least squares, co-occurrence, and correlated co-occurrence to extend co-occurrence so that it can be used across multiple data dimensions .

ADAMANS

ADAMÃS stands for Advanced Data Mining And Machine Learning System and is a deep learning library specifically for Java. This library is used to help facilitate the creation of reactive, data-driven workflows and offers a considerable range of operations and actors.

ADAMS is a great choice for data mining, retrieval processing, and data visualization. Released under GPLv3, ADAMS makes it easy to integrate ML into business processes and strictly follows the philosophy, less is more . Because of this, ADAMS is easy and efficient to use.

ADAMS uses a tree-like structure, in combination with control actors, to define how data flows without the need for any explicit connections.

Key Features // Product Highlights

While ADAMS may not be the most flexible library you've ever used, it has several important features, such as the following:

• It includes four types of actors: autonomous (no input, no output), source (output only), transformer (input and output), and sink (input only).
• Uses control actors that determine data flow or flow execution
• Actors can connect implicitly in a tree structure rather than being placed on a screen

JavaML

JavaML is a collection of ML and data mining algorithms that includes common interfaces for each. This library is extensible and offers an API for both research scientists and software developers.

Key Features // Product Highlights

• Includes many machine learning algorithms
• Provides common interfaces for each supported algorithm
• Although there is no GUI, developers will find clearly defined and easy-to-use interfaces
• Implementations for algorithms are clearly described in the scientific literature

JSAT

JSAT is a Java library that makes solving machine learning problems easier. All JSAT code is independent, without any external dependencies. JSAT is pure Java and is a solid solution for small to medium sized problems. Thanks to support for parallel execution, JSAT is relatively fast.

JSAT is currently being refactored to work with Java 8. Because JSAT is developed by one person, the process is a bit slower than it would be with a team. As we are just migrating to Java 8, there may be some solvable issues.

Key Features // Product Highlights

• JSAT has one of the largest collections of algorithms of any framework.
• JSAT is faster than comparable libraries.
• JSAT is free and open source.

Apache OpenNLP

Apache OpenNLP is an open source Java library aimed specifically at natural language processing. This library consists of components that include a phrase detector, tokenizer, name finder, document categorizer, part-of-speech tagger, chunker, and parser.

With Apache OpenNLP, developers can build complete NLP pipelines for all common NLP tasks such as sentence segmentation, part-of-speech tagging, named entity recognition, tokenization, natural language detection, chunking, parsing, and coreference resolution .

Key Features // Product Highlights

• Named Entity Recognition (NER) – Apache OpenNLP supports NER, which makes it possible to extract names of places, people, and things.
• Summarize – The summary feature allows you to summarize paragraphs, articles, documents and even collections.

Conclusion

Java is still one of the most used programming languages. And given the widespread use of artificial intelligence and machine learning developments, you can bet that these technologies will continue to go hand in hand in the future. With the right Java machine learning libraries, the sky is the limit for what your in-house or outsourced development teams can do. And as long as they are following Java best practices, the programs they develop can do wonders for your company.

If you liked this, be sure to check out one of our other Java articles:

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