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Frameworks, libraries and languages for machine learning

While some developers may find the complex algorithms and processes intimidating, these frameworks, libraries and languages for machine learning can help get you started.

It's no secret that building applications geared for artificial intelligence, machine learning and predictive analytics is a big challenge, even for the most experienced developers. Luckily, the amount of resources available to help both novice and expert programmers build sophisticated and intelligent software continues to grow. This includes major feature updates to popular application development platforms, a deluge of data-intensive algorithms created by open source developers, and an expanse of community-supported libraries.

This is particularly true when it comes to the languages and frameworks that now directly target the requirements for developing machine learning applications. Not all of them are quite the same, however, and they vary in aspects that range from data handling capabilities to their associated tool sets.

Let's take a look at the specific details surrounding 10 of these machine learning programming languages and frameworks worth considering for both small-scale projects and enterprise-level initiatives.


Python offers some competitive features when it comes to machine learning development, particularly complex analytics operations and large-scale data handling. Python is useful because of its expressive and easy-to-read syntax. This enables programmers to test, run and build relatively comprehensive machine learning systems while keeping coding errors to a minimum.

As an object-oriented language, Python treats all program elements as individual entities. Programmers can continuously reuse certain functions and classes across multiple machine learning projects simultaneously, either to test new features or deploy a completely new application. Python adds a little bit of a failsafe for not-so experienced developers, too: It is an interpreted language, meaning the compiler demands that any and all errors are resolved before it will allow the developer to run the application.

Through a comprehensive array of built-in libraries, programmers can invoke key machine learning algorithms and processes, from natural language processing (NLP) to computational graphs. Thanks to broad community support, newbie Python developers can still build machine learning systems and readily implement sophisticated algorithms.

On the other hand, Python's interpreted approach often begets slower compile times. Unfortunately, this can interfere with the efficient resource use and load balancing needed for advanced machine learning models. The limitations related to these slow compile times are certainly no secret in the Python community, and are especially jarring when compared against statically typed languages like Scala and C++. However, the ease of implementation and code clarity presents a compelling tradeoff, especially for those dipping their toes in machine learning projects for the first time.


TensorFlow 2.2, an open source project by Google, offers a highly capable framework for executing the numerical computations needed for machine learning (including deep learning). On top of that, the framework provides APIs for most major languages, including Python, C, C++, Java and Rust.

TensorFlow manipulates and connects data sets using multidimensional arrays (called tensors) and converts data flow graphs into mathematical operations (referred to as nodes). Programmers can rely on an object-oriented language like Python to treat those tensors and nodes as objects, coupling them to build the foundations for machine learning operations.

The framework takes care of the underlying details and back-end processes that bundle deep learning models and algorithms into a functional system. A key strength of TensorFlow is its ability to abstract those back-end processes and allow developers to concentrate solely on coding application logic. For some programmers, TensorFlow's power to handle these processes will open the door to design enterprise-grade systems.

TensorFlow's limitations include a steep learning curve, relatively slow startup times and tricky incompatibilities between its various versions. For instance, the framework has been criticized for confusing API structures, bloated feature additions and a lack of community support for error resolution.


In 2017, open source contributors integrated the Keras library with the TensorFlow framework. Equipped with simple, preconfigured APIs, Keras features a plug-and-play framework that programmers can use to build deep learning neural network models, even if they are unfamiliar with the specific tensor algebra and numerical techniques.

Developers can import Keras' neural network models in the form of predesigned modules. These modules can combine with additional permutations to create completely unique, customized models of your own. Keras also provides reusable code, numerous deployment options, a simplified system to export models, and plenty of documentation -- making this a viable option for machine learning programming novices.

Keras users might find its external package libraries, like scikit-learn, to be particularly useful. By using the scikit-learn algorithm library, programmers can define, compare and adopt a vast array of neural network models and machine learning algorithms. This also includes tools for preprocessing data sets and clustering unlabeled data into well-defined groups, incorporating supervised learning algorithms, performing linear regression, creating decision trees and implementing support vector machines.

On the flip side, Keras is an advanced neural network library, meaning the syntax can be challenging for some to navigate (let alone build customized models in). Users might also find it frustrating that Keras-based algorithms may return multiple, low-level errors even when performing the most basic computations. As the models it offers grow in complexity, its foreseeable that these bugs will become even harder to fix.


PyTorch was developed by Facebook as an open source platform for deep learning, highlighted by its ability to generate computational graphs that get processed at runtime. Python programmers can halt the interpreter to quickly and accurately debug their application, which is one of the biggest strengths of this highly capable framework.

Based on Torch, a scientific computing framework for creating deep learning algorithms, PyTorch is a direct competitor to TensorFlow. In particular, writing optimized code in PyTorch is somewhat easier than in TensorFlow, mainly due to its comprehensive documentation, dynamic graph computations and support for parallel processing.

PyTorch depends on attention mechanisms for neural-machine translation within complex data architectures. It also offers GPU acceleration and multithreading capabilities that can accelerate neural network training. However, some critics have said that PyTorch takes considerable time to prototype, test and create production-ready models, and that it lacks strong visualization tools.

Apache MXNet

The Apache MXNet framework offers features similar to TensorFlow and PyTorch but goes further by providing distributed training for deep learning models across multiple machines. To bridge the gap between development frameworks, GPU-centric hardware and cloud-based services, MXNet accelerates numerical computations to help create deep neural networks quickly.

MXNet supports integration with Apache TVM, an open source compiler designed for machine learning. It also features a large collection of libraries and support through GitHub and the Apache Software Foundation. However, keep in mind that the MXNet API is quite complex. And, while it offers syntax similar to PyTorch, it lacks some of the features available in that framework, such as the TensorBoard toolkit for monitoring and visualization.


Scala is a general-purpose language that blends object-oriented and functional programming styles into a concise machine learning (ML) programming language. Scala uses a static-type system, runs on the Java virtual machine (JVM) and fully interoperates with all Java libraries. Acting as a data workhorse, the language specifically benefits machine and deep learning through parallel processing and straightforward code.

As a unified compute engine, Spark exploits in-memory functions to ensure fast data processing at scale and offers ease of use. As a fast, general engine for large-scale data processing, the MLlib library ships with Spark as a standard component and integrates with Scala. Programmers can also use the Saddle library to manipulate data using array-backed data structures to visualize algorithms.

Spark is particularly capable when it comes to eliminating redundant code and debugging. Programmers can write type-safe code in an immutable manner, which helps simplify concurrency and synchronized processing, both necessary for scaling ML models. However, Scala tooling can pose a steep learning curve -- particularly for beginners, and some claim to have experienced difficulty managing dependency versions.


Programmers often turn to R for experiments with large data sets. Established in 1995 and released as an open source project in 2000, R provides sophisticated visualizations for statistical data, and is a key language for data science.

A Comprehensive R Archive Network (CRAN) offers plenty of algorithms for machine learning prototypes and for testing what-if scenarios. R also makes it simple to distribute code and documentation among development teams via functional packages. The steady adoption of R-based libraries for statistical analysis is partly due to its effectiveness in reproducing and manipulating data.

R offers effective dependency management, critical to ensuring sustainable ML models. The dplyr library offers tools for subsetting, summarizing, rearranging and joining together data sets. Another set of popular R-based tools is the mlr framework which offers classification and regression techniques similar to Python's Pandas library for data manipulation and analysis.


Java is a well-established, general-purpose programming language for ML and artificial intelligence development with a well-supported library ecosystem. Concurrency is a key component of Java and is something developers can rely on when it comes to handling the data-heavy algorithms that make up the core of machine learning efforts. Programmers can also employ Java's multithreading classes to simultaneously run sequences of machine learning operations using a variety of object types and deployment mechanisms.

The JVM supports multiple host architectures and the technology makes Java portable, easy to maintain and enables programmers to write the same ML code on multiple platforms. A number of Java ML libraries enable programmers to work with classification, clustering regression, visualization and data mining.

Java folks can also use the framework, built on top of Hadoop and Apache Spark, to work with extremely large data sets. This open source Java-based software simplifies data modeling and provides tools to work with gradient boosted machines for predictions, generalized linear models and deep learning algorithms. Beginning Java developers can access the Waikato Environment for Knowledge Analysis (Weka) and use its GUI for data mining using built-in ML algorithms. Programmers can call the Weka class library from within their Java code, applying algorithms to data and comparing outputs.

Finally, the Deep Java Library (DJL) provides a native Java development experience and offers a deep learning API that programmers can call using their framework of choice. Developers can also choose from a rich repository of pretrained models that perform specific functions regardless of the ML implementation method.

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