Can Kotlin be used for Machine Learning

Can Kotlin be used for Machine Learning?

Yes, Kotlin can be used for machine learning using libraries like KotlinDL and many others. It is worth noting that Kotlin is not as popular as Python, Matlab, Julia, or R in the machine learning world.

Kotlin was designed by JetBrains for modern multiplatform applications. It was announced in 2011 and released in 2016, it has since grown in popularity and usage in many areas of programming including machine learning.

Kotlin is one of the most loved programming languages, according to a Stack Overflow Survey of 2022, Kotlin is loved by 63.29% of professional developers versus 36.71% of developers who dreaded it.

Kotlin is used by many popular companies such as Coursera, Evernote, Google, Atlassian, Spring, Corda, Gradle, Trello, and many others. But nearly none of these companies use Kotlin for Machine Learning.

Even though Kotlin is not a popular programming language for machine learning, it has many libraries that make it a good choice for machine learning. Some of the most popular Kotlin-based machine learning libraries include

  • KotlinDL
  • Krangl
  • Kotlin Dataframe
  • Londogard NLP Toolkit
  • Kotlin for Apache Spark
  • Kotlin Statistics
  • Koma
  • Let-Plot
  • Kravis
  • SimpleDNN, etc.

These Kotlin learning libraries can be used for neural networks, deep learning, data processing, clustering, classification, linear regression, data manipulation, natural language processing, data visualization, and many other tasks.

The other advantage of Kotlin is that it provides first-class interoperability with Java. This means that you can also use Java machine learning libraries in your Kotlin code. You can also call Kotlin libraries in Java code.

This provides many advantages for both Kotlin and Java developers. You can use Java machine learning libraries such as DeepLearning4J, Smile, CoreNLP, Apache Mahout, Weka, and many others.

Although Kotlin can be used for machine learning, there are many other programming languages that are preferred over Kotlin for working on machine learning projects.

ALTERNATIVES TO KOTLIN FOR MACHINE LEARNING

Here are many programming languages that you can use as an alternative to Kotlin for machine learning. Here are some of the popular ones.

PYTHON

Python has an easy-to-understand syntax that makes it easy to write code. Getting started with Python for machine learning is much easier than with Kotlin. Python is used a lot by developers for machine learning and artificial intelligence.

According to a 2022 Stack Overflow survey, Python is the third most used programming language by professional developers. It is also one of the most loved programming languages with 67.34% of developers who loved it versus 32.66% of developers who dreaded it.

Python has a lot of resources and libraries that you can use for machine learning and artificial intelligence. Here are some of the popular ones.

TENSORFLOW – An open-source library for developing and training machine learning models. Tensorflow also helps developers to build and deploy machine learning-powered applications. It is used by many companies such as PayPal, Bloomberg, eBay, Dropbox, IBM, Coca-Cola, Google, Airbnb, DeepMind, Uber, Snapchat, Qualcomm, Airbus, Intel, Twitter, and many others.

KERAS – an open-source, high-level, deep learning API developed by Google for implementing artificial neural networks. It can run on Tensorflow, Theano, Microsoft Cognitive Toolkit, and many other platforms.

NUMPY – Numpy is the most popular package for scientific computing with Python. It is used for machine learning, data science, visualization, Array libraries, image processing, signal processing, etc. Numpy also powers many other scientific and machine learning libraries.

Other Python-based machine learning libraries include SCIPY, SCIKIT-LEARN, PYTORCH, PANDAS, THEANO, MATPLOTLIB, NLTK

JULIA

Julia is a great language for machine learning, it is a high-level, high-performance dynamic language. Julia is used for Machine Learning, Scientific Computing, Parallel Computing, Data Science, Data Visualization, etc.

Julia has a lot of packages for machine learning, some of the popular ones include MLJ.jl, Flux.jl, Knet.jl, AlphaZero.jl, Turing.jl Metalhead, ObjectDetector, and TextAnalysis.jl. These packages will helpful for Deep Learning, decision trees, clustering, pre-trained models, reinforcement learning algorithms, etc.

For example, the Federal Reserve Bank of New York used Julia to make models of the United States economy (including estimating COVID-19 shocks in 2021), noting that the language made model estimation “about 10 times faster” than its previous MATLAB implementation.

Julia is a very fast and high-performance language compared to many programming languages used in machine learning. Julia is one of the few high-level programming languages in which petaFLOPS computations have been achieved, others being C, C++, and Fortran.

Julia is the 5th most loved programming language, it is used by many companies for machine learning, these companies include Aviva, NASA, Brazilian INPE, Moderna, BlackRock, Climate Modelling Alliance, Google, Microsoft, and many others.

MATLAB

Matlab is one of the most popular numerical computing platforms among engineers and scientists. It is used for Machine Learning, Deep Learning, Algorithm development, Image processing & Computer Vision, Automated Driving Systems, Data Science, and more.

In machine learning, you can use MATLAB to train models, tune parameters, deployment of deep neural networks, and deploy to production or the edge. MATLAB is a great choice for Machine Learning.

It is reported that MATLAB has more than 4 million users. Some of the companies using MATLAB include AMD, DoubleSlash, Broadcom, General Electric, Volvo, Zendesk, Confidential Records Inc, Lucid Motors, Blue Origin, Lockheed Martin, and many others.

R

R is an open-source software environment for statistical computing and graphics. R is heavily used in building machine learning models because of its flexibility. There are many R packages that you can use for machine learning.

Some of the popular ones include Classification And Regression Training (CARET), Data Explorer, Dplyr, Ggplot2, mlr3, Xgboost, Superml randomForest, e1071, and many others. Companies using R include Amazon, Google, Flipkart, Firefox, LinkedIn, ANZ, Accenture, Infosys, etc.

C++

C++ is another great and popular option for machine learning and artificial intelligence. You can use libraries such as Caffe, Microsoft Cognitive Toolkit, MLPack, Shark, Gesture Recognition Toolkit (GRT), and many others.

These libraries are helpful for deep learning, artificial neural networks, classification, regression, forecasting, linear and non-linear optimization, algorithm development, and more.

JAVA

Java has a lot of libraries for machine learning. Some of the popular libraries include Weka, Apache Mahout, Deeplearning4j, Mallet, Spark MLlib, JSAT, Encog Machine Learning Framework, JavaML, Massive Online Analysis (MOA), and many others.

These libraries are helpful for deep learning, classification, artificial neural networks, regression, forecasting, clustering, association rules, recommendation, and more.

It is worth noting that Java libraries can be used directly in Kotlin code and Kotlin libraries can be called in Java.

CONCLUSION

It can be seen that even though Kotlin can be used for machine learning, it is not a popular choice for machine learning. There are not many developers and companies who use Kotlin for machine learning.

In order to effectively work on machine learning projects, programming languages such as Python, Julia, R, MATLAB, C++, Java, or C# are preferred over Kotlin.