Machine Learning in Kotlin: Libraries and Tools

Machine Learning in Kotlin: Modern Libraries and Tools

Kotlin is a modern, concise, and safe language that is increasingly being used not only for Android development but also in the field of Data Science and machine learning. Thanks to full compatibility with Java and a growing ecosystem, Kotlin offers developers powerful tools for building ML models. In this article, we will review the key libraries for machine learning in Kotlin, their features, and usage examples.

1. KotlinDL — Deep Learning Library from JetBrains

KotlinDL (Kotlin Deep Learning) is the official library from the creators of Kotlin, designed for building and training neural networks. It provides a high-level API that is intuitive for Kotlin developers and supports TensorFlow as a backend.

Key Features:

Creating models using a DSL (Domain Specific Language) in Kotlin.
Support for convolutional (CNN), recurrent (RNN), and fully connected networks.
Loading pre-trained models (ResNet, VGG16, BERT).
Working with images, text, and tabular data.

Example of creating a simple neural network for classification:

import org.jetbrains.kotlinx.dl.api.core.Sequentialimport org.jetbrains.kotlinx.dl.api.core.layer.*import org.jetbrains.kotlinx.dl.api.core.optimizer.Adam

val model = Sequential.of(    Dense(128, activation = Activations.Relu),    Dropout(0.5f),    Dense(10, activation = Activations.Softmax))

model.compile(    optimizer = Adam(),    loss = Losses.CATEGORICAL_CROSSENTROPY,    metric = Metrics.ACCURACY)

KotlinDL is ideal for computer vision and natural language processing tasks, especially if you are already working within the Kotlin ecosystem.

2. KMath — Mathematical Library for Scientific Computing

KMath is a modular library for numerical and symbolic computations, developed by JetBrains. It provides tools for linear algebra, optimization, statistics, and working with large data arrays.

Key Components:

Multidimensional arrays (NDArray) with support for various backends (JVM, Native, JS).
Linear algebra: matrix operations, decompositions, eigenvalues.
Statistical distributions and tests.
Optimization: gradient descent, genetic algorithms.

Example of performing linear regression:

import space.kscience.kmath.operations.*import space.kscience.kmath.structures.*import space.kscience.kmath.stat.*

// Data generationval x = DoubleArray(100) { it.toDouble() }val y = DoubleArray(100) { 2.0 * it + 1.0 + Random.nextGaussian() }

// Calculating coefficients using the least squares methodval meanX = x.average()val meanY = y.average()val slope = x.zip(y) { xi, yi -> (xi - meanX) * (yi - meanY) }.sum() /            x.sumOf { (it - meanX) * (it - meanX) }val intercept = meanY - slope * meanX

println("y = ${slope}x + $intercept")

KMath is a foundation for implementing your own ML algorithms without needing to include heavy frameworks.

3. Smile — Statistical Learning and Data Analysis Library

Smile (Statistical Machine Intelligence and Learning Engine) is a powerful framework in Java/Kotlin that includes over 200 machine learning algorithms. It is well-suited for classical ML methods: decision trees, SVM, clustering, dimensionality reduction.

Smile Features: