Kotlin ML Recipes

Introduction

The book "Kotlin ML Recipes" (author: Ajay Balamurugadas, 2022, 280 pages) is a collection of practical solutions for machine learning tasks implemented in Kotlin. Unlike many books focused on Python, this edition fills a niche for developers working in the JVM ecosystem and preferring Kotlin for its conciseness, safety, and compatibility with Java. Published by Apress, the book targets engineers who want to deploy ML models directly into production code without switching to other languages.

What the book is about

"Kotlin ML Recipes" is not a theoretical textbook on statistics or algorithms, but a practical guide in a "recipes" format. Each chapter contains specific tasks, step-by-step instructions, and ready-to-use code. The author covers the full data workflow: from loading and preprocessing to model training, evaluation, and deployment. Key topics include:

• Working with Kotlin ML libraries (Koma, Smile, KotlinDL, Deep Learning for JVM);
• Integration with popular Java frameworks (Weka, Deeplearning4j, TensorFlow Java);
• Classic algorithms: linear regression, decision trees, random forest, SVM;
• Neural networks and deep learning using KotlinDL;
• Text processing and NLP (tokenization, TF-IDF, Word2Vec);
• Image processing and convolutional networks;
• Model serialization and export to ONNX;
• Work