Practical MNIST with Kotlin

Introduction

The book «Practical MNIST with Kotlin» (author — M. Nakamura, 2020, 180 pages) is a unique practical guide to building a neural network for handwritten digit recognition in Kotlin. Unlike most educational materials that focus on Python, this book fills a gap for developers working in the JVM ecosystem who want to learn machine learning without switching to another language.

What the book is about

The book is dedicated to solving the classic MNIST problem (handwritten digit recognition) using Kotlin. The author progressively guides the reader from setting up the environment to building and training a fully connected neural network. The main focus is on practical implementation: code analysis, mathematical foundations (gradient descent, backpropagation), and result visualization. All examples are written in pure Kotlin without third-party deep learning libraries, allowing a deeper understanding of the internal mechanisms of neural networks.

Who this book is for

The book is intended for developers already familiar with the basics of Kotlin (syntax, OOP, functional programming) who want to learn machine learning. It will be useful for:

• Android developers who want to integrate ML models into mobile applications;
• Java/Kotlin programmers transitioning to the Data Science field;
• students of technical specialties studying neural networks in practice.