Machine Learning and TypeScript: A Practical Guide

The book "Machine Learning and TypeScript" is the first comprehensive guide that combines the power of TypeScript's strict typing with modern machine learning methods. The author shows that ML is not only Python and Jupyter Notebooks; TypeScript opens up new possibilities for creating interactive, fast, and secure ML solutions that run directly in the browser or on Node.js.

What this book is about

The book covers the full cycle of working with machine learning in TypeScript:

• TypeScript basics for Data Science — data typing, generics for matrices and tensors, working with complex structures without losing performance.
• Data preprocessing — loading, cleaning, normalization, and vectorization of texts and images using TypeScript utilities.
• Model building — linear regression, decision trees, neural networks with TensorFlow.js, training, and quality evaluation.
• Integration into web applications — running models in the browser, working with web workers, creating REST APIs on Node.js with ML endpoints.
• Optimization and production — quantization, model conversion from Python, caching, monitoring, and A/B testing.

Who this book is for

• Frontend developers who want to add ML to their projects.
• Data Scientists looking to move models to the client side.