Neural Machine Translation by Philipp Koehn is an English-language textbook about the development and application of machine translation systems. It covers in depth the principles of deep learning and their role in modern translation technologies.
The book starts with an introduction to machine translation, including historical and linguistic context, and then provides a detailed explanation of the underlying deep learning methods. With practical Python code examples, readers learn how to build and optimize their own translation systems.
In addition, it discusses important aspects such as data handling, model improvements, and current challenges in the analysis and visualization of translation models. This makes it an essential reference work for researchers, developers, and students who want to delve into state-of-the-art machine translation and deep learning applications within natural language processing.
Topics such as natural language processing, machine learning, and artificial intelligence complement the content.