Neural Machine Translation van Philipp Koehn is een Engelstalig gedrukt boek. Deze titel is geschikt voor lezers die zich inhoudelijk willen verdiepen in dit onderwerp.
Deep learning is revolutionizing how machine translation systems are built today. This book introduces the challenge of machine translation and evaluation - including historical, linguistic, and applied context -- then develops the core deep learning methods used for natural language applications. Code examples in Python give readers a hands-on blueprint for understanding and implementing their own machine translation systems. The book also provides extensive coverage of machine learning tricks, issues involved in handling various forms of data, model enhancements, and current challenges and methods for analysis and visualization. Summaries of the current research in the field make this a state-of-the-art textbook for undergraduate and graduate classes, as well as an essential reference for researchers and developers interested in other applications of neural methods in the broader field of human language processing.
Deep learning is revolutionizing how machine translation systems are built today. This introduction to machine translation starts from the basics of neural network methods and reaches the state of the art, while giving illuminating historical, linguistic, and applied context. Code examples in Python give a hands-on blueprint for implementation.
Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.
Inhoudelijk sluit dit boek aan bij onderwerpen als Natural language and machine translation, Machine learning, COMPUTERS / Artificial Intelligence / Natural Language Processing.