This comprehensive, reader-friendly volume offers readers a high-level orientation, discussing the foundations of the field and presenting both the classical work and the most recent results. It covers an extremely rich array of topics including not only syntax and semantics but also phonology and morphology, probabilistic approaches, complexity, learnability, and the analysis of speech and handwriting.
As the first text of its kind, this innovative book will be a valuable tool and reference for those in information science (information retrieval and extraction, search engines) and in natural language technologies (speech recognition, optical character recognition, HCI). Exercises suitable for advanced readers are included as well as suggestions for further reading and an extensive bibliography.
"I'm pleased and impressed. The book is very readable, often entertaining---it tells what the issues are, what they are called, in what health they are, where more meat can be found. Given the enormous amount of material and concepts touched on, and the technical difficulties lying under the surface almost everywhere, the book betrays scholarship in a matter-of-fact way, making due impression on, but without clobbering, the reader. This is a book that invites READING THROUGH...".
Professor Tommaso Toffoli, Boston University, USA
"It is a remarkable achievement, essential reading for every linguist who aspires to be well informed about applications of mathematics in the language sciences."
Professor Geoffrey Pullum, University of Edinburgh, UK
"I really liked this book. First, it is written very well and secondly, the author has taken a rather non-standard but very attractive approach to mathematical linguistics. It is very refreshing."
Professor Aravind K. Joshi, University of Pennsylvania, USA
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details