Neural network methods in the processing of a natural language
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Available introduction to NLP and machine learning for those who are just starting to study one or another discipline
neural networks of direct distribution
work with natural language data
Relations between words in context
Language modeling
Modeling trees using recursive neural networks
Acquaintance with the prerequisites, terminology, tools and methods necessary for understanding the principles underlying neural network models of the language
Useful and non -standard ways to apply modern technologies to processing tasks The language
This classic guide is devoted to the use of neural networks to process data data (Natural Language Processing - NLP)
In the first half of the book, the basics of machine learning with a teacher on linguistic data and the use of vector rather than symbolic representations of words are considered. The abstraction of the calculation graph is also discussed, which allows you to easily determine and train arbitrary neural networks and underlies modern software neural network libraries.
more specialized neural network architectures are introduced in the second part, including one -dimensional sparkle networks, recurrent neural networks, models of conditional generation and models with a mechanism of attention. These architectures and methods are the driving force of modern machine translation algorithms, syntactic analysis and many other applications. Finally, tree -like networks, structural prediction and prospects of multitasking learning are discussed.
The publication is intended for university students, as well as specialists in the field of machine translation and neural networks
neural networks of direct distribution
work with natural language data
Relations between words in context
Language modeling
Modeling trees using recursive neural networks
Acquaintance with the prerequisites, terminology, tools and methods necessary for understanding the principles underlying neural network models of the language
Useful and non -standard ways to apply modern technologies to processing tasks The language
This classic guide is devoted to the use of neural networks to process data data (Natural Language Processing - NLP)
In the first half of the book, the basics of machine learning with a teacher on linguistic data and the use of vector rather than symbolic representations of words are considered. The abstraction of the calculation graph is also discussed, which allows you to easily determine and train arbitrary neural networks and underlies modern software neural network libraries.
more specialized neural network architectures are introduced in the second part, including one -dimensional sparkle networks, recurrent neural networks, models of conditional generation and models with a mechanism of attention. These architectures and methods are the driving force of modern machine translation algorithms, syntactic analysis and many other applications. Finally, tree -like networks, structural prediction and prospects of multitasking learning are discussed.
The publication is intended for university students, as well as specialists in the field of machine translation and neural networks
Author:
Author:Goldberg Yoav
Cover:
Cover:Hard
Category:
- Category:Computer & Technology
ISBN:
ISBN:978-5-97060-754-1
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