num_pages: 296
description: Explore the most challenging issues of natural language processing and learn how to solve them with cutting-edge deep learning!Inside Deep Learning for Natural Language Processing youll find a wealth of NLP insights An overview of NLP and deep learning One-hot text representations Word embeddings Models for textual similarity Sequential NLP Semantic role labeling Deep memory-based NLP Linguistic structure Hyperparameters for deep NLPDeep learning has advanced natural language processing to exciting new levels and powerful new applications! For the first time computer systems can achieve human levels of summarizing making connections and other tasks that require comprehension and context. Deep Learning for Natural Language Processing reveals the groundbreaking techniques that make these innovations possible. Stephan Raaijmakers distills his extensive knowledge into useful best practices real-world applications and the inner workings of top NLP algorithms.Purchase of the print book includes a free eBook in PDF Kindle and ePub formats from Manning Publications.About the technologyDeep learning has transformed the field of natural language processing. Neural networks recognize not just words and phrases but also patterns. Models infer meaning from context and determine emotional tone. Powerful deep learning-based NLP models open up a goldmine of potential uses.About the bookDeep Learning for Natural Language Processing teaches you how to create advanced NLP applications using Python and the Keras deep learning library. Youll learn to use state-of the-art tools and techniques including BERT and XLNET multitask learning and deep memory-based NLP. Fascinating examples give you hands-on experience with a variety of real world NLP applications. Plus the detailed code discussions show you exactly how to adapt each example to your own uses!What's inside Improve question answering with sequential NLP Boost performance with linguistic multitask learning Accurately interpret linguistic structure Master multiple word embedding techniquesAbout the readerFor readers with intermediate Python skills and a general knowledge of NLP. No experience with deep learning is required.About the authorStephan Raaijmakers is professor of Communicative AI at Leiden University and a senior scientist at The Netherlands Organization for Applied Scientific Research (TNO).Table of ContentsPART 1 INTRODUCTION1 Deep learning for NLP2 Deep learning and The basics3 Text embeddingsPART 2 DEEP NLP4 Textual similarity5 Sequential NLP6 Episodic memory for NLPPART 3 ADVANCED TOPICS7 Attention8 Multitask learning9 Transformers10 Applications of Hands-on with BERT
language: English
title: Deep Learning for Natural Language Processing
publisher: Manning