This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP).

This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms.

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP).

About the book Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions quickly by building on existing pretrained models.

Representation learning refers to modern data transformation techniques that convert data of different modalities and complexity, including texts, graphs, and relations, into compact tabular representations, which effectively capture their ...

Inspired by this, a higher-level representation named semantic representation is introduced. In this book, the authors propose a dataset that weakens image processing and natural language processing.

Learning-Based Local Visual Representation and Indexing, reviews the state-of-the-art in visual content representation and indexing, introduces cutting-edge techniques in learning based visual representation, and discusses emerging topics ...

What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule ...

This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g.