Description
Morgan Neural Network Methods in Natural Language Processing by Yoav Goldberg, Series Graeme Hirst
Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data._x000D__x000D_The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries._x000D__x000D_The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning._x000D_ Table of contents :- _x000D_
Preface_x000D_
Acknowledgments_x000D_
Introduction_x000D_
Learning Basics and Linear Models_x000D_
Learning Basics and Linear Models_x000D_
From Linear Models to Multi-layer Perceptrons_x000D_
Feed-forward Neural Networks_x000D_
Neural Network Training_x000D_
Features for Textual Data_x000D_
Case Studies of NLP Features_x000D_
From Textual Features to Inputs_x000D_
Language Modeling_x000D_
Pre-trained Word Representations_x000D_
Pre-trained Word Representations_x000D_
Using Word Embeddings_x000D_
Case Study: A Feed-forward Architecture for Sentence_x000D_
Case Study: A Feed-forward Architecture for Sentence Meaning Inference_x000D_
Ngram Detectors: Convolutional Neural Networks_x000D_
Recurrent Neural Networks: Modeling Sequences and Stacks_x000D_
Concrete Recurrent Neural Network Architectures_x000D_
Modeling with Recurrent Networks_x000D_
Modeling with Recurrent Networks_x000D_
Conditioned Generation_x000D_
Modeling Trees with Recursive Neural Networks_x000D_
Modeling Trees with Recursive Neural Networks_x000D_
Structured Output Prediction_x000D_
Cascaded, Multi-task and Semi-supervised Learning_x000D_
Conclusion_x000D_
Bibliography_x000D_
Author's Biography_x000D_