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Table of Contents:
Preface
Acknowledgments
Introduction
Learning Basics and Linear Models
From Linear Models to Multi-layer Perceptrons
Feed-forward Neural Networks
Neural Network Training
Features for Textual Data
Case Studies of NLP Features
From Textual Features to Inputs
Language Modeling
Pre-trained Word Representations
Using Word Embeddings
Case Study: A Feed-forward Architecture for Sentence Meaning Inference
Ngram Detectors: Convolutional Neural Networks
Recurrent Neural Networks: Modeling Sequences and Stacks
Concrete Recurrent Neural Network Architectures
Modeling with Recurrent Networks
Conditioned Generation
Modeling Trees with Recursive Neural Networks
Structured Output Prediction
Cascaded, Multi-task and Semi-supervised Learning
Conclusion
Bibliography
Author's Biography
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