Hands-On Natural Language Processing with Python: A practical guide to applying deep learning architectures to your NLP applications

Table of Contents

1. Getting Started
2. Text Classification and POS Tagging Using NLTK
3. Deep Learning and TensorFlow
4. Semantic Embedding Using Shallow Models
5. Text Classification Using LSTM
6. Searching and DeDuplicating Using CNNs
7. Named Entity Recognition Using Character LSTM
8. Text Generation and Summarization Using GRUs
9. Question-Answering and Chatbots Using Memory Networks
10. Machine Translation Using the Attention-Based Model
11. Speech Recognition Using DeepSpeech
12. Text-to-Speech Using Tacotron
13. Deploying Trained Models