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by Hannes Hapke, Cole Howard, Hobson Lane
Natural Language Processing in Action
Copyright
Brief Table of Contents
Table of Contents
Foreword
Preface
Acknowledgments
About this Book
About the Authors
About the cover Illustration
Part 1. Wordy machines
Chapter 1. Packets of thought (NLP overview)
1.1. Natural language vs. programming language
1.2. The magic
1.3. Practical applications
1.4. Language through a computer’s “eyes”
1.5. A brief overflight of hyperspace
1.6. Word order and grammar
1.7. A chatbot natural language pipeline
1.8. Processing in depth
1.9. Natural language IQ
Summary
Chapter 2. Build your vocabulary (word tokenization)
2.1. Challenges (a preview of stemming)
2.2. Building your vocabulary with a tokenizer
2.3. Sentiment
Summary
Chapter 3. Math with words (TF-IDF vectors)
3.1. Bag of words
3.2. Vectorizing
3.3. Zipf’s Law
3.4. Topic modeling
Summary
Chapter 4. Finding meaning in word counts (semantic analysis)
4.1. From word counts to topic scores
4.2. Latent semantic analysis
4.3. Singular value decomposition
4.4. Principal component analysis
4.5. Latent Dirichlet allocation (LDiA)
4.6. Distance and similarity
4.7. Steering with feedback
4.8. Topic vector power
Summary
Part 2. Deeper learning (neural networks)
Chapter 5. Baby steps with neural networks (perceptrons and backpropagation)
5.1. Neural networks, the ingredient list
Summary
Chapter 6. Reasoning with word vectors (Word2vec)
6.1. Semantic queries and analogies
6.2. Word vectors
Summary
Chapter 7. Getting words in order with convolutional neural networks (CNNs)
7.1. Learning meaning
7.2. Toolkit
7.3. Convolutional neural nets
7.4. Narrow windows indeed
Summary
Chapter 8. Loopy (recurrent) neural networks (RNNs)
8.1. Remembering with recurrent networks
8.2. Putting things together
8.3. Let’s get to learning our past selves
8.4. Hyperparameters
8.5. Predicting
Summary
Chapter 9. Improving retention with long short-term memory networks
9.1. LSTM
Summary
Chapter 10. Sequence-to-sequence models and attention
10.1. Encoder-decoder architecture
10.2. Assembling a sequence-to-sequence pipeline
10.3. Training the sequence-to-sequence network
10.4. Building a chatbot using sequence-to-sequence networks
10.5. Enhancements
10.6. In the real world
Summary
Part 3. Getting real (real-world NLP challenges)
Chapter 11. Information extraction (named entity extraction and question answering)
11.1. Named entities and relations
11.2. Regular patterns
11.3. Information worth extracting
11.4. Extracting relationships (relations)
11.5. In the real world
Summary
Chapter 12. Getting chatty (dialog engines)
12.1. Language skill
12.2. Pattern-matching approach
12.3. Grounding
12.4 Retrieval (search)
12.5. Generative models
12.6 Four-wheel drive
12.7. Design process
12.8 Trickery
12.9. In the real world
Summary
Chapter 13. Scaling up (optimization, parallelization, and batch processing)
13.1. Too much of a good thing (data)
13.2. Optimizing NLP algorithms
13.3. Constant RAM algorithms
13.4. Parallelizing your NLP computations
13.5. Reducing the memory footprint during model training
13.6. Gaining model insights with TensorBoard
Summary
Appendix A. Your NLP tools
A.1. Anaconda3
A.2. Install NLPIA
A.3. IDE
A.4. Ubuntu package manager
A.5. Mac
A.6. Windows
A.7. NLPIA automagic
Appendix B. Playful Python and regular expressions
B.1. Working with strings
B.2. Mapping in Python (dict and OrderedDict)
B.3. Regular expressions
B.4. Style
B.5. Mastery
Appendix C. Vectors and matrices (linear algebra fundamentals)
C.1. Vectors
Appendix D. Machine learning tools and techniques
D.1. Data selection and avoiding bias
D.2. How fit is fit?
D.3. Knowing is half the battle
D.4. Cross-fit training
D.5. Holding your model back
D.6. Imbalanced training sets
D.7. Performance metrics
D.8. Pro tips
Appendix E. Setting up your AWS GPU
E.1. Steps to create your AWS GPU instance
Appendix F. Locality sensitive hashing
F.1. High-dimensional vectors are different
F.2. High-dimensional indexing
F.3. “Like” prediction
Resources
Applications and project ideas
Courses and tutorials
Tools and packages
Research papers and talks
Competitions and awards
Datasets
Search engines
Glossary
Acronyms
Terms
Chatbot Recirculating (Recurrent) Pipeline
Index
List of Figures
List of Tables
List of Listings
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Natural Language Processing in Action: Understanding, analyzing, and generating text with Python
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