Applied Natural Language Processing in the Enterprise

Spis treści ebooka

• Preface
  • What Is Natural Language Processing?
  • Why Should I Read This Book?
  • What Do I Need to Know Already?
  • What Is This Book All About?
  • How Is This Book Organized?
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
    • Ajay
    • Ankur
• I. Scratching the Surface
• 1. Introduction to NLP
  • What Is NLP?
    • Popular Applications
    • History
    • Inflection Points
    • A Final Word
  • Basic NLP
    • Defining NLP Tasks
    • Set Up the Programming Environment
    • spaCy, fast.ai, and Hugging Face
      • spaCy
      • fast.ai
      • Hugging Face
    • Perform NLP Tasks Using spaCy
      • Download pretrained language models
      • Tokenization
      • Part-of-speech tagging
      • Dependency parsing
      • Chunking
      • Lemmatization
      • Named entity recognition
      • Named entity linking
  • Conclusion
• 2. Transformers and Transfer Learning
  • Training with fastai
    • Using the fastai Library
    • ULMFiT for Transfer Learning
    • Fine-Tuning a Language Model on IMDb
    • Training a Text Classifier
  • Inference with Hugging Face
    • Loading Models
    • Generating Predictions
  • Conclusion
• 3. NLP Tasks and Applications
  • Pretrained Language Models
  • Transfer Learning and Fine-Tuning
  • NLP Tasks
  • Natural Language Dataset
    • Explore the AG Dataset
  • NLP Task #1: Named Entity Recognition
    • Perform Inference Using the Original spaCy Model
    • Custom NER
    • Annotate via Prodigy: NER
    • Train the Custom NER Model Using spaCy
    • Custom NER Model Versus Original NER Model
  • NLP Task #2: Text Classification
    • Annotate via Prodigy: Text Classification
    • Train Text Classification Models Using spaCy
  • Conclusion
• II. The Cogs in the Machine
• 4. Tokenization
  • A Minimal Tokenizer
  • Hugging Face Tokenizers
    • Subword Tokenization
  • Building Your Own Tokenizer
  • Conclusion
• 5. Embeddings: How Machines Understand Words
  • Understanding Versus Reading Text
  • Word Vectors
    • Word2Vec
      • A new hope
      • The English language strikes back
      • The Return of the Character-Level Models
    • Embeddings in the Age of Transfer Learning
  • Embeddings in Practice
    • Preprocessing
    • Model
    • Training
    • Validation
  • Embedding Things That Arent Words
    • A sonnet in the MIDI protocol
    • Making Vectorized Music
    • Some General Tips for Making Custom Embeddings
  • Conclusion
• 6. Recurrent Neural Networks and Other Sequence Models
  • Recurrent Neural Networks
    • RNNs in PyTorch from Scratch
    • Bidirectional RNN
    • Sequence to Sequence Using RNNs
  • Long Short-Term Memory
  • Gated Recurrent Units
  • Conclusion
• 7. Transformers
  • Building a Transformer from Scratch
  • Attention Mechanisms
    • Dot Product Attention
    • Scaled Dot Product Attention
    • Multi-Head Self-Attention
    • Adaptive Attention Span
    • Persistent Memory/All-Attention
    • Product-Key Memory
  • Transformers for Computer Vision
  • Conclusion
• 8. BERTology: Putting It All Together
  • ImageNet
    • The Power of Pretrained Models
  • The Path to NLPs ImageNet Moment
  • Pretrained Word Embeddings
    • The Limitations of One-Hot Encoding
    • Word2Vec
    • GloVe
    • fastText
    • Context-Aware Pretrained Word Embeddings
  • Sequential Models
    • Sequential Data and the Importance of Sequential Models
  • RNNs
    • Vanilla RNNs
    • LSTM Networks
    • GRUs
  • Attention Mechanisms
  • Transformers
    • Transformer-XL
  • NLPs ImageNet Moment
    • Universal Language Model Fine-Tuning
    • ELMo
    • BERT
    • BERTology
    • GPT-1, GPT-2, GPT-3
  • Conclusion
• III. Outside the Wall
• 9. Tools of the Trade
  • Deep Learning Frameworks
    • PyTorch
    • TensorFlow
    • Jax
    • Julia
  • Visualization and Experiment Tracking
    • TensorBoard
    • Weights & Biases
    • Neptune
    • Comet
    • MLflow
  • AutoML
    • H2O.ai
    • Dataiku
    • DataRobot
  • ML Infrastructure and Compute
    • Paperspace
    • FloydHub
    • Google Colab
    • Kaggle Kernels
    • Lambda GPU Cloud
  • Edge/On-Device Inference
    • ONNX
    • Core ML
    • Edge Accelerators
  • Cloud Inference and Machine Learning as a Service
    • AWS
    • Microsoft Azure
    • Google Cloud Platform
  • Continuous Integration and Delivery
  • Conclusion
• 10. Visualization
  • Our First Streamlit App
    • Build the Streamlit App
    • Deploy the Streamlit App
    • Explore the Streamlit Web App
    • Build and Deploy a Streamlit App for Custom NER
    • Build and Deploy a Streamlit App for Text Classification on AG News Dataset
    • Build and Deploy a Streamlit App for Text Classification on Custom Text
  • Conclusion
• 11. Productionization
  • Data Scientists, Engineers, and Analysts
    • Prototyping, Deployment, and Maintenance
    • Notebooks and Scripts
  • Databricks: Your Unified Data Analytics Platform
    • Support for Big Data
    • Support for Multiple Programming Languages
    • Support for ML Frameworks
    • Support for Model Repository, Access Control, Data Lineage, and Versioning
  • Databricks Setup
    • Set Up Access to S3 Bucket
    • Set Up Libraries
    • Create Cluster
    • Create Notebook
    • Enable Init Script and Restart Cluster
    • Run Speed Test: Inference on NER Using spaCy
  • Machine Learning Jobs
    • Production Pipeline Notebook
    • Scheduled Machine Learning Jobs
    • Event-Driven Machine Learning Pipeline
  • MLflow
    • Log and Register Model
    • MLflow Model Serving
  • Alternatives to Databricks
    • Amazon SageMaker
    • Saturn Cloud
  • Conclusion
• 12. Conclusion
  • Ten Final Lessons
    • Lesson 1: Start with Simple Approaches First
    • Lesson 2: Leverage the Community
    • Lesson 3: Do Not Create from Scratch, When Possible
    • Lesson 4: Intuition and Experience Trounces Theory
    • Lesson 5: Fight Decision Fatigue
    • Lesson 6: Data Is King
    • Lesson 7: Lean on Humans
    • Lesson 8: Pair Yourself with Really Great Engineers
    • Lesson 9: Ensemble
    • Lesson 10: Have Fun
  • Final Word
• A. Scaling
  • Multi-GPU Training
  • Distributed Training
  • What Makes Deep Training Fast?
• B. CUDA
  • Threads and Thread Blocks
  • Writing CUDA Kernels
  • CUDA in Practice
• Index

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