Natural Language Processing Basics
Coming Soon
This lesson is currently under development. Check back soon for comprehensive content covering:
- Text Processing: Tokenization, stemming, lemmatization, part-of-speech tagging
- Word Embeddings: Word2Vec, GloVe, contextual embeddings (BERT, GPT)
- Intent Detection: Classification models for understanding robot commands
- LLM Integration: Using GPT-4, Claude for natural robot interaction
- Speech Recognition: Whisper, end-to-end speech-to-text for voice commands
Expected Completion: This lesson will be available soon.
Learning Objectives
By the end of this lesson, you will be able to:
- Process natural language input for robot commands
- Implement intent detection for conversational interfaces
- Integrate LLMs into robot interaction systems
- Understand speech recognition for voice-controlled robots
Further Reading
- Speech and Language Processing by Dan Jurafsky
- Hugging Face Transformers — State-of-the-art NLP models
- OpenAI Whisper — Robust speech recognition
What's Next?
Continue to Lesson 5: Reinforcement Learning Introduction to learn how robots learn from experience.
This lesson is part of Chapter 2: AI Fundamentals Review