💬 NLP & Transformers

Master natural language processing and the revolutionary transformer architecture

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NLP & Transformers Curriculum

12
Specialized Units
~105
NLP Concepts
25+
Model Architectures
40+
Language Tasks
1

NLP Fundamentals

Introduction to natural language processing, linguistic concepts, and computational approaches.

  • What is NLP?
  • Linguistic foundations
  • Levels of language analysis
  • NLP pipeline overview
  • Challenges in NLP
  • Language resources
  • Evaluation metrics
  • Historical development
2

Text Preprocessing

Essential text cleaning, normalization, and preprocessing techniques for NLP tasks.

  • Text cleaning and normalization
  • Tokenization strategies
  • Stop word removal
  • Stemming and lemmatization
  • Regular expressions
  • Unicode handling
  • Subword tokenization
  • Language detection
3

Text Representation

Learn various methods to convert text into numerical representations for machine learning.

  • Bag of words
  • TF-IDF
  • N-gram models
  • Word embeddings
  • Word2Vec and GloVe
  • Contextual embeddings
  • Document embeddings
  • Sparse vs dense representations
4

Classical NLP Tasks

Master fundamental NLP tasks using traditional machine learning approaches.

  • Text classification
  • Sentiment analysis
  • Named entity recognition
  • Part-of-speech tagging
  • Information extraction
  • Text clustering
  • Topic modeling
  • Keyword extraction
5

Sequence Models

Explore RNNs, LSTMs, and GRUs for sequential text processing and language modeling.

  • Recurrent neural networks
  • LSTM and GRU
  • Bidirectional RNNs
  • Sequence-to-sequence models
  • Encoder-decoder architecture
  • Language modeling
  • Text generation
  • Attention mechanisms
6

Attention and Transformers

Understand the transformer architecture that revolutionized NLP and deep learning.

  • Attention mechanism
  • Self-attention
  • Multi-head attention
  • Transformer architecture
  • Positional encoding
  • Layer normalization
  • Residual connections
  • Training strategies
7

Pre-trained Language Models

Leverage powerful pre-trained models like BERT, GPT, and their variants.

  • Transfer learning in NLP
  • BERT and variants
  • GPT family
  • T5 and UL2
  • Fine-tuning strategies
  • Prompt engineering
  • In-context learning
  • Model comparison
8

Advanced NLP Tasks

Tackle complex language understanding and generation tasks with modern techniques.

  • Machine translation
  • Question answering
  • Text summarization
  • Dialogue systems
  • Natural language inference
  • Coreference resolution
  • Semantic parsing
  • Knowledge graphs
9

Multimodal and Multilingual NLP

Explore systems that combine text with other modalities and handle multiple languages.

  • Vision-language models
  • Speech and text integration
  • Cross-modal understanding
  • Multilingual models
  • Cross-lingual transfer
  • Low-resource languages
  • Code-switching
  • Cultural considerations
10

Large Language Models

Understand the architecture, training, and capabilities of large-scale language models.

  • Scaling laws
  • Training large models
  • Emergent abilities
  • Prompt engineering
  • Chain-of-thought reasoning
  • In-context learning
  • Model alignment
  • Efficiency techniques
11

NLP Applications

Build real-world NLP applications and systems for various domains and use cases.

  • Search and information retrieval
  • Conversational AI
  • Content moderation
  • Document processing
  • Social media analysis
  • Financial NLP
  • Healthcare NLP
  • Legal tech applications
12

Ethics and Future Directions

Explore ethical considerations, limitations, and future developments in NLP.

  • Bias in language models
  • Fairness and inclusivity
  • Privacy considerations
  • Misinformation detection
  • Model interpretability
  • Environmental impact
  • Emerging research areas
  • Career opportunities

Unit 1: NLP Fundamentals

Introduction to natural language processing, linguistic concepts, and computational approaches.

What is NLP?

Understand the scope, definition, and interdisciplinary nature of natural language processing.

Definition Scope Applications
Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and human language. It combines computational linguistics, machine learning, and deep learning to enable machines to understand, interpret, and generate human language.
# NLP Definition and Scope
nlp_overview = {
  "definition": "Computational processing of human language",
  "core_components": {
    "computational_linguistics": "Mathematical and algorithmic approaches to language",
    "machine_learning": "Data-driven approaches to language understanding",
    "deep_learning": "Neural network-based language models",
    "cognitive_science": "Understanding human language processing"
  },
  "main_goals": {
    "understanding": "Extract meaning from text and speech",
    "generation": "Produce coherent and contextually appropriate text",
    "interaction": "Enable natural communication between humans and machines",
    "translation": "Convert between different languages"
  },
  "applications": [
    "Virtual assistants", "Machine translation", "Sentiment analysis",
    "Search engines", "Chatbots", "Document summarization",
    "Question answering", "Content recommendation"
  ]
}

Linguistic Foundations

Explore the linguistic principles that underpin computational approaches to language processing.

Key Linguistic Concepts:
• Phonetics/Phonology: Sound systems of language
• Morphology: Word structure and formation
• Syntax: Sentence structure and grammar rules
• Semantics: Meaning of words and sentences
• Pragmatics: Context and intended meaning
Ambiguity Challenge:
Natural language is inherently ambiguous at multiple levels. A single sentence can have multiple valid interpretations based on syntax, semantics, or context. This makes NLP particularly challenging compared to formal languages.
# Linguistic Levels in NLP
linguistic_levels = {
  "phonological": {
    "description": "Sound patterns and pronunciation",
    "nlp_tasks": ["Speech recognition", "Text-to-speech", "Phonetic analysis"],
    "example": "Converting /kæt/ to 'cat'"
  },
  "morphological": {
    "description": "Word structure and formation",
    "nlp_tasks": ["Stemming", "Lemmatization", "Morphological analysis"],
    "example": "'running' → stem: 'run', suffix: '-ing'"
  },
  "syntactic": {
    "description": "Sentence structure and grammar",
    "nlp_tasks": ["Parsing", "POS tagging", "Grammar checking"],
    "example": "Subject-verb-object structure analysis"
  },
  "semantic": {
    "description": "Meaning of words and sentences",
    "nlp_tasks": ["Word sense disambiguation", "Semantic role labeling"],
    "example": "'bank' → financial institution vs. river bank"
  },
  "pragmatic": {
    "description": "Context-dependent meaning",
    "nlp_tasks": ["Dialogue systems", "Discourse analysis"],
    "example": "'Can you pass the salt?' → request, not question"
  }
}

Challenges in NLP

Understand the fundamental challenges that make natural language processing complex and difficult.

Core NLP Challenges:
• Ambiguity: Multiple valid interpretations
• Context dependency: Meaning changes with context
• Variability: Different ways to express same idea
• World knowledge: Implicit assumptions and common sense
• Cultural and social factors: Language use varies across groups
Data Challenges:
Language data is often noisy, biased, and sparse for many languages and domains. Quality labeled data is expensive to create, and language constantly evolves with new words and meanings.
# NLP Challenge Categories
nlp_challenges = {
  "linguistic_challenges": {
    "ambiguity": {
      "lexical": "Word has multiple meanings",
      "syntactic": "Multiple parse trees possible",
      "semantic": "Multiple interpretations of meaning",
      "example": "'I saw the man with the telescope'"
    },
    "variability