Module 1: Foundations of Word Prediction

What You'll Learn

This module takes you on a journey from basic language statistics to sophisticated neural embeddings. You'll discover how language follows predictable patterns, why simple n-gram models have limitations, and how neural networks revolutionized language modeling.

Through interactive visualizations, you'll see how word embeddings capture semantic relationships, understand the training process for neural language models, and explore the specific innovations of Word2Vec that made large-scale embedding learning practical.

Learning Objectives

Understand Zipf's law and power distributions in language
See how next-word prediction enables instruction following
Build n-grams and discover the sparsity problem
Explore word embeddings and semantic vector spaces
Learn about neural language model architectures
Understand training dynamics and loss functions
Compare Word2Vec architectures (Skip-gram vs CBOW)
Discover limitations of static embeddings

Module Progress

5 Sessions • 17 Visualizations

Session 1.1

Introduction to Next-Word Prediction

Session 1.2

N-gram Models & Limitations

Session 1.3

Neural Language Models

Session 1.4

Training Neural Models

Session 1.5

Word2Vec & Static Embeddings

1.1

Introduction to Next-Word Prediction

Discover how language follows statistical patterns and enables AI instruction following

Visualizations

📊

Word Frequency Explorer

Discover how word frequencies in natural language follow predictable power law patterns (Zipf's Law).

Statistics Power Laws Zipf's Law

Explore

❓

Instruction Following Patterns

See how next-word prediction naturally leads to question-answering and instruction-following capabilities.

Emergent Behavior QA Patterns Instructions

Explore

1.2

N-gram Models and Their Limitations

Build n-grams and discover the sparsity problem that motivates neural approaches

Visualizations

🔤

N-gram Builder

Build n-grams from text and see how prediction quality changes with context size.

N-grams Context Prediction

Explore

🕳️

Sparsity Explorer

Visualize how n-gram coverage drops exponentially as sequence length increases.

Sparsity Limitations Exponential Growth

Explore

1.3

Neural Language Models

Understand word embeddings, softmax, and the first neural language models

Visualizations

🗺️

Word Embedding Space

Explore semantic relationships through vector distances in a conceptual 2D embedding space.

Embeddings Semantics Vector Space

Explore

🧠

Bengio Neural Language Model

Explore the complete neural network architecture from input words to probability distributions.

Neural Networks Architecture Bengio Model

Explore

1.4

Training Neural Language Models

Explore loss functions, gradient descent, and training dynamics

Visualizations

📉

Loss Function Explorer

See how cross-entropy loss varies with prediction confidence and understand training dynamics.

Loss Functions Cross-Entropy Training

Explore

⛰️

Gradient Descent Simulator

Watch gradient descent navigate loss landscapes with different learning rates.

Optimization Gradient Descent Learning Rate

Explore

📈

Training Progress Visualizer

Monitor neural language model training with real-time loss and accuracy metrics.

Training Metrics Progress

Explore

🔢

Perplexity Calculator

Calculate and compare perplexity scores across different language models.

Perplexity Evaluation Metrics

Explore

1.5

Word2Vec and Static Embeddings

Deep dive into Word2Vec architectures and discover their limitations

Visualizations

🔄

Word2Vec Architecture Comparison

Compare Skip-gram and CBOW architectures with interactive demonstrations.

Word2Vec Skip-gram CBOW

Explore

🎯

Negative Sampling Demo

Explore how negative sampling makes training Word2Vec dramatically more efficient.

Negative Sampling Efficiency Training

Explore

🧮

Vector Analogy Solver

Solve word analogies with vector arithmetic: king - man + woman = queen

Analogies Vector Math Semantics

Explore

🎭

Polysemy Problem Demo

See how static embeddings struggle with words that have multiple meanings.

Limitations Context Polysemy

Explore