Artificial Intelligence Neural Network
Season 01 · How AI Thinks

The Architecture of Intelligence

From the perceptron to the transformer — a ground-up exploration of how neural networks learn, predict, and generate, and what it actually means for a machine to understand.

SeriesHow AI Thinks
Episodes03
Total Duration1h 40m
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Episode Archive

Neural network visualization
EP 01 // Neural Networks

How Machines Learn

Weights, activations, and backpropagation — the mathematical engine behind every neural network, explained from first principles.

schedule 35 Min article 3.5k Words
Transformer architecture attention mechanism
EP 02 // Transformers

Attention Is All You Need

The transformer architecture dissected — self-attention, positional encoding, and why this mechanism changed everything in AI.

schedule 40 Min article 4.0k Words
Large language model AI generation
EP 03 // Large Language Models

When Prediction Becomes Thought

How next-token prediction at billion-parameter scale gives rise to emergent reasoning — and why nobody fully understands why it works.

schedule 25 Min article 2.8k Words
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Technical Specifications

  • Focus AreaML / LLM
  • Episodes03
  • Read Time1h 40m
  • DifficultyAdvanced

menu_book Quick Glossary

Backpropagation

The algorithm that updates neural network weights by propagating errors backward through layers to minimize loss.

Attention Mechanism

A technique allowing models to weigh the relevance of every word to every other word simultaneously — the core of transformers.

Emergent Behavior

Capabilities that appear in large models that were not explicitly trained for — reasoning, analogies, code generation.