🧭 The Complete Journey · 15 Modules

AI Fundamentals

From a single pixel to building your own working language model. Every concept behind modern AI — taught visually, one interactive module at a time. Start at the top and click Continue through to the end.

▶ Start with Module 1

15modules

4parts + capstone + bonus

50+interactive games

0→LLMfull arc

PART A

Foundations — How a Neural Network Works

Before language, the basics: how a computer sees, what a neuron is, and how a network learns from its mistakes.

MODULE 1

A Picture Becomes Numbers

How an image turns into a flat list of numbers a network can read.

pixelsvectors

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MODULE 2

The Neuron & the Network

Weights, biases, layers — the building blocks of every model.

neuronslayers

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MODULE 3

How a Network Learns

Cost, gradient descent & backprop — "turning the knobs" to improve.

trainingbackprop

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PART B

Words & Language — Teaching Machines to Read

Now apply networks to text: turn words into meaning-vectors, predict what comes next, and judge sentiment.

MODULE 4

Words as Numbers

word2vec & embeddings — why king − man + woman ≈ queen.

word2vecembeddings

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MODULE 5

Predicting the Next Word

Bigrams, trigrams, n-grams & neural prediction — the core LLM task.

n-gramsprediction

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MODULE 6

Reading the Mood

Sentiment analysis — classifying text as positive or negative.

sentimentclassify

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PART C

Sequence & Attention — The Road to Transformers

The heart of the course: how models handle order & memory, why the old way failed, and the breakthrough that fixed it — attention.

MODULE 7

Memory & Context (RNNs)

The first models that remember earlier words via a hidden state.

RNNmemory

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MODULE 8

The Vanishing Gradient

Why RNNs forget long context — the wall that forced a new idea.

the limit

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MODULE 9

Attention: The Big Idea

"Attention Is All You Need" — every word looks at every word.

attention2017

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MODULE 10

Query, Key, Value

The mechanism inside attention — a search engine per word + multi-head.

Q·K·Vmulti-head

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PART D

Transformers & LLMs — The Modern Machine

Assemble everything into the full Transformer, look inside a decoder as it writes, and run a model yourself.

MODULE 11

The Transformer: Encoder vs Decoder

The full architecture; GPT (writer) vs BERT (understander).

encoderdecoder

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MODULE 12

Inside the Decoder

Tokenize → embed → position → layers → output → auto-regression.

tokensgeneration

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MODULE 13

Running LLMs Yourself

Ollama, system prompts, temperature & top-p, activation functions.

Ollamatemp/top-p

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🏗️ CAPSTONE

Build an AI — Put It All Together

The finale. Assemble a working text model from raw data to generation — using everything you learned.

MODULE 14 · CAPSTONE

The Build Simulator

Dataset → Tokenize → Embed → Architecture → Train → Generate. Build an LLM end-to-end in a sandbox.

full pipelinehands-on

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🎓 BONUS

How Models Really Learn

The deep-dive everyone asks about: how a real LLM actually gets trained to become a helpful assistant.

MODULE 15 · BONUS

Pretraining → Fine-tuning → RLHF

Where knowledge comes from, how it learns the assistant format, and how human feedback polishes it.

pretrainingRLHF

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🎓 By the end, you'll understand — and be able to build — the whole pipeline:

✓ Pixels → vectors ✓ Neurons & learning ✓ Word embeddings ✓ Next-word prediction ✓ Sentiment ✓ RNNs & memory ✓ Vanishing gradient ✓ Attention ✓ Query/Key/Value ✓ Transformers ✓ Inside the decoder ✓ Running LLMs ✓ Building one yourself ✓ How models are trained (RLHF)

How to use this map: each module is self-contained and builds on the last. Click any card to jump in, or just hit "Start with Module 1" and use the Continue → button at the bottom of every page to walk the whole path. You can always come back here to jump around.

▶ Begin the journey — Module 1

🚀 Finished the fundamentals? Continue to Applied AI — the hands-on sequel where you build it all in real PyTorch: train a GPT from scratch, fine-tune with LoRA, and set up RAG.
⚙️ Go to the Applied AI course →