MODULE MAP

Board the Archimedes and learn to see data the way neural networks do. You'll master tensors, shapes, and operations — the building blocks of every ML model — using concepts you already know from frontend development.

Build your first neural network from scratch. You'll learn how neurons, layers, and activations work — translating frontend component composition into network architecture.

Train the DEEP SCAN signal classifier. You'll master loss functions, gradient descent, and the training loop — the engine that turns data into intelligence.

Take your trained model out of the lab and into the browser. Nazari challenges Demir to deploy the DEEP SCAN classifier on the ship's browser-based dashboard — with real performance constraints. You'll master TensorFlow.js setup, model loading, real-time inference, and compare runtimes like a frontend engineer compares frameworks.

The signals show temporal patterns — a rhythm spanning dozens of time steps. Feedforward networks see snapshots; recurrent networks see sequences. You'll build RNNs, LSTMs, and GRUs that remember past inputs and predict what comes next — using concepts from Redux reducers and localStorage caching.

DEEP SCAN's anomaly detector fires on a signal that matches no known category. The reconstruction error is off the charts. You'll build autoencoders that learn what "normal" looks like, then flag anything that deviates — using the same intuition as git diff and error boundaries.

Dr. Farah discovers hidden structure in the signals that only appears in the frequency domain. You'll master the Fourier Transform, spectrograms, and windowing — transforming raw time-series data into rich features that boost DEEP SCAN's accuracy from 78% to 87%.

The crew attempts to predict the anomalous signal's next occurrence. You'll build attention mechanisms, temporal convolutional networks, Kalman filters, and model ensembles — advanced forecasting tools that reveal the signal's timing is changing.

Mirza needs a comprehensive report for Earth Command — 47 days of data, 12,000 classified signals, and one anomaly that could change everything. You'll integrate language models into DEEP SCAN: prompt engineering, RAG over signal logs, and multi-model orchestration.

Nazari's module. Your model works — now make it fast. The bridge dashboard has 512 MB of RAM and your model is 340 MB. You'll master quantization, pruning, caching, and WebGPU acceleration to shrink the model to 42 MB with only 2% accuracy loss.

Deploy the complete DEEP SCAN signal classification system. System architecture, CI/CD pipeline, monitoring, documentation, and launch — everything the crew has built comes together.