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What’s Inside Deep Orbit

every module, lesson by lesson

1. 01

   Module 1: Foundations: Tensors & Data

   Free

   Tensors, their shapes, and the operations that change them, starting from the Float32Array you already know.

   5 lessons

   ~109 min
2. 02

   Module 2: Neural Networks: Layers & Architecture

   Assemble and compile an untrained classifier: a neuron (a weighted sum written with reduce), the activation that keeps stacked layers from collapsing, layers that cost parameters, and the batch axis a reading carries.

   5 lessons

   ~129 min
3. 03

   Module 3: Training: Loss, Gradients & Optimization

   Loss functions score each guess, gradient descent nudges the weights to shrink that score, backpropagation gets every nudge from one backward pass, the loop ties them together, and evaluation decides what ships.

   5 lessons

   ~167 min
4. 04

   Module 4: Browser ML Runtimes

   Run a trained model in the browser: TensorFlow.js setup, exporting, loading and caching a model, real-time inference (a prediction per reading), and how TensorFlow.js, ONNX Runtime Web, and Transformers.js compare.

   6 lessons

   ~165 min
5. 05

   Module 5: Signal Processing

   Use the Fourier Transform (which frequencies a signal contains), spectrograms (those frequencies pictured over time), and windowing (tapering each slice's edges) to turn raw readings into inputs a model learns from.

   5 lessons

   ~135 min
6. 06

   Module 6: Recurrent Networks

   Recurrent networks that carry state across time: RNNs and LSTMs mapped onto the state-update code you already write, plus the split-by-time rule that keeps a windowed stream from scoring itself.

   5 lessons

   ~142 min
7. 07

   Module 7: Anomaly Detection

   A baseline that says what normal is, an autoencoder that learns it, reconstruction error scored against a tuned cutoff, and a 2-of-3 vote across three detectors that can still fail together.

   5 lessons

   ~178 min
8. 08

   Module 8: Advanced Time-Series

   Explore attention, forecasting, Kalman filtering and ensembles. Compare their assumptions, inspect errors and decide what each method can support.

   5 lessons

   ~148 min
9. 09

   Module 9: LLM Integration

   Add language models to the pipeline: prompt engineering, retrieval over your own logs, and orchestrating more than one model.

   5 lessons

   ~155 min
10. 10

    Module 10: Optimization & Edge

    Shrink a model to fit the hardware: profiling, quantization (fewer bytes per weight), pruning (zeroing small weights and measuring the effect), caching, and WebGPU (the math on the GPU), a trade of size against accuracy.

    5 lessons

    ~161 min
11. 11

    Module 11: Capstone: Deploy DEEP SCAN

    Assemble the finished system: architecture, a deployment pipeline, monitoring, the documentation that ships with it, and one rehearsal that trains the deploy candidate and gates it.

    5 lessons

    ~187 min

Module 1 is free, no account needed. Browse the full Deep Orbit syllabus.