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The goal F AI engineering

Build a voice interface

From waveform to spectrogram and mel features, on to speech recognition and speech synthesis — and the privacy questions that voice data always brings with it.

Knowledge nodes
46
From zero
about 34 h
Labs
6
See what you already know — no account

The diagnostic removes what you already know, so your path is usually much shorter.

What you can do afterwards

Labs along the way

You write the code. Tests you cannot see decide whether it holds up.

Lab: a neural network in pure NumPy — forward, backprop, gradient checkDa sandbox · about 75 minLab: dot product, norm and cosine similarityDin the browser · about 40 minLab: gradient descent from scratchDin the browser · about 50 minLab: matrix multiplication and one layer of a neural networkDin the browser · about 45 minLab: scaled dot-product attention with a causal maskDa sandbox · about 60 minLab: semantic search with embeddingsDa sandbox · about 45 min

The whole path

Everything the goal builds on, grouped by level and in the order it builds on itself. Show on the map

AExplorer3 knowledge nodes
  1. Patterns and categories
  2. Sequences and precise instructions
  3. Integer arithmetic and order of operations
BInvestigator7 knowledge nodes
  1. Data in everyday life
  2. Algorithmic thinking
  3. Rule-based systems and machine learning
  4. Source criticism and responsibility in AI use
  5. Binary numbers and bits
  6. Training data, features and labels
  7. Classification: how a model sorts information
CBuilder11 knowledge nodes
  1. Functions and coordinate systems
  2. Distance calculation and Pythagoras' theorem
  3. Programming logic — variables, conditions, loops
  4. Python — the basics
  5. Python — lists, loops and dictionaries
  6. Statistics — mean, median and spread
  7. Probability — the basics
  8. Digital representation: text, images and audio
  9. Sound as data: sampling rate and amplitude
  10. Linear regression: fitting a straight line to data
  11. Neural networks — the intuition
DAI developer14 knowledge nodes
  1. Derivatives and optimisation
  2. Licences and open data
  3. Loss functions
  4. Gradient descent
  5. Trigonometry — the basics
  6. Vectors
  7. Matrices and matrix multiplication
  8. Neural networks — the forward pass with matrices
  9. Backpropagation
  10. Embeddings — words as vectors
  11. Attention
  12. PyTorch — tensors and autograd
  13. Train a neural network in PyTorch
  14. Transformers — the architecture
EUniversity7 knowledge nodes
  1. Personal data and anonymisation
  2. Voice data and privacy
  3. Fourier and spectrograms
  4. The mel scale and MFCCs
  5. Convolutional networks (CNNs)
  6. Generative models — an overview
  7. Audio classification
FAI engineering4 knowledge nodes
  1. Diffusion models
  2. Speech synthesis (TTS)
  3. Music and audio generation
  4. Speech recognition (ASR)