6.18 Autoencoders and representation learning
You can learn a useful compressed representation without labels.
Before:05. Classical Machine LearningUnlocks:07. Natural Language Processing11. The ECE Bridge — Signals, Edge & Embedded AI
Autoencoders learn compressed representations by reconstructing input through a bottleneck; variational autoencoders make the latent space smooth enough to sample from, via the reparameterisation trick. Embeddings learned this way become reusable assets across tasks. It opens the representation-learning run. The failure to design against is trivial identity — a bottleneck too wide, and the network learns to copy rather than to understand, perfectly and uselessly.
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Undercomplete, denoising, sparse autoencoders
Networks trained to reproduce their own input through a narrow middle, with variants that add noise or sparsity to force better representations. The bottleneck is where the learning happens.
Latent space structure and interpolation
What the compressed representation looks like, and whether moving smoothly through it produces sensible outputs. This is where representation quality becomes visible.
Variational autoencoders and the reparameterization trick
A probabilistic version that learns a distribution rather than a point, using a trick that makes the sampling differentiable. That trick is the technical heart of the topic.
Embeddings as a reusable asset
A learned representation is an asset reusable across tasks, which is frequently more valuable than the model that produced it. This framing matters in production work.
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