Hi Pythonistas!
We’ve explored models that predict and classify. Now let’s dive into models that understand by reconstructing data.
Meet Autoencoders - neural networks that learn to compress and then rebuild inputs.
What Is an Autoencoder?
Autoencoders have two parts:
Encoder: Compresses the input into a smaller representation (called the latent space or bottleneck).
Decoder: Takes this compressed info and tries to reconstruct the original input.
The goal?
Learn a compact "summary" of data that still lets you rebuild it well.
Why Compress?
- Think of autoencoders like zipping a file.
- Compress big data into small codes
- Capture essential features
- Throw away noise or redundant info
Applications include:
- Dimensionality reduction
- Anomaly detection
- Data denoising
- Pretraining for other tasks
How Does It Work?
Autoencoders are trained to minimize reconstruction error basically, how close the output is to the original input.
During training:
The encoder learns to squeeze inputs into a tight space.
The decoder learns to unpack that tight space back to the original shape.
The latent space is a compressed snapshot.
If it’s too large → model just memorizes (no compression).
If it’s too small → model loses important info.
Good autoencoders find the sweet spot.
Variants to Know
Denoising Autoencoders
Input noisy data
Output clean data
Useful for removing noise from images or signals
Variational Autoencoders (VAEs)
Learn distributions in latent space
Useful for generative tasks create new data resembling the original
Where Are Autoencoders Used?
- Image compression and generation
- Anomaly detection in fraud or manufacturing
- Data visualization (like t-SNE alternatives)
- Pretraining layers for better features
What I Learned This Week
Autoencoders learn to compress and reconstruct data
Consist of an encoder + decoder
Latent space is a compact data summary
Useful for compression, denoising, anomaly detection, and generation
Autoencoders teach networks to understand the essence of data not just predict, but recreate.
What’s Coming Next
Next week we will learn about GANs