Model Training
Autoencoders
A neural network that learns to compress data into a small code and then unzip it back to the original.
Definition
A type of neural network trained to compress input data into a compact latent representation (encoder) and then reconstruct the original input (decoder). Used for dimensionality reduction, anomaly detection, and generative modeling.
Why it matters
Excellent for unsupervised learning tasks like cleaning noisy images or finding anomalies.
Related terms in Model Training
Adversarial Training
Teaching an AI to defend itself by constantly attacking it with tricky or malicious inputs during training.
Distillation (Model Distillation)
Teaching a small, fast AI model to mimic a large, expensive one, so you get similar results at a fraction of the cost.
Dropout
Randomly turning off some neurons during training so the AI doesn't over-memorize and can generalize better.
Epoch
One complete pass through the entire training dataset, the AI sees every example once per epoch.
From vocabulary to outcomes
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