Model Training
Backpropagation
The algorithm that teaches neural networks by calculating how wrong each neuron was and adjusting it backward through the layers.
Definition
An optimization algorithm that computes the gradient of the loss function with respect to each weight by propagating errors backward through the network. The foundation of neural network training.
Why it matters
Without backpropagation, deep learning wouldn't exist, it's the mathematical engine behind all neural network learning.
Related terms in Model Training
Adversarial Training
Teaching an AI to defend itself by constantly attacking it with tricky or malicious inputs during training.
Autoencoders
A neural network that learns to compress data into a small code and then unzip it back to the original.
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.
From vocabulary to outcomes
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