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Deep Learning Academy · Lesson

Loss as a Landscape to Descend

Picture error as hills and valleys.

What Loss Measures

Your model's loss is a single number that says how wrong its predictions are. Smaller loss means better predictions. Training is just the quest to shrink it. 📉

Loss Depends on Weights

The same data gives different loss depending on your weights. Change a weight and the loss moves up or down. So loss is really a function of the weights.

loss = compute_loss(weights, data)

All lessons in this course

  1. Loss as a Landscape to Descend
  2. Gradients Point Uphill — So Step the Other Way
  3. Learning Rate: Too Big, Too Small, Just Right
  4. Minimize a Function by Hand in Python
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