What is overfitting in the context of training CNNs?

Practice Questions

Q1
What is overfitting in the context of training CNNs?
  1. When the model performs well on training data but poorly on unseen data
  2. When the model is too simple to capture the underlying patterns
  3. When the model has too few parameters
  4. When the model is trained on too much data

Questions & Step-by-Step Solutions

What is overfitting in the context of training CNNs?
  • Step 1: Understand that a CNN (Convolutional Neural Network) is a type of model used for tasks like image recognition.
  • Step 2: When training a CNN, we use a set of data called training data to teach the model.
  • Step 3: The goal is for the model to learn the important patterns in the training data.
  • Step 4: Overfitting happens when the model learns the training data too well, including its noise and specific details.
  • Step 5: As a result, the model performs very well on the training data but fails to generalize to new, unseen data.
  • Step 6: This means the model is not truly learning the underlying patterns, but rather memorizing the training examples.
No concepts available.
Soulshift Feedback ×

On a scale of 0–10, how likely are you to recommend The Soulshift Academy?

Not likely Very likely