0Pricing
Deep Learning Academy · Lesson

Cross-Entropy for Multiclass

Why it expects raw logits, not softmax.

More Than Two Choices

When your model must pick one label from many, like a digit 0 through 9, you use cross-entropy, the standard multiclass classification loss. 🔢

One Logit Per Class

For C classes, your network outputs a vector of C raw scores called logits, one per possible class, for every sample in the batch.

All lessons in this course

  1. MSE & MAE for Regression
  2. Binary Cross-Entropy with Logits
  3. Cross-Entropy for Multiclass
  4. Class Weights for Imbalanced Data
← Back to Deep Learning Academy