Experiment Here: https://www.gloqo.ai/visuals/cross_entropy_loss/
Prerequisites:
1. Basic Probability: Understanding likelihoods as values between 0 and 1 that sum up to 1.
2. Logarithms: Knowing how natural logs (log(x)) behave, especially when x is between 0 and 1.
3. Classification Basics: Familiarity with training models using true labels versus predicted outputs.
4. One-Hot Encoding: Representing true target categories as vectors of 1s and 0s.
5. Softmax Function: Transforming raw network scores (logits) into a clean probability distribution.
6. np.clip(): Forcing prediction values into a safe range to avoid log(0) calculation errors.
7. np.sum(axis=...): Aggregating probabilities or total losses across specific rows or columns in a matrix.
#crossentropy #machinelearning #deeplearning #datascience #neuralnetworks #pythonprogramming #numpy #ai #coding #neetcode #deeplearning #education #maths
