AI Systems · core
Softmax & Cross-Entropy
Logits to probabilities, negative log-likelihood loss.
Mental model
Softmax turns raw scores (logits) into probabilities that sum to 1. Cross-entropy then measures how wrong those probabilities are versus the true answer. Together they give a clean gradient that backprop can work with.
How to study Softmax & Cross-Entropy
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use CS231n — Linear Classification: SVM vs Softmax, cross-entropy loss, Neural Networks: Zero to Hero (Karpathy), Softmax function (Wikipedia) to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Softmax & Cross-Entropy with Language Modeling. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Softmax and cross-entropy loss and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Learn from primary sources
Practice and explain it back
Softmax and cross-entropy loss
Logits [2,1,0.1], true class index 0. Compute softmax probabilities and −log p(true).
Expected evidence: p≈[0.659,0.242,0.099], loss≈0.417.
Open the interactive drill →Review prompts
- Why are softmax and cross-entropy implemented as one fused op rather than two?
Build evidence
Synthesize: AI Models & Training
Move from transformer foundations through pre-training, fine-tuning, post-training, compression, and evaluation. Produce one working system, benchmark, or evidence-backed design that integrates the path.
- Implements or precisely models the core mechanisms from all three milestones
- Includes at least one injected failure or adversarial case and demonstrates recovery
- Reports quality, latency, resource, reliability, or usability measurements relevant to the domain
- Ships a concise architecture note explaining decisions, trade-offs, and remaining risks