← 📊 Evaluation & ML FoundationsNEXT IN EVALUATION & ML FOUNDATIONSProbability Distributions You Should Know→
Core
Information Theory for ML
ML rests on four information-theoretic quantities: entropy (how uncertain a distribution is), cross-entropy (the cost of modeling the true distribution with your predicted one, the classification loss), KL divergence (the gap between two distributions), and mutual information (how much one variable reveals about another). You meet them as the loss you minimize, the regularizer inside VAEs and RLHF, and the split criterion in decision trees. AI, ML, and GenAI engineer interviews test this because cross-entropy and KL sit under training, distillation, and alignment.
a free account unlocks the core curriculum tier · no card
RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
Machine Learning & Data ScienceExplain entropy, cross-entropy, KL divergence, and mutual information.→LLM & GenAI FundamentalsWhat is perplexity, and what are its limits as a language-model metric?→Machine Learning & Data ScienceYour churn model's AUC jumps from 0.71 to 0.93 after adding a 7-day rolling feature. What now?→Coding & DSAImplement a numerically stable softmax and cross-entropy loss from scratch.→Machine Learning & Data ScienceDefine precision, recall, F1, and AUC, and give a case where each (and accuracy) is misleading.→Machine Learning & Data ScienceHow do you handle a severely imbalanced dataset, and what are the tradeoffs of each technique?→
COMPANIES THAT ASSUME THIS