← 📊 Evaluation & ML FoundationsNEXT IN EVALUATION & ML FOUNDATIONSProbability Distributions You Should Know→
Core
Information Theory for ML
Information theory gives ML its core measures: entropy (uncertainty in a distribution), cross-entropy (the cost of modeling the true distribution with your predicted one, the classification loss), KL divergence (how far one distribution is from another), and mutual information (how much one variable tells you about another). These appear as the loss you minimize, the regularizer in VAEs and RLHF, and the splitting criterion in decision trees. Applied-AI interviews probe it because cross-entropy and KL underlie 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