Cheat Sheets
At-a-glance one-pagers that distill dense AI & ML topics into scannable visuals — formulas, decision trees, and quick-reference tables you can keep open while you build.
Core Machine Learning
Problem framing, generalization, classic supervised and unsupervised models, features, validation, and leakage.
Open sheetData Structures and Algorithm Patterns
Complexity, core patterns with Python templates, graphs, dynamic programming, and the cues that point to each pattern.
Open sheetGenerative AI
LLM fundamentals, transformers, decoding, RAG, agents, fine-tuning, inference cost, evaluation, and production safety.
Open sheetJava Syntax
A comprehensive reference for Java developers: core syntax, data structures, and standard library.
Open sheetMath for Machine Learning
Linear algebra, calculus, optimization, probability, information theory, and the numerics that keep models stable.
Open sheetModel Evaluation Metrics
Confusion-matrix metrics, ROC and PR curves, calibration, thresholds, multiclass averaging, regression, ranking, and reporting.
Open sheetPrompt Engineering
Prompt structure, examples, reasoning, tools, retrieval, structured output, injection defense, evaluation, and production habits.
Open sheetPython for Data Science
NumPy, pandas, cleaning, reshaping, time series, plotting, scikit-learn pipelines, and performance habits.
Open sheetPyTorch and TensorFlow
Tensors, models, losses, data pipelines, training loops, optimizers, mixed precision, checkpoints, and debugging.
Open sheetSQL
Query order, joins, aggregation, window functions, CTEs, dates, NULL semantics, analytics patterns, and query safety.
Open sheetStatistics and A/B Testing
Hypothesis tests, confidence intervals, power, sample size, variance reduction, and the traps that break experiments.
Open sheetSystem Design
Requirements, estimation, APIs, caching, databases, replication, sharding, queues, consistency, reliability, and security.
Open sheet