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Section Navigation

  • Terminology
    • Beta Distribution
    • Confidence Level
    • Correlation
    • Critical Value
    • Cumulative Distribution Function (CDF)
    • Frequentist
    • IID (Independent and Identically Distributed)
    • Likelihood
    • Margin of Error (MoE)
    • Mean
    • Median
    • Normal Distribution
    • Outlier
    • Population Proportion
    • Probability
    • Probability Density
    • Probability Distribution
    • Probability Mass
    • Proportion
    • Regression Coefficient
    • Sample Mean
    • Sample Standard Deviation
    • Standard Error (SE)
    • Statistical Significance
    • Statistically Significant
    • True Mean (Population Mean)
    • True Population Parameter
    • Z-Score
    • A Priori Power Analysis
    • Chi-square (χ²) Test
    • Clopper–Pearson Interval
    • Compromise Power Analysis
    • Confidence Intervals (CIs)
    • Effect Size (δ)
    • Hypothesis Testing
    • Kolmogorov–Smirnov (KS) Test
    • Minimum Detectable Lift (MDL)
    • P-Value (probability value)
    • Post Hoc Power Analysis
    • Power (1 – β)
    • Power Analysis
    • Sample size
    • Significance Level (α)
    • Statistical Power
    • Statistical Tests
    • T-Test
    • Trivial Effects
    • Two-Proportion Z-Test
    • Type I Error
    • Wilson Score Interval
    • Z-Test
    • AI (Artificial Intelligence)
    • Classification Models
    • Computer Vision (CV)
    • Decision Trees
    • Linear Models
    • LLMs (Large Language Models)
    • Logistic Regression
    • Machine Learning (ML)
    • Medical AI
    • Natural Language Processing (NLP)
    • Neural Networks
    • Regression Models
    • Support Vector Machines (SVMs)
    • Target Variable
    • Class Weighting
    • Cluster-based undersampling
    • Downsampling
    • NearMiss (Distance-based Undersampling)
    • Oversampling
    • Random Undersampling
    • SMOTE (Synthetic Minority Over-sampling Technique)
    • Subsampling
    • Upsampling
    • Accuracy
    • AUC (Area Under the Curve)
    • Average Precision (AP)
    • Binary Classification
    • Classification Probability
    • Discriminatory Power
    • F1-score
    • Gini Coefficient
    • Harmonic Mean
    • Log Loss (also called Logarithmic Loss or Cross-Entropy Loss)
    • Macro AUC
    • Macro AUROC (Macro-Averaged AUROC)
    • Macro Averaging
    • Macro F1
    • Macro Precision
    • Macro Recall
    • Micro AUC
    • Micro AUROC
    • Micro Averaging
    • Micro F1
    • Micro Precision
    • Micro Recall
    • Model Score
    • Multi-label Classification
    • Multiclass AUROC
    • Multiclass Classification
    • Multiclass Precision
    • Multilabel Precision
    • One-vs-Rest (OvR)
    • One-vs-Rest (OvR) AUROC
    • Partial AUC (pAUC)
    • Per-class Precision (sometimes called class-wise precision)
    • Precision (a.k.a. Positive Predictive Value, PPV)
    • Precision–Recall AUC (PR-AUC)
    • Recall
    • ROC Curve (Receiver Operating Characteristic)
    • ROC-AUC (Receiver Operating Characteristic – Area Under Curve, = AUROC)
    • Single-label Classification
    • Weighted Averaging
    • Average Absolute Error (AAE)
    • Baseline Heuristics
    • Bootstrap
    • Bootstrap Confidence Intervals (CIs)
    • Coverage
    • Cramér’s V
    • DeLong’s Test
    • KS Statistic (Kolmogorov–Smirnov Statistic)
    • Likelihood Ratio (LR)
    • Mann–Whitney U Test (also called the Wilcoxon rank-sum test)
    • MASE (Mean Absolute Scaled Error)
    • Mean Absolute Error (MAE)
    • Mean Absolute Percentage Error (MAPE)
    • Mean Squared Error (MSE)
    • Relative accuracy
    • RMSLE (Root Mean Squared Logarithmic Error)
    • Root Mean Squared Error (RMSE)
    • R² (R-squared)
    • sMAPE (Symmetric Mean Absolute Percentage Error)
    • WAPE (Weighted Absolute Percentage Error)
    • WMAPE (Weighted Mean Absolute Percentage Error)
    • A/B Testing
    • A/B/n Test
    • Bayesian Sequential Testing
    • Bayesian Stopping Rules
    • Conversion Rate Uplift
    • Fixed-Horizon Testing
    • Group Sequential Testing
    • Multivariate Test (MVT)
    • Online Experimentation Platforms
    • Optimizely
    • Risk of Peeking
    • Sequential Testing (also called sequential analysis)
    • Stopping Rules
    • Traditional A/B Test (Fixed-Horizon A/B Test)
    • Treatment Effect
    • True Conversion Rate
    • Blended CAC (Customer Acquisition Cost)
    • CAC (Customer Acquisition Cost)
    • Cannibalization
    • Channel-Specific CAC (Customer Acquisition Cost)
    • Churn
    • Cohort
    • Cohort-Based LTV (Simple Version)
    • Conversion Rate (CR)
    • Cost-Per-Click (CPC) Models
    • Cross-Selling
    • CTR (Click-Through Rate)
    • Customer Lifetime
    • Customer Segmentation
    • D2C (Direct-to-Consumer)
    • FTEs
    • Fully Loaded CAC (Customer Acquisition Cost)
    • Gross LTV (Customer Lifetime Value)
    • Gross Margin
    • KYC
    • Lagging Indicators
    • Lead-Gen Software
    • Leading Indicators
    • LTV (Customer Lifetime Value)
    • LTV:CAC Ratio
    • Net LTV (sometimes called Contribution LTV)
    • OpEx
    • Organic CAC (Customer Acquisition Cost)
    • Paid CAC (Customer Acquisition Cost)
    • Predictive LTV (pLTV)
    • Retention
    • Revenue per User (RPU / ARPU)
    • ROI (Return on Investment)
    • SaaS (Software as a Service)
    • Session Length
    • Upselling
    • Valuation Metric
    • Blocked Splits (Single Holdout)
    • Cross-Validation (CV)
    • Data Leakage
    • Evaluation Set
    • Expanding Window Cross-Validation
    • k-fold cross-validation
    • k-fold Stratified Cross-Validation (Stratified CV)
    • Multiclass stratified CV
    • Sliding Window (Rolling Window) Cross-Validation
    • Stratified Group K-Fold
    • Stratified Shuffle Split
    • Time-based splits (a.k.a. Temporal Cross-Validation, Rolling Window Validation)
    • Active Learning
    • Binary Cross-Entropy (BCE)
    • Deep Ensembles
    • Early Stopping
    • Ensemble
    • Epochs
    • FLOPs
    • Full Annotation
    • Hyperparameter
    • Label Noise
    • Log-Odds
    • Logit Space
    • Logits
    • Loss Functions
    • Model Distillation (Knowledge Distillation)
    • Model Weights
    • Quantization
    • Sigmoid Function
    • Softmax Function
    • Squashing Function
    • Underflow
    • Weak Supervision
    • AWS SageMaker
    • Google Experiments
    • Kaggle
    • ONNX (Open Neural Network Exchange)
    • OpenAI API (ML API)
    • TPU Clusters
    • Vertex AI
    • Backorder Rate
    • Crew Overtime
    • Demand Forecasting
    • Fill Rate
    • Long Lead Times
    • Long-Tail Items
    • Lost Sales Value
    • Overstock %
    • Real-Time Inventory Tracking
    • Reorder Point (ROP) Optimization
    • Safety Stock
    • SKU
    • Slow-Moving SKUs
    • Stockout Rate
    • Stockouts
    • Supplier Constraints
    • Supplier Management
    • Advanced Sorting in Spreadsheets
    • Encode (in Feature Engineering)
    • Normalize (in Feature Engineering)
    • Sensitivity in Feature Engineering
    • Demographic Parity (Statistical Parity)
    • Equal Opportunity (Fairness)
    • Equalized Odds (Fairness)
    • Fairness Guardrails
    • Fairness parity
    • Four-Fifths (80%) Rule
    • Predictive Parity (Calibration)
    • Selection Rate
    • Bayes’ Theorem
    • Bayesian Correction
    • Bayesian Decision Theory (BDT)
    • Bayesian Inference.
    • Bayesian Neural Networks (BNNs)
    • Binomial Likelihood
    • Gaussian Processes (GPs)
    • Marginal Likelihood (also called The Model Evidence or Integrated Likelihood)
    • MCMC (Markov Chain Monte Carlo)
    • Parameter(s) of Interest
    • Posterior
    • Posterior belief
    • Posterior Probability
    • Posterior probability of uplift
    • Prior Belief (or Prior Probability)
    • Variational Inference (VI)
    • Bandit Algorithms
    • O’Brien–Fleming (OBF) Method
    • Pocock Method
    • Sequential Probability Ratio Test (SPRT)
    • Sequential Settings
    • Thompson Sampling (TS) in Bandits (Multi-Armed Bandit Problem (MAB))
    • ARIMA (AutoRegressive Integrated Moving Average)
    • Bayesian Time Series
    • Forecast Error
    • Forecasting Benchmarks
    • Forecasting Competitions
    • Log-Space
    • Low-pass Filtering
    • LSTM — Long Short-Term Memory Networks
    • M-Competitions (Makridakis Competitions)
    • Naïve Baseline Forecast
    • Prophet — Time Series Forecasting by Facebook (Meta)
    • Seasonal Lag
    • Seasonality
    • Signal Processing
    • Simple Baseline Methods
    • Temporal autocorrelation (Serial Correlation)
    • Time Series
    • Time Series Forecasting
    • Windows (in Time-Series)
    • AWS SageMaker Endpoints
    • Caching
    • Cloud Inference
    • Cloud Inference with Big Payloads
    • Compute budgets
    • Continuous Retraining
    • Feature Values
    • Guardrails (in ML & Data Systems)
    • Inference Cost (Inference $)
    • Latency Guardrails
    • Manual review minutes
    • Model KPIs (Key Performance Indicators)
    • Model Stability
    • Monitoring Pipelines
    • Ops Health Dashboard
    • Re-scoring
    • Recalibrate Thresholds
    • Recalibration
    • Reweighting
    • SLA (Service Level Agreement)
    • SLA Breach Rate
    • SLA Breaches
    • SLI (Service Level Indicator)
    • SLOs (Service Level Objectives)
    • Cardinality in Categorical Data
    • Categorical Drift
    • Categorical Explosions
    • Classifier Two-Sample Tests (C2STs)
    • Concept Drift
    • Covariate Drift (a.k.a. Covariate Shift)
    • Data Drift
    • Dataset Shift
    • Drift Detection
    • Drift Guardrails
    • Energy Distance
    • Jensen–Shannon (JS) Divergence
    • KS shift (Kolmogorov–Smirnov shift)
    • Kullback–Leibler (KL) Divergence
    • Label Drift (a.k.a. Target Drift)
    • Macro Shifts
    • Maximum Mean Discrepancy (MMD)
    • Off-Distribution
    • PSI (Population Stability Index)
    • Representation Shift
    • Autoencoder
    • Embedding
    • Embedding Similarity
    • Frozen Encoder
    • Balanced Interleaving
    • DCG (Discounted Cumulative Gain)
    • Interleaving Tests
    • Mean Average Precision (MAP)
    • NDCG (Normalized Discounted Cumulative Gain)
    • Probabilistic Interleaving
    • Ranking Algorithms
    • Team Draft Interleaving (TDI)
    • TREC (Text REtrieval Conference)
    • AUUC (Area Under the Uplift Curve)
    • Causal Effect
    • Causal Impact
    • Causal Inference
    • Causal ML (Causal Machine Learning)
    • Causal Trees
    • Cumulative Incremental Gain (CIG)
    • Cumulative Uplift
    • Incremental Conversions
    • Incremental Gain
    • Incremental Recovery Rate (IRR)
    • Incremental Revenue
    • Incremental Sales
    • Qini Coefficient
    • Qini Curve
    • Random Targeting Strategy
    • Revenue net of treatment cost
    • Total Incremental Benefit (TIB)
    • Treatment Cost
    • Uplift
    • Uplift Curve
    • Uplift Models
    • Uplift Random Forests
    • Uplift Score
    • Uplift@k
    • Continuous Probabilistic Forecasts
    • Continuous Ranked Probability Score (CRPS)
    • Deterministic forecasts
    • Full Distribution
    • Pinball Loss (a.k.a. Quantile Loss)
    • Point Forecasts
    • Predicting Percentiles
    • Prediction Intervals (PI)
    • Probabilistic Forecasts
    • Probabilistic Scoring
    • Probability Forecasts
    • Quantile Forecasts
    • Quantile Level
    • Quantile Regression
    • Return Distribution
    • Risk Forecast
    • Risk-Based Decisions
    • Strictly Proper Scoring Rules
    • Value-at-Risk (VaR)
    • Catalog Coverage
    • Cosine Similarity of Item Features
    • Diminishing Utility
    • Diversity (in Recommender Systems)
    • Dominating in Recommender Systems
    • Genre Overlap
    • Hit Rate (HR)
    • Intra-List Diversity (ILD)
    • Item Coverage
    • Jaccard index
    • Novelty (in Recommender Systems)
    • Relevance in Recommender Systems
    • Self-Information of Popularity
    • User Coverage
    • Adaptive ECE (Expected Calibration Error with Adaptive Binning)
    • Brier Score
    • Calibration quality (Model Calibration)
    • Expected Calibration Error (ECE)
    • Isotonic Regression
    • Maximum Calibration Error (MCE)
    • Murphy’s Decomposition
    • Overconfident
    • Platt Scaling
    • Reliability Curves (also called Calibration Curves)
    • Temperature Scaling
    • Underconfident
    • Basel III
    • Counterfactual Explanations
    • Fair Lending laws
    • High-Stakes Domains
    • LIME (Local Interpretable Model-agnostic Explanations)
    • Post-hoc Explainability
    • SHAP (SHapley Additive exPlanations)
  • External Resources
    • Data Resources
    • Documentation Resources
    • Plot Resources
    • Model Resources
    • Research Resources
    • Youtube Resources
  • Data Analytics
    • 🌱 Foundations
    • 🎯 Data-Driven Decisions
    • 📦 Data Preparation
    • 🧽 Data Cleaning & Preparation
    • 📊 Analyze Data
    • 🎨 Data Visualization
    • 🐍 Data Analysis Using Python
    • 💼 Job Search
  • Data Preparation & Analysis
    • Why Do We Analyze Data?
    • The Process of Data Analysis
    • CRISP-DM for Data Science
    • Big Data: Definition, Characteristics, Evolution, and Business Impact
    • The First Step in Knowing Your Data
    • IEEE 754 Floating-Point Standard
    • Discovering Associations Through Data: From Everyday Patterns to Chicago Taxi Trips (September 2022)
    • Taxi Trips – 2022 dataset from the City of Chicago open data portal
    • Objective Selection of the Bin Width for a Time Histogram
    • Measuring Associations in Data
    • Measuring Associations Between Two Continuous Variables
    • Correlation Coefficients in Python (Pearson, Spearman, Kendall)
    • Karl Pearson
    • Harald Cramér
    • What Are Statistical Tests?
    • Eta Squared (η²): Effect Size in ANOVA
    • Understanding Market Baskets and Ideal Customers
    • What Can Association Rules Tell Us?
    • How Association Rules Are Discovered: Concepts, Scale, Measures, and the Apriori Approach
    • Apriori: Frequent Itemsets via the Apriori Algorithm
    • association_rules: Generating Association Rules from Frequent Itemsets (mlxtend)
    • Cross-Selling
    • Stratified Random Sampling
    • Linear Congruential Random Number Generator (LCG)
    • Partitioning Observations to Train Objective Models
    • Putting Similar Observations into Clusters
    • Clustering
    • Recency, Frequency, and Monetary Value (RFM)
    • RFM Analysis
    • Creating Segments of Observations for Business Reasons (RFM)
    • Least Squares Regression
    • Multiple Linear Regression
    • Feature Importance in Linear Regression
    • Forward Selection: Definition and Core Idea
    • Forward Selection and Model Interpretation in Linear Regression
    • Understanding Forward and Backward Stepwise Regression
    • How Shapley Values Work
    • Logistic Regression: Modeling Binary Outcomes via Odds and Log-Odds
    • Maximum Likelihood (MLE): Fitting a Distribution to Observed Data
    • Assessing Model Fit in Logistic Regression
    • Complete and Quasi-Complete Separation in Logistic Regression
    • Forward Selection with Nested Models and Deviance Tests
    • Interpreting and Assessing a Forward-Selection Logistic Regression Model for College Student Retention
    • Motivation of Decision Trees: An Incremental Model of Decision-Making
    • The CART Algorithm
    • Decision Trees as Piecewise Models and Their Predictive Structure
    • How CART Decision Trees Model Interactions
    • Cluster Profiling Using Decision Trees
    • Using Decision Trees to Explain Clustering Results
    • Assessing the Quality of Prediction Models
    • Binary Classification Models – Conceptual Framework and Evaluation Metrics
    • Nominal Classification Models: Model State and Evaluation Metrics
    • Binary Classification Model Evaluation and Threshold Optimization
    • Identifying Outliers Using Residuals and Studentized Residuals
    • AUC–ROC Curve: Evaluating Classification Model Performance
    • Lift Analysis for Direct Mail Campaigns: Concept, Process, and Business Value
  • Bayesian Data Analysis
    • The three steps of Bayesian data analysis
    • General Notation for Statistical Inference
    • Bayesian Inference
    • Discrete Bayesian Examples – Genetics and Spell Checking (with θ)
    • Probability as a Measure of Uncertainty
    • Example — Probabilities from Football Point Spreads
    • Example — Calibration for Record Linkage
    • Some Useful Results from Probability Theory
    • Computation and Software
    • Bayesian Inference in Applied Statistics
    • Estimating a Probability from Binomial Data
    • Posterior as a Compromise Between Data and Prior Information
    • Summarizing Posterior Inference
    • Informative Prior Distributions
    • Normal Distribution with Known Variance
    • Other Standard Single-Parameter Models
    • Informative Prior Distribution for Cancer Rates
    • Noninformative Prior Distributions
    • Weakly Informative Prior Distributions
    • Averaging Over Nuisance Parameters
    • Normal Data with a Noninformative Prior Distribution
    • Normal Data with a Conjugate Prior Distribution
    • Multinomial Model for Categorical Data
    • Multivariate Normal Model with Known Variance
    • Multivariate Normal with Unknown Mean and Variance
    • Example: Bayesian analysis of a bioassay experiment (logistic, nonconjugate)
    • Summary of Elementary Modeling and Computation
    • Normal Approximations to the Posterior Distribution
    • Large-Sample Theory
    • Counterexamples to large-sample (asymptotic) Bayesian theorems
    • Frequency Evaluations of Bayesian Inferences
    • Bayesian interpretations of other statistical methods
    • Constructing a Parameterized Prior Distribution
    • Exchangeability and hierarchical models
    • Bayesian analysis of conjugate hierarchical models
    • Normal model with exchangeable parameters
    • Example: parallel experiments in eight schools
    • Hierarchical modeling applied to a meta-analysis
    • Weakly Informative Priors for Variance Parameters
    • The Place of Model Checking in Applied Bayesian Statistics
    • Do the Inferences from the Model Make Sense?
    • Posterior predictive checking
    • Graphical posterior predictive checks
    • Model checking for the educational testing example
    • Measures of predictive accuracy
    • Model comparison based on predictive performance
    • Model comparison using Bayes factors
    • Continuous model expansion
    • Implicit assumptions and model expansion: an example
    • Bayesian inference requires a model for data collection
    • Data-collection models and ignorability
    • Sample surveys
    • Designed experiments
    • Sensitivity and the role of randomization
    • Observational studies
    • Censoring and truncation
    • Bayesian decision theory in different contexts
    • Using regression predictions: survey incentives
    • Multistage decision making: medical screening
    • Hierarchical decision analysis for home radon
    • Personal vs. institutional decision analysis
    • Numerical integration
    • Distributional approximations
    • Direct simulation and rejection sampling
    • Importance sampling
    • How many simulation draws are needed?
    • Computing environments
    • Debugging Bayesian computing
    • Gibbs sampler
    • Metropolis and Metropolis-Hastings algorithms
    • Using Gibbs and Metropolis as building blocks
    • Inference and assessing convergence
    • Effective number of simulation draws
    • Example: hierarchical normal model
    • Efficient Gibbs samplers
    • Efficient Metropolis jumping rules
    • Further extensions to Gibbs and Metropolis
    • Hamiltonian Monte Carlo
    • Hamiltonian Monte Carlo for a hierarchical model
    • Stan: developing a computing environment
    • Finding posterior modes
    • Boundary-avoiding priors for modal summaries
    • Normal and related mixture approximations
    • Finding marginal posterior modes using EM
    • Conditional and marginal posterior approximations
    • Example: hierarchical normal model (continued)
    • Variational inference
    • Expectation propagation
    • Other approximations
    • Unknown normalizing factors
    • Conditional modeling
    • Bayesian analysis of classical regression
    • Regression for causal inference: incumbency and voting
    • Goals of regression analysis
    • Assembling the matrix of explanatory variables
    • Regularization and dimension reduction
    • Unequal variances and correlations
    • Including numerical prior information
    • Regression coefficients exchangeable in batches
    • Example: forecasting U.S. presidential elections
    • Interpreting a normal prior distribution as extra data
    • Varying intercepts and slopes
    • Computation: batching and transformation
    • Analysis of variance and the batching of coefficients
    • Hierarchical models for batches of variance components
    • Standard generalized linear model likelihoods
    • Working with generalized linear models
    • Weakly informative priors for logistic regression
    • Overdispersed Poisson regression for police stops
    • State-level opinons from national polls
    • Models for multivariate and multinomial responses
    • Loglinear models for multivariate discrete data
    • Aspects of robustness
    • Overdispersed versions of standard models
    • Posterior inference and computation
    • Robust inference for the eight schools
    • Robust regression using t-distributed errors
    • Notation
    • Multiple imputation
    • Missing data in the multivariate normal and t models
    • Example: multiple imputation for a series of polls
    • Missing values with counted data
    • Example: an opinion poll in Slovenia
    • Example: serial dilution assay
    • Example: population toxicokinetics
    • Splines and weighted sums of basis functions
    • Basis selection and shrinkage of coefficients
    • Non-normal models and regression surfaces
    • Gaussian process regression
    • Example: birthdays and birthdates
    • Latent Gaussian process models
    • Functional data analysis
    • Density estimation and regression
    • Setting up and interpreting mixture models
    • Example: reaction times and schizophrenia
    • Label switching and posterior computation
    • Unspecified number of mixture components
    • Mixture models for classification and regression
    • Bayesian histograms
    • Dirichlet process prior distributions
    • Dirichlet process mixtures
    • Beyond density estimation
    • Hierarchical dependence
    • Density regression
  • Time Series
    • What Are Time Series, and How Are They Used?
    • Getting Started with R
    • A Gentle Introduction to Stationarity
    • Weak and Strong Stationarity
    • Linear Processes
    • Understanding ARMA Processes
    • Computing ACFs of Causal AR(2) Processes Using Difference Equations
    • Understanding ACFs via Difference Equations for AR(p) and ARMA(p, q)
    • Best Linear Predictor of a Stationary Process
    • Sample ACF and Sample PACF
    • Preliminary Estimation for AR Models and the Yule–Walker Equations
    • Maximum Likelihood Estimation for ARMA Models (Gaussian MLE)
    • Diagnostics After Fitting a Time Series Model
    • Order Selection for Time Series Models
    • ARIMA Models: How Nonstationary Models Are Built from Stationary Ones
    • SARIMA Models: Seasonal ARIMA
    • Beyond One-Step Ahead Predictions
    • Exponential Smoothing Models
  • Deep Learning
    • What is a Neural Network?
    • Supervised Learning and Neural Networks
    • Why Deep Learning is Taking Off
    • Geoffrey Hinton Interview
    • Binary Classification and Logistic Regression (Neural Network Basics)
    • Logistic Regression (Binary Classification Model)
    • Logistic Regression – Loss Function and Cost Function
    • Gradient Descent in Logistic Regression
    • Derivatives
    • More Derivative Examples
    • Computation Graph
    • Derivatives with a Computation Graph
    • Logistic Regression Gradient Descent
    • Gradient Descent on m Training Examples
    • Vectorization in Logistic Regression
    • More Vectorization Examples
    • Vectorizing Logistic Regression
  • Hands-On Materials
    • BilimEdtech Labs
  • Cheatsheet
    • Md Cheatsheet
    • RST Cheatsheet
  • Glossary
    • https://scikit-learn.org/stable/glossary.html
    • https://ml-cheatsheet.readthedocs.io/en/latest/glossary.html
  • ☀️ Learning Hub • ✨ AI-Powered
  • Hands-On (Practice) Materials
  • Welcome to BilimEdtech Labs!
  • Operating Systems
  • 2. Simple IO in C

2. Simple IO in C#

See the instructions for Lab 2 in Google Docs.

Source & license

Reproduced verbatim, without modification from © 2022, BilimEdtech Labs, licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Source page: https://labs.bilimedtech.com/operating-systems/2/index.html

See LICENSE for the full license text.

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