Concepts starting with O

131 concepts, each with a plain-language explanation, examples and diagrams. Pick a letter or open a concept.

OAuth 2.0 Authorization Code Flow — OAuth libraries are free, pre-built tools that handle the complexity of the OAuth 2.0 protocol so you do not have to implement…
OAuth and Token-Based Authentication — API keys are the mechanism vendors use to identify users and monitor their service consumption.
Object Attributes and Methods — Objects have a complete lifecycle: creation (via __init__), active use, and destruction (via __del__). Python calls these…
Object Equality and the equals() Method — A record declaration with only fields causes the compiler to add a canonical constructor, accessor methods, and definitions of…
Object Initialization and Cleanup — Special methods enable custom classes to mimic built-in type behaviors through double-underscore naming conventions.
Object Initialization and Constructors — A constructor has the same name as its class, has no return type, and initializes an object when it is created.
Object Instantiation and Class Calls — Class methods have only one specific difference from ordinary functions - they must have an extra first name that has to be…
Object References and Variables — Instance variables of an object are reached with dot notation, written object_name.variable_name, following the rule that when B…
Object Types and Class Names — ArrayList<T> is a parameterized type: a single class ArrayList with a type parameter slot that can be filled to form many…
Object-Oriented Design Principles — Fields are ordinary variables bound to class or object namespaces, scoped to their context and not global.
Object-oriented programming — A programming paradigm where the program is constructed around objects that integrate data and functionality.
Object-Oriented Programming (OOP) — Object-oriented programming models a problem as a set of objects instead of a sequence of tasks, so the program's structure…
Object-Oriented Programming Basics — Procedure-oriented programming organizes code around reusable functions; object-oriented programming organizes code around…
Object-Oriented Programming Fundamentals — Procedure-oriented programming organizes code around reusable functions; object-oriented programming organizes code around…
Object-Oriented Programming in Java — User interactions with GUI components generate events.
Object-Oriented Programming Principles — Procedure-oriented programming organizes code around reusable functions; object-oriented programming organizes code around…
ObjectInputStream and ObjectOutputStream — Serializable is a marker interface: it declares no methods and exists only to indicate that an object may be written and read.
Objects and Class Fields in Java — Declaring Student[] classlist and creating it with new Student[30] makes an array of references, not thirty Student objects.
Objects and Messages — Polymorphism concerns different responses to one shared message.
Objects and Methods — Abstraction is the ability to hide complexity so you can focus on what matters to you and ignore the rest.
Objects and Their Classes — Classes have a dual role: they can contain static members and describe objects.
Obligations for High-Risk AI Systems — The EU AI Act is Regulation 2024/1689, in force since August 1, 2024, with staged obligations through 2026 and 2027.
Off-policy Every-Visit Monte Carlo Policy Evaluation — The weighted average update rule incorporates a newly obtained return into an existing estimate.
Off-policy learning — Semi-gradient off-policy TD(0) extends the corresponding on-policy TD(0) algorithm by adding ρ_t.
Off-policy Learning with n-step Tree Backup — Expected action value is defined as a scalar random variable under the target policy.
Off-Policy Methods — Function approximation introduces issues including nonstationarity, bootstrapping, and delayed targets.
Off-policy Prediction with Importance Sampling — Importance sampling lets off-policy methods use behavior-policy samples to estimate quantities associated with a target policy.
Off-policy TD control — Q-learning is an off-policy TD control algorithm in reinforcement learning.
Off-screen Canvas Usage with Graphics2D — A custom Swing component can be defined as a subclass of JPanel, which supplies its layout, size handling, and inherited colors…
Off-Screen Drawing and Buffers — To a computer, an image is a set of numbers, with each number specifying the color of one pixel.
Offboarding and Identity Lifecycle Management — Role-based access control restricts what a clinician can reach according to their role, and in this implementation it was…
On-Policy and Off-Policy Control — Sarsa learns action values one transition at a time.
On-Policy and Off-Policy Learning — Exploration is necessary because the currently estimated best action may not be truly best.
On-policy and off-policy reinforcement learning — Expected Sarsa replaces Sarsa's single sampled next action with an expected next-action value.
On-Policy Control with Sarsa — Sarsa controls the acrobot through repeated online interaction rather than through access to a simulator model.
On-policy Monte Carlo Control Methods — ε-greedy balances preference for the current best-known action with random action selection.
On-policy Monte Carlo Policy Evaluation — Off-policy every-visit Monte Carlo evaluation separates data collection from policy evaluation.
On-policy Prediction — Off-policy learning separates the policy that generates data from the policy being evaluated.
On-Policy Prediction and Control — Function approximation introduces issues including nonstationarity, bootstrapping, and delayed targets.
On-Policy Prediction with Function Approximation — Function approximation introduces distinctive challenges into reinforcement learning.
On-Policy Reinforcement Learning — Sarsa control estimates the action values of its current behavior policy.
On-Policy Temporal-Difference Control — Sarsa convergence is tied to the policy's dependence on Q.
One-Dimensional Arrays in Java — Java does not have true two-dimensional arrays. A two-dimensional array is an array whose elements are one-dimensional arrays.
One-Hot Encoding — A word embedding is a dense, low-dimensional floating-point vector learned from data.
One-Hot Encoding for Tokens — The embedding layer is a lookup operation: a token ID selects a row of the weight matrix by position.
One-Step Tabular Q-Learning — Planning uses model-generated experience, while learning uses experience from the real environment.
one-step TD backup — The λ-return is a normalized weighted average of n-step backups.
One-step Temporal-Difference Learning — λ identifies important endpoint cases of the λ-return.
One-Step-Ahead Search — q* turns optimal action selection into comparing values for the current state's available actions.
One-to-Many and Many-to-Many Relationships — Primary and foreign keys are omitted from simplified data model diagrams because their placement follows an absolute…
One-to-Many Relationships — Crow's Foot Diagrams use boxes to represent tables and lines with notation symbols to represent relationships between tables.
One-to-Many Relationships: Linking Tables with Foreign Keys — A junction table is an intermediate table that resolves many-to-many relationships by breaking them into two one-to-many…
One-to-Many Relationships: The Foundation of Relational Databases — A many-to-many relationship exists when records on both sides can connect to multiple records on the opposite side, unlike…
One-versus-All Multiclass Classification — One-versus-All represents a multiclass hypothesis using one binary hypothesis for each label.
One-versus-All Reduction — General multiclass-to-binary reductions build multiclass predictors from several binary classifiers.
Online Binary Classification — PAC learning separates training from prediction, whereas online learning does not.
Online Classification — An online convex optimization problem is defined by a convex hypothesis class, a domain, and a loss function convex in its first…
Online Gradient Descent — The Perceptron can be placed inside the Online Gradient Descent framework.
Online Learnability — Online learning proceeds round by round, with the environment choosing instances and revealing labels after the learner predicts.
Online Learning — An online learner can be converted into a PAC learner by running it on T independently sampled examples.
Online Learning Algorithms — The realizable case assumes that labels come from a hypothesis h⋆ in a known hypothesis class H.
Online Learning Fundamentals — An ERM hypothesis is any hypothesis consistent with all past examples.
Online Learning Game — Online learning proceeds round by round, with the environment choosing instances and revealing labels after the learner predicts.
Online Learning Round — Online learning has no separate training phase followed by a separate prediction phase.
Online λ-Return Algorithm — The general update is a defining part of the online λ-return algorithm.
Opening and Closing Files in Python — The pickle module converts Python objects into byte streams for storage and retrieves them back into memory.
Opening and Closing Files Safely — The file handle does not contain file data; it is a connection to the file on disk that the for loop uses to read data…
Opening and Closing Files Safely with the with Statement — read() loads an entire file into a single string, including all newline characters, and exhausts the file handle resource after…
Opening and Reading Files — The file search pattern reads through a file line by line, applies a filter condition using string methods, and processes only…
Opening and Reading Files in Python — Hardcoding file names in your code limits reusability; you have to edit the code every time you want to process a different file.
Opening Files and Getting File Handles — Attempting to open a non-existent file raises a FileNotFoundError and prevents the program from obtaining a file handle.
Opening Files for Reading: Mode 'r' — Write mode ('w') opens a file for writing and immediately truncates (erases) any existing content in the file.
Opening Files in Write Mode — The write() method puts data into a file exactly as provided and returns the character count written.
Opening the Python Interpreter Prompt — To verify, open the terminal by opening the Terminal application or by pressing Alt + F2 and entering gnome-terminal . If…
Operations on Strings — Substring extraction selects a sequence of characters from a string.
Operator Overloading — Special methods are methods with double underscores that have special significance in Python classes. They are called…
Operator Overloading Fundamentals — Special methods enable custom classes to mimic built-in type behaviors through double-underscore naming conventions.
Operator Precedence — Python provides five basic arithmetic operators: +, -, *, /, and **
Operator Precedence and Associativity — If you had an expression such as 2 + 3 * 4 , is the addition done first or the multiplication? Our high school…
Operator Precedence and Expressions — Most statements (logical lines) that you write will contain expressions . A simple example of an expression is 2 + 3 . An…
Operator Precedence and Parentheses — If you had an expression such as 2 + 3 * 4 , is the addition done first or the multiplication? Our high school…
Operator Precedence and PEMDAS — When operators have equal precedence, they are evaluated from left to right, not right to left.
Operator Precedence in Complex Programs — Python follows mathematical convention for operator precedence, not left-to-right evaluation
Operator Precedence in Python — If you had an expression such as 2 + 3 * 4 , is the addition done first or the multiplication? Our high school…
Operators and Expressions — Most statements (logical lines) that you write will contain expressions . A simple example of an expression is 2 + 3 . An…
Operators and Operands: Performing Computations — In Python 3, the / operator always returns a float, even when dividing two integers—a significant change from Python 2.
Operators and Their Precedence — An expression is a combination of values, variables, and operators, or a single value or variable by itself.
Opponent Models in Sequential Decision Problems — The simplicity of Tic-Tac-Toe does not remove the need to match an algorithm to the opponent information available.
Optimal Control Methods — Optimization unpredictability is one part of the wider challenge of responsible reinforcement learning deployment.
Optimal Decision Making in Finite Markov Decision Processes — Policy iteration makes progress through a repeating evaluation-improvement cycle.
Optimal Policies and Value Functions — Evaluation and improvement impose different requirements on a policy-value pair.
Optimal Policies in Reinforcement Learning — An optimal value function evaluates the best achievable outcome from a state.
Optimal Policy — Policy evaluation measures a given policy by assigning expected returns to states.
Optimal Policy Search — Dynamic programming solves MDPs with a worst-case operation count bounded by some polynomial function of the number of states…
Optimal Value Functions — Optimality describes the highest-value policy, not the computational effort required to find it.
Optimistic Initial Values for Exploration — Initial estimates affect action-value methods because they provide the starting values used before experience is available.
Optimization Algorithms — Data preparation turns raw, heterogeneous information into scaled and appropriately formatted tensors.
Optimization and Parameter Learning — The hypothesis space is the set of possible transformations or solutions available to a learning algorithm.
Optimization Methods for Reinforcement Learning — Global and local optima differ according to how widely the candidate weight vector is compared.
Optimizers and Learning Rate — A Keras callback is an object passed to fit that is called at various points during training.
Optimizing Database Queries for Large Datasets — Three Python scripts (gbasic.py, gword.py, gline.py) each answer a different question about email participation: who sent the…
Order of Operations — At this point, the most likely cause of a semantic error is the order of operations. For example, to evaluate 1 / 2 π , you…
Order of Operations in Complex Expressions — Operators are special symbols representing computations; operands are the values they act upon.
Ordinary Importance Sampling — The behavior policy and target policy may generate and evaluate different action choices.
Organizational Physics and Change Requirements — The top-five-priorities filter asks whether a proposed AI problem is among the customer's top five priorities. If it is not, the…
Organizing code with functions — A module is a .py file containing functions and variables designed to be reused by other programs.
Organizing Code with Functions and Modules — A Python script is a text file containing Python code, saved with the .py extension, that the interpreter executes from top to…
Organizing Code with Objects: An Introduction to Object-Oriented Programming — All programs are built from four fundamental patterns: sequential code (executing instructions in order), conditional code…
Organizing Code with Packages — Every Python program is a module; creating a module requires only saving a file with the .py extension.
Organizing Data into Training Batches — The dataset is split into training, validation, and test portions before the PyTorch data loaders and batches are built…
Organizing Instruction Data in Training Batches — Preparing a dataset is a step within supervised instruction fine-tuning.
Organizing Your Python Projects — A Python source file must be saved with the .py extension (for example, hello.py) so that the operating system and Python…
Orthogonal Matching Pursuit — Greedy selection evaluates feature subsets through the learning algorithm rather than separating selection from learning.
Other List Methods: insert, remove, pop — append adds a single element to the end of a list, treating the argument as one indivisible object
Other Math Module Functions: Trigonometry and Logarithms — math.pi is a variable from the math module that provides an approximation of π accurate to about 15 digits; use it without…
Outcome Devaluation — Instrumental conditioning established lever pressing as a behavior that produced sucrose pellets.
Outcome Pricing for AI Agents — A well-designed contract aligns the vendor's revenue with the customer's outcome and turns the engagement into a flywheel rather…
Outcome-Based Pricing Terms — Value-share pricing is a structure in which the customer pays a fraction of a measured business outcome, such as cost savings…
Output Layers in Neural Networks — Classification fine-tuning replaces the output layer of a large language model with a smaller classification layer.
Overfitting — Data augmentation applies transformations to existing data.
Overfitting and Generalization — Finite hypothesis classes provide a controlled candidate set for ERM.
Overfitting and Model Generalization — JPEG images must be read, decoded, converted into floating-point tensors, and rescaled before entering the network.
Overfitting and Model Selection — SRM selects a hypothesis by minimizing empirical loss plus a penalty term.
Overfitting and Training Length — Fine-tuning adjusts a pre-trained LLM to a specific task, here converting active sentences to passive voice, using a single…
Overfitting and Underfitting — Overfitting is reduced by making memorization less easy and generalizable representations more useful.
Overfitting and Validation Loss — The number of epochs is the number of full passes through the training dataset, and it is a hyperparameter that depends on the…
Overfitting in Deep Learning — Data augmentation creates varied training examples from existing images.
Overloading Subroutines — The signature of a subroutine does not include its return type.
Overriding Standard Procurement Practices — The contract is the architectural expression of the relationship the FDE built during scoping, so its structure should reflect…
Overview of Instruction Fine-Tuning — Instruction fine-tuning adapts a pretrained LLM to follow human instructions and generate desired responses.
Overview of Large Language Models — The transformer architecture is a specific design from which Large Language Models are derived.