Common Row Terminology – SolveForce Fiber Internet, Cloud Computing & Telecommunications

5G: The fifth-generation mobile network technology, designed to deliver higher speed, lower latency, and increased capacity compared to previous generations of mobile networks, and to support the growth of IoT and other data-intensive applications.
A/B Testing: A statistical technique used in data science and marketing to compare the performance of two or more variants of a product, treatment, or strategy, by randomly dividing a target audience into two or more groups, and exposing each group to a different variant, and measuring and comparing the outcomes or metrics.
ACID: ACID stands for Atomicity, Consistency, Isolation, and Durability, and refers to a set of properties that ensure that database transactions are processed reliably. Atomicity ensures that a transaction is treated as a single, indivisible unit of work, either completing in its entirety or rolling back to its original state. Consistency ensures that the transaction brings the database from one valid state to another. Isolation ensures that concurrent transactions do not interfere with each other. Durability ensures that once a transaction is committed, its effects persist, even in the face of hardware failure or other problems.
ACID: ACID stands for Atomicity, Consistency, Isolation, and Durability. It is a set of properties that define the behavior of a transaction in a database management system. The ACID properties ensure that a transaction is executed in a reliable and consistent manner, even in the presence of errors or system failures.
Adaptive Learning Rate: A technique used in optimization algorithms, such as SGD and Mini-Batch Gradient Descent, to dynamically adjust the learning rate during the optimization, based on the progress of the optimization and the magnitude of the gradients, and allowing for faster convergence and better handling of noisy gradients.
Aggregate Function: A function such as SUM, AVG, COUNT, MIN, or MAX that performs a calculation on a set of values and returns a single result.
Artificial Intelligence (AI): The field of computer science and engineering that focuses on creating systems and algorithms that can perform tasks that normally require human intelligence, such as perception, reasoning, and learning.
Artificial Intelligence (AI): The field of study that focuses on building intelligent systems and machines, that can perform tasks that normally require human intelligence, such as perception, reasoning, learning, and decision-making.
Artificial Neural Networks: A type of machine learning algorithm that is inspired by the structure and function of the human brain, and consists of multiple interconnected nodes, or neurons, that process and transmit information.
Association Rule Mining: The process of discovering frequent patterns or associations in
Autoencoder: A type of neural network used for unsupervised learning, where the goal is to learn a compact representation, or encoding, of the input data, by training the network to reconstruct the input data from its encoding, through an encoding-decoding architecture, where the encoding is learned as a bottleneck or compressed layer, and the decoder is learned as an upsampling or expansion layer.
Autoencoder: A type of neural network used for unsupervised learning, where the goal is to reconstruct the input data, by encoding it into a lower dimensional representation (latent code), and then decoding it back to the original data space, through an encoding-decoding architecture, with the encoding and decoding parameters learned through optimization. Autoencoders can be used for tasks such as dimensionality reduction, anomaly detection, and generative modeling.
Backpropagation: An algorithm used in machine learning to compute the gradients of the parameters, with respect to the loss function, in neural networks and deep learning models, by propagating the error back through the network and using chain rule of differentiation.
Backstops: Devices in a rowing boat that prevent the oars from sliding out of the oarlocks during a stroke.
Backup: A backup is a copy of data that is stored for safekeeping in case the original data is lost or damaged. Backups are used to ensure that data can be recovered in case of a disaster, such as a hardware failure, software bug, or security breach.
Batch Normalization: A technique in deep learning where the activations of each layer are normalized by subtracting the mean and dividing by the standard deviation, for each mini-batch, to improve the stability, generalization, and convergence of the learning process, by reducing the internal covariate shift and the dependence on the initial weights.
Batch Processing: The processing of a large number of records in a database or spreadsheet in a single batch, as opposed to processing each record individually.
Big Data: A term used to describe large and complex datasets that are beyond the capacity of traditional data processing systems and methods, requiring new technologies and approaches to capture, store, process, and analyze them.
Big Data: A term used to describe the large and complex datasets, generated from various sources, such as social media, IoT devices, sensors, and applications, that require new and advanced technologies, such as distributed systems and parallel processing, to process and analyze them in real-time or near real-time.
Big Data: Large, complex data sets that are beyond the ability of traditional data processing systems to handle, often requiring new technologies and approaches, such as distributed computing and NoSQL databases.
Blade work: The technique used by a rower to generate power and speed with the oar blade.
Blade: The flat part of the oar that enters the water and creates propulsion.
Blade: The flat, broad part of the oar that comes into contact with the water and propels the boat forward.
Blade: The flat, broad part of the oar that enters the water.
Boat speed: The speed at which the boat is moving through the water.
Boltzmann Machine: A type of generative model, used for unsupervised learning, where the goal is to learn a probability distribution over the binary states of the nodes, by using the Gibbs sampling algorithm, to generate new samples, and by using the contrastive divergence algorithm, to estimate the gradient of the log-likelihood of the data, with respect to the model parameters.
Bow seat: The rower who sits closest to the bow of the boat.
Bow Seat: The seat closest to the bow in a rowing boat.
Bow seat: The seat closest to the bow of the boat, usually occupied by the largest or strongest rower.
Bow side: The side of the boat closest to the bow.
Bow: The front of a rowing boat.
Bow: The front of the boat.
Bow: The front of the rowing boat.
Bowball: A round ball attached to the bow of a rowing boat to prevent collisions.
Bowball: A round ball mounted on the bow of a boat, used to prevent collisions.
Bullet Point List All Row: Terminology and Related Definitions.
Business Intelligence (BI): The technologies, tools, and practices used to transform data into actionable information for use in decision-making and strategic planning.
catch: The beginning of the drive phase in the rowing stroke, when the oar blade is placed in the water.
Catch: The beginning of the rowing stroke when the blade enters the water.
Catch: The beginning of the stroke when the blade enters the water.
Catch: The starting position of the rowing stroke, where the oar blade is submerged in the water and the legs are extended.
Cell: The smallest unit of data in a table or spreadsheet, typically defined by the intersection of a row and column.
Classification: A type of predictive modeling that involves assigning data points to one of several predefined categories or classes, based on their characteristics.
Cloud Computing: A model of delivering computing resources and services over the internet, on demand, and on a pay-per-use basis, allowing users to access and run applications, store and process data, and manage networks and infrastructure, without having to own and maintain physical hardware.
Cloud Computing: A model of delivering computing resources, such as servers, storage, and applications, over the internet, on a pay-per-use basis, rather than owning and managing them locally.
Cloud Computing: The delivery of computing resources, such as servers, storage, and applications, over the internet, as a service, allowing for flexible and scalable computing capabilities on-demand.
Cloud Native: A term used to describe applications and services that are designed, developed, and deployed to take advantage of the features and benefits of cloud computing, such as scalability, reliability, and agility.
Clustering: A type of unsupervised machine learning that involves grouping data points based on their similarity, without using predefined categories or classes.
Column: A vertical arrangement of data or information in a table or spreadsheet.
Containerization: A technology and approach that involves packaging and running applications and their dependencies in containers, isolated from the underlying operating system and infrastructure, to improve portability, consistency, and scalability.
Containers: A lightweight and isolated operating system virtualization technology, that enables developers to package and deploy applications and services with their dependencies, configurations, and libraries, in a consistent and portable manner, across different environments and platforms.
Continuous Delivery (CD): The practice of automatically delivering and deploying software changes to production, as soon as they are ready, with high confidence and low risk, by automating and testing the entire delivery pipeline.
Continuous Integration (CI): The practice of automatically building and testing software every time a change is made to the code, to ensure the stability and quality of the software and catch problems early in the development process.
Convolutional Neural Network (CNN): A type of neural network used in computer vision and image recognition, characterized by its convolutional layers, pooling layers, and fully connected layers, that extract features and hierarchies of features from the images, and use them to classify the images.
Convolutional Neural Network (ConvNet or CNN): A type of feedforward neural network, used for supervised learning, where the goal is to model the spatial structure and invariances of image data, by using convolutional layers, that scan a local region, or kernel, of the input image, and apply a shared set of weights, to produce a feature map, that represents the activations of a particular filter, and by using pooling layers, that reduce the spatial dimensions, or resolution, of the feature maps, by applying a down-sampling operation, such as max pooling, or average pooling.
Convolutional Neural Network (ConvNet/CNN): A type of neural network designed to process data with a grid-like topology, such as an image, by applying a set of filters or kernels that learn local patterns and features, through convolution and pooling operations, to reduce the spatial dimensions, increase the abstraction, and capture the hierarchical representations of the data.
Convolutional Neural Networks (CNNs): A type of neural network commonly used in image and video recognition tasks, that uses convolutional layers to learn local features and hierarchies of features from the data.
Cox Box: A device that amplifies the voice of the coxswain and provides information such as boat speed and stroke rate to the rowers.
Cox box: A device that provides the coxswain with information about the boat’s speed, stroke rate, and other data.
Coxbox: A device mounted in the boat that amplifies the coxswain’s voice and provides information such as stroke rate, distance, and elapsed time.
Coxed: A boat with a coxswain.
Coxed: A rowing boat with a coxswain, typically propelled by a pair, four or eight rowers.
Coxless: A boat without a coxswain.
Coxless: A rowing boat without a coxswain, typically propelled by a pair or four rowers.
Coxswain (Cox): The person in charge of steering and coordinating the rowers in a rowing boat.
Coxswain: A person who steers the boat and provides guidance and direction to the rowers, often using a microphone or megaphone.
Coxswain: The person in a rowing boat who sits at the stern (rear) and steers the boat, sets the pace, and motivates the rowers.
Coxswain: The person in charge of steering and directing the boat, usually located in the stern.
Coxswain: The person responsible for steering, timing, and communicating in a rowing boat.
Crab: When a rower fails to fully extract their oar from the water and gets it caught, causing the boat to slow down or stop.
Cross-Validation: A technique used in machine learning to evaluate and compare the performance of different models, and to select the best model, by dividing the available data into training and validation sets, and using the validation set to evaluate the performance of each model, and avoiding overfitting.
Cross-Validation: A technique used in machine learning to evaluate the performance and generalization of the model, by dividing the training data into multiple folds, training the model on different combinations of the folds and evaluating the performance on the remaining fold, and averaging the performance over the folds, to obtain a more robust and unbiased estimate of the performance.
Crotch Strap: A strap in a rowing boat that helps secure the rower to their seat and prevent them from sliding during a stroke.
Daggerboard: A removable board located near the keel of the boat, used to improve stability and prevent lateral drift.
Damper: A device in a rowing boat that controls the swing of the boat and helps keep it stable.
Dashboard: A visual interface that provides a consolidated view of key performance indicators and data from multiple sources, often used in business intelligence and data analysis applications.
Data Cleansing: The process of identifying and correcting errors, inconsistencies, and duplicates in data.
Data Cleansing: The process of identifying and removing errors, inconsistencies, and duplicates in data, to improve the quality and accuracy of the data, and prepare it for analysis and modeling.
Data Dictionary: A data dictionary is a metadata repository that contains information about the structure, relationships, and constraints of a database. Data dictionaries are used to manage the metadata of a database, to ensure that data is consistent and accurate, and to provide a central source of information about the database for developers, administrators, and end users.
Data Engineering: The process of designing, building, and maintaining the infrastructure and pipelines for data collection, storage, processing, and analysis, to support data-driven applications and services.
Data Governance: The overall management of the availability, usability, integrity, and security of the data used in an organization.
Data Governance: The processes, policies, standards, and metrics used to ensure the availability, reliability, and security of an organization’s data.
Data Lake: A large and scalable repository of raw and unstructured data, stored in its original format, and accessible for processing and analysis through a variety of tools and methods.
Data Lineage: The history of where data came from and how it was transformed over time.
Data Mart: A smaller, departmental data repository that provides data specifically tailored to the needs of a particular business unit or function.
Data Mart: A subset of a larger data warehouse, focused on a specific business area or department.
Data Mining: Data mining is the process of discovering patterns and knowledge from large amounts of data. Data mining algorithms are used to analyze data and identify relationships, trends, and patterns that can be used to make predictions, classify data, and identify anomalies.
Data Mining: The process of analyzing data to identify patterns and relationships, often with the goal of discovering new information.
Data Mining: The process of automatically discovering patterns or relationships in large data sets.
Data Mining: The process of discovering hidden patterns, relationships, and insights in large and complex datasets, using various algorithms, models, and techniques, such as association rule learning, clustering, and classification.
Data Model: A representation of data and the relationships between data entities, used to guide the design and implementation of databases.
Data Profiling: The process of examining data to discover characteristics and anomalies, such as data types, value distributions, and missing or invalid values.
Data Quality: A measure of the degree to which data meets the needs of its intended users, such as accuracy, completeness, consistency, and timeliness.
Data Quality: The degree to which data is accurate, complete, and relevant to its intended use.
Data Science: An interdisciplinary field that combines various techniques, methods, and tools from statistics, mathematics, computer science, and domain expertise, to extract insights, knowledge, and value from data.
Data Science: The field of study that combines statistics, mathematics, and computer science, to extract insights and knowledge from data, using techniques such as data mining, machine learning, and visualization.
Data Stewardship: The responsibility for ensuring the accuracy, completeness, and accessibility of data within an organization.
Data Stewardship: The role of individuals or teams within an organization responsible for ensuring the accuracy, completeness, and consistency of data.
Data Type: A classification of data, such as text, numerical, or date/time, which determines how the data can be stored and manipulated.
Data Visualization: The process of creating graphical and interactive representations of data, such as charts, graphs, maps, and dashboards, to visually communicate and explore data insights, patterns, and trends, and enable data-driven decision-making.
Data Warehouse: A centralized repository of structured data, used for storing, managing, and analyzing large amounts of data from multiple sources, for business intelligence and decision-making purposes.
Data Warehouse: A data warehouse is a large, centralized repository of data that is designed to support business intelligence activities, such as data analysis, reporting, and data mining. Data warehouses are optimized for read-intensive workloads and are typically populated with data from multiple sources, including transactional systems, log files, and external data sources.
Data Warehouse: A large, centralized repository of data optimized for fast query performance and analysis, used in business intelligence and data analysis applications.
Data Warehousing: A large, centralized repository of data that is optimized for querying and analysis.
Data Warehousing: A process of collecting, storing, and organizing large amounts of data, from various sources, into a centralized repository, for analysis and decision-making.
Database: A collection of data that is organized and stored in a structured manner, to allow for efficient retrieval, manipulation, and analysis of the data.
Deep Belief Network (DBN): A type of generative model, used for unsupervised learning, where the goal is to learn a hierarchy of RBMs, where each RBM is trained to reconstruct the activations of the previous layer, and the final layer is used as the generative model, to sample new data, by successively transforming random noise into higher-level representations.
Deep Learning: A subfield of machine learning that focuses on artificial neural networks with multiple hidden layers, designed to learn hierarchical representations of data and perform tasks such as image recognition or natural language processing.
Deep Learning: A subfield of machine learning that focuses on building artificial neural networks, inspired by the structure and function of the human brain, to perform complex tasks such as image and speech recognition, natural language processing, and autonomous decision-making.
Deep Learning: A type of machine learning that uses deep artificial neural networks, with multiple layers, to learn and extract high-level features and representations from the data, for various tasks, such as image recognition, natural language processing, and speech recognition.
Denormalization: Denormalization is the process of adding redundant data to a database in order to improve query performance. Denormalization is sometimes used in data warehousing to reduce the complexity of queries and to improve query performance, although it can also increase the risk of data inconsistencies.
Denormalization: Denormalization is the process of relaxing the normalization rules to improve query performance by reducing the number of join operations required to retrieve data. Denormalization involves adding redundant data to a database to avoid the need for complex join operations.
Denormalization: The process of intentionally introducing redundancy into a database for the purpose of improving query performance.
DevOps: A culture and practice of collaboration, communication, and integration between software development and operations teams, aimed at delivering high-quality and reliable software faster, by automating and streamlining the development and delivery pipeline.
DevOps: A set of practices and tools that aim to improve the collaboration and communication between software developers and operations teams, and to automate and accelerate the software development and deployment process.
Dimension Table: In a dimensional data model, a table that contains descriptive information about the data in a fact table, such as product information or time information.
Dimension: In a data warehouse or OLAP cube, a dimension is a category of data, such as time, location, or product, that is used to categorize data and support multidimensional analysis.
Dimensional Modeling: A technique for organizing data in a data warehouse, in which data is organized into facts and dimensions.
Distributed database: A distributed database is a database that is spread across multiple nodes or machines. Distributed databases are designed to provide high availability, scalability, and performance by allowing data to be stored and processed across multiple nodes.
Double: A boat designed for two rowers, each with one oar.
Drag: The resistance that opposes the motion of a body through a fluid, such as water.
D-rigger: A type of rigger that places the oarlocks inboard of the gunwales, allowing for a longer oar and increased leverage.
D-rigging: A type of rigging that is used to reduce the amount of catch and increase the efficiency of the rowing stroke.
Drill-Down: The process of navigating to a lower level of detail in a hierarchy, such as from year to quarter in a time dimension.
Drive: The middle part of the stroke when the rower applies power to the oar.
Drive: The portion of the rowing stroke when the rower applies power to the oar and moves the boat forward.
Drive: The power phase of the rowing stroke, where the legs, back, and arms apply force to the oar, propelling the boat forward.
Dropout: A regularization technique in deep learning where some of the neurons in a layer are randomly dropped out, or set to zero, during each forward pass, with a certain probability, to prevent overfitting, by reducing the complexity and co-adaptation of the model, and by adding some randomness and diversity to the model.
Early Stopping: A technique used in optimization algorithms, such as SGD and Mini-Batch Gradient Descent, to prevent overfitting and improve the generalization of the model, by monitoring the performance on a validation set, and stopping the optimization when the performance starts to degrade, and choosing the best model based on the validation performance.
Edge Computing: A computing paradigm that brings computation and data storage closer to the edge of the network, where devices and sensors are located, to reduce latency, improve response times, and reduce network bandwidth and costs.
Eight: A boat designed for eight rowers, each with one oar.
Ensemble Methods: Techniques used in machine learning to combine multiple models and leverage their diversity and complementary strengths, to achieve better performance and generalization, such as bagging, boosting, and stacking.
Ergometer: A machine that simulates the rowing motion and is used for indoor training.
ETL (Extract, Transform, Load): ETL is the process of extracting data from one or more sources, transforming the data to meet the requirements of a target system, and loading the data into the target system. ETL is a common activity in data warehousing, where data is extracted from transactional systems, transformed to meet the requirements of the data warehouse, and loaded into the data warehouse for analysis.
ETL (Extract, Transform, Load): The process of extracting data from multiple sources, transforming the data into a common format, and loading the data into a data warehouse or other target system.
Extraction, Transformation, Loading (ETL): The process of extracting data from source systems, transforming it into a format suitable for analysis, and loading it into a data warehouse or data mart.
F1 Score: A metric used in classification problems, to evaluate the performance of a machine learning model, by combining precision and recall, and representing the harmonic mean of the two, and balancing the trade-off between them.
Fact Table: In a data warehouse, a fact table contains the measures, or facts, that are being analyzed, along with the foreign keys to the dimension tables.
Fact Table: In a dimensional data model, a table that contains the measures or facts of interest, such as sales or quantities.
Feather: The tilting of the blade in the oarlock to reduce wind resistance while not rowing.
Feathering: The act of turning the oar blade perpendicular to the water in preparation for the recovery.
Feature Engineering: The process of creating and selecting the relevant and informative features from the raw data, to represent the problem and improve the performance of the machine learning model. Feature engineering may involve techniques such as feature scaling, normalization, transformation, dimensionality reduction, and feature extraction.
Feature Engineering: The process of creating new and relevant features from existing data, to improve the performance and accuracy of machine learning models, and capture the relationships and dependencies between the data.
Feature Selection: The process of selecting a subset of the features from the complete feature set, based on their relevance, importance, or correlation to the target, to reduce the dimensionality, complexity, and noise in the data, and improve the performance and interpretability of the machine learning model.
Field: An individual data item within a record in a database or spreadsheet.
Filter: A function that allows you to display only specific records in a table or spreadsheet based on criteria you specify.
Finish: The end of the rowing stroke when the blade is lifted out of the water.
Finish: The end of the stroke when the blade is lifted out of the water.
Finish: The end position of the rowing stroke, where the oar blade is lifted out of the water and the body is leaned forward.
Foot plate: A flat platform in a rowing boat where the rower’s feet are placed.
Foot Stretcher: A device in a rowing boat that allows the rower to adjust the length of their slide and the position of their feet.
Foot Stretcher: A device in a rowing boat that allows the rower to secure their feet and apply more power to the stroke.
Foot stretcher: A device in a rowing boat that the rower’s feet are secured to during the rowing stroke.
Foot stretcher: A flat board attached to the boat that the rower places their feet against, used for leverage during the rowing stroke.
Footstretcher: A device that allows the rower to adjust the position of their feet while rowing.
Foreign Key: A field in a database table that refers to the primary key of another table, creating a relationship between the two tables.
Foreign Key: A foreign key is a column or combination of columns in one table that refers to the primary key of another table, creating a relationship between the two tables. Foreign keys are used to enforce referential integrity, ensuring that data in one table corresponds to data in another table.
Foreign Key: A reference to a primary key in another table, used to enforce referential integrity.
Four: A boat designed for four rowers, each with one oar.
Gates: The movable pieces in an oarlock that hold the oar in place.
Gaussian Mixture Model (GMM): A type of probabilistic generative model, used for unsupervised learning, where the goal is to model a data distribution, by assuming it is a mixture of several Gaussian distributions, with different means and variances, and by estimating the parameters, such as the weights, means, and covariances, of the Gaussians, using the Expectation-Maximization (EM) algorithm, to maximize the likelihood of the data.
Generative Adversarial Network (GAN): A type of deep generative model, used for unsupervised learning, where the goal is to learn a generator, that can produce new data samples, that are similar to the training data, and a discriminator, that can distinguish between the generated data and the real data, by playing a two-player minimax game, where the generator tries to fool the discriminator, by producing increasingly realistic data, and the discriminator tries to detect the fake data, by updating its parameters, based on the feedback from the generator.
Generative Adversarial Network (GAN): A type of generative model composed of two parts: a generator that creates new data samples by transforming a random noise vector into a synthetic sample, and a discriminator that evaluates the authenticity of the generated sample and the real sample, by classifying them into fake or real, and providing feedback to the generator to improve its generation, through an adversarial process that minimizes the cross-entropy loss between the generated and real distributions.
Generative Adversarial Network (GAN): A type of neural network used in generative modeling, consisting of two networks: a generator and a discriminator, that compete against each other, by generating fake samples and trying to fool the discriminator, and by trying to distinguish between real and fake samples, respectively, until convergence or a stopping criterion is reached.
Generative Adversarial Networks (GANs): A type of machine learning model that involves two neural networks, a generator and a discriminator, competing against each other to generate realistic data, such as images or speech, or to distinguish real data from generated data.
Gradient Descent: An optimization algorithm used in machine learning to find the optimal parameters of the model, by iteratively updating the parameters in the direction of the negative gradient of the loss function, with respect to the parameters, until convergence or a stopping criterion is reached.
Gradient Descent: An optimization algorithm used in machine learning to find the optimal values of the model parameters that minimize the loss function, by iteratively adjusting the parameters in the direction of the steepest decrease of the loss, using the gradient of the loss with respect to the parameters.
Graph Database: A database that stores data in the form of nodes and edges, representing entities and relationships, respectively, and allows for fast querying and analysis of complex relationships in the data.
Gunwale: The upper edge of the boat’s side, often reinforced to provide extra strength.
Gunwale: The upper edge of the side of a rowing boat.
Hadoop: An open-source framework for distributed storage and processing of big data, using a cluster of commodity computers and the Hadoop Distributed File System (HDFS).
Hadoop: An open-source software framework for storing and processing big data on a cluster of commodity hardware, using a distributed file system (HDFS) and a parallel processing engine (MapReduce).
Handle: The part of an oar that the rower holds onto.
Handle: The part of the oar that is gripped by the rower.
Handle: The part of the oar that the rower holds onto and uses to apply force to the blade.
Hatchet blade: A type of oar blade that is narrow and shaped like a hatchet and used primarily in sculling.
Here are some additional row-related terms and definitions:
Here are some additional terms related to databases and data management:
Here are some more rowing-related terms:
Here’s a bullet point list of row terminology and related definitions:
Hierarchy: A level of detail in a dimension, such as year, quarter, month, and day in a time dimension.
Hull: The bottom and sides of a rowing boat that sits in the water.
Hybrid Cloud: A cloud computing model that combines the use of public and private clouds, for different workloads, to leverage the benefits of both models and meet the specific needs and requirements of the organization.
Hyperparameter Optimization: The process of finding the best values for the hyperparameters, which are the parameters that control the learning algorithm and the model, such as the learning rate, regularization strength, number of hidden units, and others, by tuning them based on a validation set, or by using techniques such as grid search, random search, or Bayesian optimization.
Hyperparameter Optimization: The process of tuning the values of the hyperparameters, or the parameters that control the learning process and the model complexity, in a machine learning model, to improve its performance and generalization, and prevent overfitting.
Hyperparameters: The parameters of a machine learning model that are set prior to training the model, and control the behavior and the capacity of the model, such as the learning rate, the number of hidden units, the regularization strength, etc.
Index: A data structure that provides fast and efficient access to specific rows in a database, based on a particular column or set of columns, and is used to speed up queries and reduce the number of rows that need to be scanned.
Index: A database index is a data structure that provides a fast and efficient way to access data in a table. An index acts as an intermediary between a query and a table and speeds up search operations by reducing the amount of data that must be scanned.
Index: An index is a data structure that provides fast access to data in a database. An index works by creating a mapping between the values in a column and the location of the corresponding rows in a table. By using an index, a database can avoid scanning the entire table to find the data it needs, reducing the time required to retrieve data.
Index: An ordered list of values used to improve the speed of data retrieval operations in a database.
Infrastructure as a Service (IaaS): A cloud computing service that provides virtualized computing resources, such as servers, storage, and networking, over the internet, allowing users to rent and manage their own infrastructure.
Infrastructure as a Service (IaaS): A model of delivering computing infrastructure, such as servers, storage, and network, over the internet as a service, rather than as a product to be installed and maintained on-premises.
Internet of Things (IoT): A network of connected devices, sensors, and other physical objects, equipped with the ability to collect and exchange data, often used for real-time monitoring and control of physical systems and processes.
Internet of Things (IoT): A network of interconnected physical devices, vehicles, buildings, and other items, embedded with electronics, software, and sensors, that enables them to collect and exchange data, and to be monitored and controlled remotely.
Join: A operation in a relational database that combines rows from two or more tables based on a related column between them.
Keel: A central ridge or beam running the length of the boat’s underside, providing stability.
Keel: A central structural element running the length of a rowing boat that provides stability and prevents it from tipping over.
Keel: A longitudinal structural element in the bottom of the boat that helps to keep it stable and prevent it from tipping over.
Key: A unique identifier for a row in a database table.
K-Nearest Neighbors (KNN): A type of instance-based classifier, used for supervised learning, where the goal is to classify a new data sample, by finding the K nearest neighbors in the training data, based on a distance metric, such as Euclidean distance, and by majority voting, among the K neighbors, to determine the class label of the sample.
Kubernetes: An open-source platform for automating the deployment, scaling, and management of containerized applications, widely used for cloud-native and microservices applications.
Kubernetes: An open-source system for automating the deployment, scaling, and management of containerized applications, using a declarative configuration, and providing features such as self-healing, automatic scaling, and rolling updates.
Launch: A motorized boat used to follow and support rowing boats during training and races.
Layback: The amount of backward lean that a rower has at the finish of the stroke.
Machine Learning (ML): A subfield of artificial intelligence that focuses on the development of algorithms and models that can learn from data and improve their performance over time, without being explicitly programmed.
Machine Learning: A subfield of AI that focuses on developing algorithms and models that can learn from data, and make predictions or decisions, without being explicitly programmed.
Machine Learning: A subset of artificial intelligence that focuses on the development of algorithms and models that can learn from and make predictions or decisions based on data, without being explicitly programmed.
Master Data Management (MDM): The processes, policies, and technologies used to manage the master data of an organization, such as customer and product data.
Master Data: Data that is used across multiple systems or departments within a company, such as customer information or product information.
Materialized View: A materialized view is a view that is stored on disk and is pre-populated with data. Materialized views can be used to improve query performance by reducing the amount of data that needs to be retrieved from the underlying tables.
MDX (Multidimensional Expressions): A query language used to extract data from OLAP cubes.
Mean Absolute Error (MAE): A common metric used in regression problems, to evaluate the performance of a machine learning model, by measuring the average absolute difference, or error, between the predicted values and the actual values.
Metadata: Data about data, including information about the structure, quality, and context of data.
Microservices: A software architecture approach that involves breaking down a monolithic application into smaller, loosely coupled, and independently deployable services, to improve scalability, reliability, and manageability.
Microservices: An architectural style for building and deploying software applications as a collection of loosely coupled, small, and independent services, each running in its own process, and communicating through APIs and messaging protocols.
Mini-Batch Gradient Descent: An optimization algorithm used in machine learning to find the optimal values of the model parameters, by randomly selecting a small batch of samples from the training data at each iteration, and updating the parameters based on the gradient of the loss with respect to the parameters for that batch, and balancing the trade-off between the computation cost and the accuracy of the optimization.
Model Ensemble: A technique in machine learning where multiple models are trained and combined to make a prediction, either by voting, weighting, averaging, or stacking, to improve the robustness, stability, and performance of the prediction, by reducing the variance, bias, and overfitting of a single model.
Model Pruning: A technique in machine learning where the parameters and connections of a model are removed or set to zero, based on their importance, magnitude, or sparsity, to reduce the complexity, size, and overfitting of the model, and improve the efficiency and interpretability of the model.
Model Selection: The process of choosing the best machine learning model among a set of candidate models, based on the performance and the generalization on a validation set, or using techniques such as cross-validation and model averaging.
Momentum: A technique used in optimization algorithms, such as SGD and Mini-Batch Gradient Descent, to improve the convergence and stability of the optimization, by adding a memory of the previous gradients to the current update, and dampening the oscillations and speeding up the convergence.
Multi-Cloud: A cloud computing model that involves using multiple public cloud providers, for different workloads, to achieve diversity, resilience, and cost optimization.
Multi-Dimensional Data Model: A data model that represents data in multiple dimensions, such as time, geography, and product.
Multi-layer Perceptron (MLP): A type of feedforward neural network, used for supervised learning, where the goal is to model a non-linear function, by using multiple layers of artificial neurons, connected by weighted edges, that apply non-linear activation functions, such as sigmoid, tanh, or ReLU, to the weighted sum of their inputs, to produce the outputs, and by updating the weights, using gradient descent and backpropagation, to minimize the error, or the difference between the predicted and target outputs.
Natural Language Processing (NLP): A field of artificial intelligence and machine learning that focuses on the interactions between computers and human languages, and involves tasks, such as text classification, named entity recognition, sentiment analysis, and machine translation.
Natural Language Processing (NLP): A subfield of AI that focuses on the interaction between computers and humans, using natural language, for tasks such as text classification, sentiment analysis, machine translation, and question-answering.
Natural Language Processing (NLP): The field of artificial intelligence and computer science concerned with the interactions between computers and humans using natural language.
Neural Networks: A type of machine learning model, inspired by the structure and function of the human brain, that consists of interconnected nodes, or artificial neurons, that perform simple computations and pass the information to each other.
Normalization: Normalization is the process of organizing data in a database so that it is stored in a consistent and efficient manner. Normalization involves dividing a database into smaller tables, and establishing relationships between the tables to minimize data redundancy and improve data integrity.
Normalization: Normalization is the process of organizing data in a database to minimize redundancy and improve data integrity. Normalization involves dividing a database into two or more tables, and defining relationships between the tables to ensure that data is stored in the most efficient and accurate way.
Normalization: The process of organizing data in a database to minimize data redundancy and improve data consistency.
Normalization: The process of organizing data in a database to minimize data redundancy and improve data integrity.
NoSQL Database: A type of database that does not use the relational model, and can handle a wider range of data structures and types, such as documents, graphs, key-value pairs, and columns, and is designed for scalability, flexibility, and performance.
NoSQL: A class of databases that do not use the traditional relational database model, but instead use alternative data models, such as key-value, document, or graph, for storing and accessing data.
NoSQL: A term used to describe non-relational databases, such as document databases, key-value stores, graph databases, or columnar databases, that are designed to handle big data, provide high scalability and availability, and allow flexible and unstructured data models.
NoSQL: NoSQL refers to a non-relational database management system that does not use the traditional SQL (Structured Query Language) for data storage and retrieval. NoSQL databases are designed to handle large amounts of unstructured or semi-structured data and can be used for applications that require fast, scalable, and flexible data storage.
Oar collar: A plastic or metal ring that fits around the oar shaft, near the blade, to prevent water from entering the boat.
Oar Rigging: The adjustable mechanism in a rowing boat that sets the position of the oarlocks relative to the rower.
Oar stop: A device that limits the extent to which the oar can pivot.
Oarlock: A device that holds the oar in place and allows it to pivot.
Oarlock: A pivot point on the side of a rowing boat that holds the oar and allows it to move freely.
Oarlock: A U-shaped device that holds the oar in place and allows it to pivot as the rower applies power.
Oarlock: The mechanism that holds the oar in place and allows it to pivot during the rowing stroke.
Of course! Here are some more terms related to machine learning:
OLAP (Online Analytical Processing): A category of software that enables users to analyze data from multiple dimensions and levels of detail, including drilling down, rolling up, and slicing and dicing the data.
OLAP (Online Analytical Processing): A type of software that allows for multidimensional analysis of data stored in databases.
OLAP (Online Analytical Processing): OLAP is a category of software tools that support multi-dimensional analysis of data. OLAP tools are used to analyze data from multiple perspectives and to support decision-making tasks.
OLAP Cube: A multidimensional data structure used in OLAP to represent data in a compact and efficient manner, allowing for fast querying and analysis.
OLTP (Online Transaction Processing): A category of software that supports a high volume of transactions for business-critical applications, such as financial systems and e-commerce sites.
OLTP (Online Transaction Processing): A type of software that processes transactions, such as sales or banking transactions, in real-time.
OLTP (Online Transaction Processing): OLTP is a category of software tools that support transaction-oriented applications, such as financial systems, inventory systems, and customer relationship management systems. OLTP tools are used to support high-volume, high-concurrency, and low-latency data access.
Overfitting: A phenomenon in machine learning where a model is too complex or flexible, and fits the training data too well, but fails to generalize to new and unseen data, due to high variance, low bias, and excessive memorization of the noise and outliers in the training data.
Overfitting: A problem in machine learning, where the model fits too closely to the training data, and captures the noise and the outliers in the data, leading to poor performance and generalization on unseen data.
Overfitting: A problem in machine learning, where the model is too complex, and has too many parameters, and fits the training data too well, resulting in poor generalization and poor performance on new, unseen data.
Pair: A boat designed for two rowers, each with one oar.
Partition: A partition is a way to divide a large table into smaller, more manageable pieces called partitions. Partitioning can be used to improve query performance by reducing the amount of data that needs to be scanned, and it can also be used to manage data growth by allowing older data to be moved to separate partitions.
Partitioning: Partitioning is the process of dividing a large table into smaller, more manageable pieces, called partitions. Partitioning is used to improve the performance of data retrieval and management, to make it easier to manage large amounts of data, and to support scalability and high availability.
Pivot Table: A type of spreadsheet data representation used to summarize and analyze data by grouping and aggregating data based on one or more columns.
Platform as a Service (PaaS): A cloud computing service that provides a platform for developing, running, and managing applications and services, without the complexity of managing the underlying infrastructure.
Platform as a Service (PaaS): A model of delivering a platform for the development, deployment, and management of applications and services over the internet as a service, rather than as a product to be installed and maintained on-premises.
Port Side: The left side of a rowing boat when facing the bow (front) of the boat.
Port Side: The left side of a rowing boat, as seen from the coxswain’s seat facing the bow.
Port side: The left side of the boat when facing the stern.
Port: The left side of the boat when facing the bow (front) of the boat.
Port: The left-hand side of a rowing boat when facing the bow (front).
Power 10: A call from the coxswain to row at maximum effort for ten strokes.
Precision: A metric used in classification problems, to evaluate the performance of a machine learning model, by measuring the fraction of the positive predictions that are actually correct, and represents the ability of the model to avoid false positives.
Predictive Analytics: The use of predictive models, data mining, and machine learning, to analyze and interpret data, and make predictions and decisions, in various domains and applications, such as business, healthcare, and finance.
Predictive Analytics: The use of statistical and machine learning techniques to analyze data and make predictions about future events or trends.
Predictive Model: A mathematical representation of a system or process that is used to make predictions about future outcomes based on historical data and other inputs.
Predictive Modeling: A type of data science that uses statistical and machine learning techniques to build models that can make predictions, based on historical and current data, about future events or outcomes, such as sales, customer churn, and risk.
Primary Key: A primary key is a unique identifier for each row in a table, used to enforce the integrity of the data and to ensure that data can be retrieved and updated efficiently. A primary key is a column or combination of columns in a table that must have a unique value for each row.
Primary Key: A unique identifier for a row in a database table, used to enforce referential integrity.
Primary Key: A unique identifier for each record in a database table, used to distinguish between records.
Principal Component Analysis (PCA): A type of linear dimensionality reduction, used for unsupervised learning, where the goal is to find a lower-dimensional representation of the data, by finding the directions, or principal components, of maximum variance, and by projecting the data onto these directions, to capture most of the variance, while ignoring the noise, and to avoid overfitting, or retaining too much detail.
Private Cloud: A cloud computing model where the services and infrastructure are owned and operated by a single organization for its exclusive use, either on-premises or hosted by a third-party provider.
Public Cloud: A cloud computing model where the services and infrastructure are owned and operated by a third-party provider and made available to the public over the internet.
Query Optimization: Query optimization is the process of improving the performance of a query by selecting the most efficient execution plan. Query optimization involves analyzing the structure of the query and the data it accesses, and selecting the most efficient algorithms and data structures to retrieve the data.
Query: A query is a request for information from a database. Queries are used to retrieve data from a database and to manipulate data stored in a database.
Query: A request for specific data from a database or spreadsheet, typically using a language such as SQL or a graphical user interface.
Query: An instruction, written in a database language, such as SQL, that retrieves and returns data from a database, based on specific criteria and conditions.
Rating: The number of strokes per minute that a crew is rowing at.
rating: The number of strokes taken per minute by a rowing crew, often used as a measure of their speed and power.
Real-Time Analytics: The use of real-time data and stream processing to support decision-making and immediate actions, such as fraud detection or customer engagement.
Recall: A metric used in classification problems, to evaluate the performance of a machine learning model, by measuring the fraction of the positive cases that are correctly identified by the model, and represents the ability of the model to find all the positive cases.
Recommendation Systems: A type of machine learning system that provides personalized recommendations, such as items, products, or content, to users, based on their past behaviors, preferences, and context, and aims to increase engagement, satisfaction, and loyalty.
Recommender Systems: Algorithms and systems that use data, such as user preferences and behavior, to make personalized recommendations to users, such as movie or product recommendations.
Record: A collection of related data fields in a database or spreadsheet that represent a single item or entity.
Recovery: The part of the stroke when the blade is lifted out of the water and the rower prepares for the next catch.
Recovery: The portion of the rowing stroke when the oar is lifted out of the water and returned to the catch position.
recovery: The second half of the rowing stroke, when the oar is pulled back towards the rower.
Recurrent Neural Network (RNN): A type of neural network designed to process sequences of data, such as text, speech, or time series, by using feedback connections and hidden states that allow the network to retain and propagate information from one step to the next, through recurrent and sequential operations, to capture the dependencies and patterns of the data over time.
Recurrent Neural Network (RNN): A type of neural network used in sequential data processing, characterized by its recurrent connections, that allow the network to capture the dependencies and patterns in the sequences, and use them for tasks such as language modeling, machine translation, and speech recognition.
Recurrent Neural Network (RNN): A type of neural network, used for sequential learning, where the goal is to model the temporal dependencies and dynamics of time series or sequential data, by using a hidden state, that summarizes the previous inputs and outputs, and is updated at each time step, based on the current input and the previous state, and by using a feedforward layer, that outputs the prediction, based on the current state, to handle the variable length of sequences, and to avoid vanishing gradients, by using gating mechanisms, such as Long Short-Term Memory (LSTM), or Gated Recurrent Unit (GRU).
Recurrent Neural Networks (RNNs): A type of neural network commonly used in sequence data, such as time series or natural language text, that uses feedback connections to persist information across time steps and process sequences of data.
Reference Data: Data that is used as a reference or lookup, such as a list of states or countries.
Referential Integrity: A property of a database that ensures that relationships between tables are maintained, so that data is not lost or corrupted.
Regression: A type of predictive modeling that involves predicting a continuous numerical value, such as a stock price or a person’s salary, based on one or more input variables.
Regularization: A technique used in machine learning to control the complexity and prevent overfitting of the model, by adding a penalty term to the loss function, such as the L1 regularization (Lasso), which adds the absolute value of the coefficients, or the L2 regularization (Ridge), which adds the square of the coefficients, or the Elastic Net, which is a combination of the two.
Regularization: A technique used in machine learning to penalize certain model parameters, based on their magnitude or complexity, to reduce overfitting, improve generalization, and prevent over-complex models. Examples of regularization methods include L1 regularization (Lasso), L2 regularization (Ridge), and dropout.
Regularization: A technique used in machine learning, to prevent overfitting, by adding a penalty term, such as L1 or L2 norm, to the objective function, and reducing the magnitude of the model parameters, and forcing some of them to be close to zero.
Reinforcement Learning: A type of machine learning that focuses on learning by trial and error, where an agent takes actions in an environment to maximize a reward signal.
Reinforcement Learning: A type of machine learning that focuses on learning from actions and consequences, rather than from explicit supervision, by maximizing a reward signal through trial and error in an environment.
Reinforcement Learning: A type of machine learning where an agent learns to interact with an environment, by taking actions and receiving rewards, based on its policy, to maximize a cumulative reward signal over time. Reinforcement learning is used in applications such as game playing, robotics, and autonomous systems.
Reinforcement Learning: A type of machine learning where an agent learns to make decisions and take actions in an environment, by receiving rewards or penalties for its actions, and by updating its policy, or mapping from states to actions, based on its experience, to maximize the expected cumulative reward over time, in a trial-and-error manner.
Reinforcement Learning: A type of machine learning where the algorithms learn to make decisions and take actions, based on the feedback and rewards they receive from the environment, to maximize a cumulative reward signal.
Relational Database: A type of database that stores data in tables with rows and columns, and uses a relational model, based on the theory of relations, to represent the relationships and dependencies between the data.
Release: The moment when the blade leaves the water during the rowing stroke.
Replication: Replication is the process of copying data from one database to another, either within the same database server or across different database servers. Replication is used to improve the availability and performance of data access, to provide disaster recovery solutions, and to support data warehousing and business intelligence activities.
Replication: Replication is the process of copying data from one database to another, typically in real-time. Replication is used to improve data availability and reliability by creating multiple copies of data that can be used in case of a failure or outage.
Restore: Restore is the process of copying data from a backup to a database to replace lost or damaged data. Restore operations are used to recover data after a disaster or other unexpected event.
Restricted Boltzmann Machine (RBM): A type of Boltzmann Machine, where the nodes are restricted to have binary states, and the connections between the nodes are restricted to be undirected, between the visible and hidden layers, to form a bipartite graph, making it easier to train, and to use for deep learning, as a pre-training technique for the initial layers of a deep network.
Rigger: A device that attaches the oar to the boat, allowing it to pivot and allowing the rower to apply power to the oar.
rigger: A device that attaches the oar to the side of the rowing boat.
Rigger: The assembly that holds the oar and allows it to pivot in and out of the water.
Rigging plan: The blueprint or design for the rigging of a rowing boat.
Rigging screws: Adjustable bolts that are used to fine-tune the rigger angle and oar position.
Rigging: The adjustable parts of a rowing boat that determine the location of the oarlocks and the angle of the oars.
Rigging: The arrangement of the oars, oarlocks, and other equipment on a rowing boat.
Rigging: The system of ropes, pulleys, and other components that attach the oar to the boat and control the position of the blade in the water.
ROC Curve: A graphical representation of the performance of a binary classification model, by plotting the True Positive Rate (TPR) against the False Positive Rate (FPR), at various threshold values, and measuring the area under the curve (AUC), which represents the overall performance of the model.
Roll-Up: The process of aggregating data to a higher level of detail in a hierarchy, such as from quarter to year in a time dimension.
Root Mean Squared Error (RMSE): A common metric used in regression problems, to evaluate the performance of a machine learning model, by measuring the average difference, or error, between the predicted values and the actual values, and taking the square root of the mean of the squares of the errors.
Row: A horizontal arrangement of data or information in a table or spreadsheet.
Rudder: A steerable blade that is attached to the stern of the boat and used to steer the boat.
run: The distance that a rowing boat travels during one stroke, also referred to as the slide.
Run: The distance that the boat travels during one complete cycle of the rowing stroke.
Runway: A term used to describe the distance traveled by the boat during one complete cycle of the rowing stroke.
Scalability: The ability of a system to handle increased loads of work or data, such as by adding more hardware or processing power.
Schema: A schema is a blueprint for a database, defining the tables, columns, relationships, and constraints that make up the database. The schema defines the structure of the data stored in the database, and the rules for how data can be added, modified, and deleted.
Sculling oars: Oars used in sculling, with two oars per rower.
Sculling: A form of rowing where each rower has two oars, one in each hand.
Sculling: A form of rowing where the rower uses two oars, one in each hand.
Sculling: A method of rowing with two oars, one in each hand, used in sculling boats such as singles, doubles, and quads.
Sculling: A rowing technique in which one oar is used in each hand, as opposed to sweep rowing where two oars are used in each hand.
Sculling: A style of rowing in which each rower has two oars, one in each hand.
Seat: The individual seat in a rowing boat for each rower.
Seat: The sliding seat in a rowing boat that allows the rower to move back and forth as they row.
Semi-Supervised Learning: A type of machine learning that combines the techniques of supervised and unsupervised learning, where the model is trained with a combination of labeled and unlabeled data.
Sentiment Analysis: The process of determining the emotional tone of text data, such as positive, negative, or neutral.
Serverless Computing: A cloud computing model that allows users to run applications and functions without having to manage the underlying servers and infrastructure, by abstracting and automatically scaling the infrastructure as needed.
Set: The position and angle of the boat in the water.
Shaft: The long handle part of the oar that the rower holds onto.
Shaft: The long, thin part of an oar that connects the blade to the handle.
Shaft: The long, thin part of the oar that connects the blade to the handle.
Sharding: Sharding is a database design pattern that involves dividing a large database into smaller, more manageable pieces called shards. Each shard contains a portion of the data and can be stored on a separate physical or virtual machine. Sharding is used to improve performance and scalability by reducing the amount of data that must be scanned for each query.
Shell: Another term for a rowing boat.
Skeg: A small fin attached to the hull of a rowing boat that helps stabilize the boat and prevent it from drifting sideways.
Skeg: A small fin or projection on the underside of a boat, used to improve stability and tracking.
Skeg: A small fin under the stern (rear) of the boat that helps to keep it pointed in the right direction.
Skiff: A light and narrow rowing boat, typically propelled by a single sculler.
Slide: The metal track that the seat moves on during the rowing stroke, allowing the rower to apply force more efficiently.
Slide: The track along which the seat moves back and forth in the boat during the rowing stroke.
Slide: The track in a rowing boat that the seat moves on during the rowing stroke.
Slide: The tracks in a rowing boat that the seat moves on.
Slowly Changing Dimension: A dimension that changes over time, such as a customer’s address or a product’s price, and requires special handling in the data warehouse to preserve historical data.
Snowflake Schema: A data model used in data warehousing where dimension tables are normalized, resulting in a more complex structure than a star schema.
Software as a Service (SaaS): A cloud computing service that provides software applications and services over the internet, accessible through a web browser or API, allowing users to subscribe and use them on a pay-per-use basis.
Software as a Service (SaaS): A model of delivering software applications over the internet as a service, rather than as a product to be installed on individual computers.
Sort: A function that allows you to rearrange the order of the rows in a table or spreadsheet based on the values in one or more columns.
Spark: An open-source data processing framework designed for fast and large-scale data processing, often used in conjunction with Hadoop for big data processing.
Spark: An open-source, fast and flexible, data processing engine for big data, that supports batch processing, real-time stream processing, machine learning, and graph processing, with in-memory capabilities and optimized performance.
Spoon blade: A type of oar blade that is rounded and used primarily in sculling.
Spoon: The curved part of the oar blade that enters the water.
Spoon: The curved portion of the blade that dips into the water and provides lift during the stroke.
SQL: A standard language for accessing and manipulating relational databases, used to define, manage, and query structured data, and to support transaction management and consistency.
SQL: Structured Query Language, a standard language for accessing and manipulating relational databases.
Square blade: A type of oar blade that is square in shape and used primarily in sweep rowing.
Square Blade: A type of oar blade used in some rowing boats that is wider and less curved than a spoon blade.
Star Schema: A data model used in data warehousing where a central fact table is surrounded by multiple dimension tables, with relationships between the tables represented by foreign keys.
Star Schema: A type of data model that represents data in a centralized fact table surrounded by dimension tables, which describe the data in more detail.
Starboard and Port oars: Oars used in sweep rowing, with one oar on each side of the boat.
Starboard Side: The right side of a rowing boat when facing the bow (front) of the boat.
Starboard Side: The right side of a rowing boat, as seen from the coxswain’s seat facing the bow.
Starboard side: The right side of the boat when facing the stern.
Starboard: The right side of the boat when facing the bow (front) of the boat.
Starboard: The right-hand side of a rowing boat when facing the bow (front).
Stern: The back of the boat.
Stern: The rear of the rowing boat.
Stochastic Gradient Descent (SGD): A variation of gradient descent, where the gradient is estimated on a randomly selected sample or mini-batch from the training data, instead of the entire dataset, to reduce the computational cost and enable online learning.
Stochastic Gradient Descent (SGD): An optimization algorithm used in machine learning to find the optimal values of the model parameters, by randomly selecting one sample from the training data at each iteration, and updating the parameters based on the gradient of the loss with respect to the parameters for that sample, and allowing for faster convergence and better handling of large-scale and noisy data.
Stored Procedure: A stored procedure is a pre-compiled collection of SQL statements that can be executed as a single unit. Stored procedures are used to encapsulate complex business logic, to improve the performance of frequently-executed queries, and to allow for code reuse.
Stream Processing: The processing of data in real-time as it is generated, allowing for immediate analysis and action.
Stretcher: A device in a rowing boat that allows the rower to adjust the length of their slide and the position of their feet.
Stroke Rate: The number of strokes taken by a rowing boat per minute, often measured and communicated by the coxswain.
Stroke rate: The number of strokes taken per minute by a rowing crew, often used as a measure of their speed and power.
Stroke seat: The rower sitting closest to the stern, responsible for setting the rhythm and pace of the crew.
Stroke Seat: The rower who sets the rhythm and pace for the rest of the crew, typically sitting in the bow seat or the stroke seat.
Stroke seat: The rower who sits closest to the stern and sets the pace for the rest of the crew.
Stroke: A complete cycle of the rowing motion, from the catch to the recovery.
Stroke: The complete cycle of movements performed by a rower during each phase of a rowing race.
Subquery: A query nested inside another query, used to return data that will be used in the main query as a condition to further restrict data to be retrieved.
Supervised Learning: A type of machine learning that focuses on learning from labeled training data, where the correct output is provided for each input, and the goal is to learn a mapping from inputs to outputs.
Supervised Learning: A type of machine learning where the algorithms are trained on labeled data, to learn the relationship between the input features and the output labels, and then used to make predictions on new, unseen data.
Support Vector Machine (SVM): A type of linear classifier, used for supervised learning, where the goal is to find the hyperplane that maximally separates the positive and negative classes, by maximizing the margin, or the distance between the hyperplane and the closest data points, called support vectors, that determine the location and orientation of the hyperplane.
Sure! Here are a few more terms related to machine learning:
Sure! Here are some additional terms related to databases and data management:
Sure, here are some additional row-related terms and definitions:
Sure, here are some more row-related terms and definitions:
Sure, here are some more terms related to machine learning:
Surrogate Key: An artificial primary key used in a data warehouse to provide a unique identifier for each record, separate from the natural keys found in the source data.
Sweep oar: An oar used in sweep rowing, with one oar per rower.
Sweep rowing: A form of rowing where each rower has one oar, usually longer than a sculling oar, and rows with both hands.
Sweep Rowing: A form of rowing where each rower uses one oar that is much longer than those used in sculling.
Sweep Rowing: A rowing technique in which two oars are used in each hand, as opposed to sculling where one oar is used in each hand.
Sweep rowing: A style of rowing in which each rower has one oar and the boat has a coxswain.
Table: A table is a basic unit of data storage in a relational database, and represents a collection of related data. Tables are made up of rows and columns, where rows represent individual records and columns represent the fields within each record.
Tailwind: A wind that blows in the direction of travel of the rowers, providing an advantage by reducing the resistance of the water.
Text Mining: The process of extracting information and insights from unstructured text data, such as customer reviews or social media posts.
These are some additional terms related to rowing:
Track: The path that the seat slides along in a rowing boat.
Transaction: A transaction is a unit of work that is performed against a database and that must either be completed in its entirety or rolled back in case of an error. Transactions are used to ensure that data remains in a consistent state by allowing multiple operations to be executed as a single, atomic unit of work.
Transaction: A unit of work that is executed on a database, and either completes fully and atomically, or is rolled back and undone, ensuring the consistency and integrity of the database.
Transfer Learning: A technique in machine learning that leverages the knowledge learned from one task to improve the performance on another related task, by using a pre-trained model as a starting point and fine-tuning it on the new data.
Transfer Learning: A technique in machine learning where a model trained on one task is used as a starting point to solve a related but different task, by fine-tuning the model with the new data.
Transfer Learning: A technique in machine learning where a pre-trained model, trained on a large and similar dataset, is fine-tuned or adapted for a new task, using a smaller and different dataset, to leverage the knowledge and feature representations learned from the original task, and improve the performance and efficiency of the new task.
Transfer Learning: A technique used in machine learning, where a pre-trained model on a related task or a large dataset is fine-tuned or adapted to a new task with a smaller dataset, using techniques such as feature extraction or fine-tuning.
Trigger: A trigger is a set of instructions that are automatically executed in response to a specific event, such as the insertion, update, or deletion of data in a table. Triggers are used to enforce business rules, to maintain data consistency and integrity, and to maintain related data in different tables.
Type 1 SCD: A slowly changing dimension in which the new data simply overwrites the old data, discarding the previous data.
Type 2 SCD: A slowly changing dimension in which both the old and the new data are preserved, allowing for historical analysis.
Underfitting: A phenomenon in machine learning where a model is too simple or rigid, and fails to capture the patterns and relationships in the training data, due to high bias, low variance, and insufficient model capacity and flexibility.
Underfitting: A problem in machine learning, where the model is too simple and cannot capture the pattern and the complexity in the data, leading to poor performance and high bias on both the training and the test data.
Unsupervised Learning: A type of machine learning that focuses on learning from unlabeled data, where the goal is to discover hidden patterns and structures in the data, without being told what the outputs should be.
Unsupervised Learning: A type of machine learning where the algorithms are trained on unlabeled data, to discover patterns and relationships within the data, without any prior knowledge of the output labels.
Unsupervised Learning: A type of machine learning where the training data consists of only input samples, without any corresponding target values, and the goal is to learn patterns, relationships, and representations of the data, without any explicit supervision or guidance, by using techniques such as clustering, dimensionality reduction, and generative modeling.
Validation: The process of checking data to ensure it meets certain criteria, such as being in the correct format or having expected values.
View: A view is a virtual table that is based on the result of a query. Views are used to simplify data access, to present data in a specific format, and to provide controlled access to sensitive data.
View: A view is a virtual table that is created by using a SELECT statement. A view is not a physical table, but rather a representation of the data in one or more tables. Views can be used to simplify the complexity of a query by hiding the underlying data structure, or to control access to the data by limiting the columns that are exposed.
Vortex Shedding: The formation of vortices or eddies behind a body as it moves through a fluid, creating drag and reducing efficiency.
Wake: The disturbance in the water created by a moving rowing boat.
Wherry: A type of rowing boat that was traditionally used for carrying goods and passengers on rivers and canals.
Wing Rigger: A support structure on a rowing boat that holds the oarlocks and helps to balance the boat.
Wing Rigger: A type of rigger that places the oarlocks on the gunwales of the boat, giving the rowers more leverage and allowing them to row at a higher rate.