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convolutional neural network (CNN) : class of neural network models developed to process structured, grid-like data, such as images, making use of the mathematical operation of convolution | https://openstax.org/books/principles-data-science/pages/7-key-terms |
deep learning : training and implementation of neural networks with many layers to learn hierarchical (structured) representations of data | https://openstax.org/books/principles-data-science/pages/7-key-terms |
deepfake : product of an AI system that seem realistic, created with malicious intent to mislead people | https://openstax.org/books/principles-data-science/pages/7-key-terms |
depth : number of hidden layers in a neural network | https://openstax.org/books/principles-data-science/pages/7-key-terms |
dimension : the number of components in a vector | https://openstax.org/books/principles-data-science/pages/7-key-terms |
dynamic backpropagation : adjustment of parameters (weights and biases) and the underlying structure (neurons, layers, connections, etc.) in the training of a neural network | https://openstax.org/books/principles-data-science/pages/7-key-terms |
epoch : single round of training of a neural network using the entire training set (or a batch thereof) | https://openstax.org/books/principles-data-science/pages/7-key-terms |
exploding gradient problem : failure to train an RNN due to instability introduced by having connecting weights at values larger than 1 | https://openstax.org/books/principles-data-science/pages/7-key-terms |
feature map : output of convolutional layers in a CNN, representing the learned features of the input data | https://openstax.org/books/principles-data-science/pages/7-key-terms |
feedback loop : internal connection from one neuron to itself or among multiple neurons in a cycle | https://openstax.org/books/principles-data-science/pages/7-key-terms |
fully connected layers : layers of a neural network in which every neuron in one layer is connected to every neuron in the next layer | https://openstax.org/books/principles-data-science/pages/7-key-terms |
generative art : use of AI tools to enhance or create new artistic works | https://openstax.org/books/principles-data-science/pages/7-key-terms |
gradient descent : method for locating minimum values of a multivariable function using small steps in the direction of greatest decrease from a given point | https://openstax.org/books/principles-data-science/pages/7-key-terms |
hallucinations : in the context of NLP, AI-generated responses that have no basis in reality | https://openstax.org/books/principles-data-science/pages/7-key-terms |
hidden layers : layers between the input and output layers | https://openstax.org/books/principles-data-science/pages/7-key-terms |
hinge loss : loss function commonly used in binary classification tasks:â1nâi=1nmax(0,1âyiy^i)â1nâi=1nmax(0,1âyiy^i) | https://openstax.org/books/principles-data-science/pages/7-key-terms |
hyperbolic tangent (tanh) : common activation function,tanhx=exâeâxex+eâxtanhx=exâeâxex+eâx | https://openstax.org/books/principles-data-science/pages/7-key-terms |
imbalanced data : datasets that contain significantly more data points of one class than another class | https://openstax.org/books/principles-data-science/pages/7-key-terms |
input layer : neurons that accept the initial input data | https://openstax.org/books/principles-data-science/pages/7-key-terms |
large language model (LLM) : powerful natural language processing model designed to understand and generate humanlike text based on massive amounts of training data | https://openstax.org/books/principles-data-science/pages/7-key-terms |
leaky ReLU : common activation function,LReLU(x)=max(cx,x)LReLU(x)=max(cx,x), for some small positive parametercc | https://openstax.org/books/principles-data-science/pages/7-key-terms |
long short-term memory (LSTM) network : type of RNN incorporatingmemory cellsthat can capture long-term dependencies | https://openstax.org/books/principles-data-science/pages/7-key-terms |
loss (or cost) function : measure of error between the predicted output and the actual target values for a neural network | https://openstax.org/books/principles-data-science/pages/7-key-terms |
margin : measure of the separation of data points belonging to different classifications | https://openstax.org/books/principles-data-science/pages/7-key-terms |
memory cells : internal structures that allow the network to store and access information over long time intervals | https://openstax.org/books/principles-data-science/pages/7-key-terms |
multilayer perceptron (MLP) : basic paradigm for neural networks having multiple hidden layers | https://openstax.org/books/principles-data-science/pages/7-key-terms |
natural language processing (NLP) : area of AI concerned with recognizing written or spoken language and generating new language content | https://openstax.org/books/principles-data-science/pages/7-key-terms |
neural network : structure made up of neurons that takes in input and produces output that classifies the input information | https://openstax.org/books/principles-data-science/pages/7-key-terms |
neuron : individual decision-making unit of a neural network that takes some number of inputs and produces an output | https://openstax.org/books/principles-data-science/pages/7-key-terms |
nonlinear : not linear; that is, not of the formf(x)=mx+bf(x)=mx+b | https://openstax.org/books/principles-data-science/pages/7-key-terms |
output layer : neurons that are used to interpret the answer or give classification information | https://openstax.org/books/principles-data-science/pages/7-key-terms |
perceptron : single-layer neural network using the step function as activation function, designed for binary classification tasks | https://openstax.org/books/principles-data-science/pages/7-key-terms |
pooling layers : layers of a CNN that reduce the dimensions of data coming from the feature maps produced by the convolutional layers while retaining important information | https://openstax.org/books/principles-data-science/pages/7-key-terms |
rectified linear unit (ReLU) : common activation function,ReLU(x)=max(0,x)ReLU(x)=max(0,x) | https://openstax.org/books/principles-data-science/pages/7-key-terms |
recurrent neural network (RNN) : neural network that incorporates feedback loops | https://openstax.org/books/principles-data-science/pages/7-key-terms |
responsible AI : ethical and socially conscious development and deployment of artificial intelligence systems | https://openstax.org/books/principles-data-science/pages/7-key-terms |
semantic segmentation : process of partitioning a digital image into multiple components by classifying each pixel of an image into a specific category or class | https://openstax.org/books/principles-data-science/pages/7-key-terms |
sigmoid function : common activation function,Ï(x)=11+eâxÏ(x)=11+eâx | https://openstax.org/books/principles-data-science/pages/7-key-terms |
softmax : activation function that takes a vector of real-number values and yields a vector of values scaled into the interval between 0 and 1, which can be interpreted as discrete probability distribution | https://openstax.org/books/principles-data-science/pages/7-key-terms |
softplus : common activation function,f(x)=ln(1+ex)f(x)=ln(1+ex) | https://openstax.org/books/principles-data-science/pages/7-key-terms |
sparse categorical cross entropy : generalization of binary cross entropy, useful when the target labels are integers | https://openstax.org/books/principles-data-science/pages/7-key-terms |
static backpropagation : adjustment of parameters (weights and biases) only in the training of a neural network | https://openstax.org/books/principles-data-science/pages/7-key-terms |
step function : function that returns 0 when input is below a threshold and returns 1 when input is above the threshold | https://openstax.org/books/principles-data-science/pages/7-key-terms |
tensor : multidimensional array, generalizing the concept of vector | https://openstax.org/books/principles-data-science/pages/7-key-terms |
vanishing gradient problem : failure to train an RNN due to very slow learning rates caused by having connecting weights at values smaller than 1 | https://openstax.org/books/principles-data-science/pages/7-key-terms |
vector : ordered list of numbers,x=(x1,x2,â¦,xn)x=(x1,x2,â¦,xn) | https://openstax.org/books/principles-data-science/pages/7-key-terms |
weight : valuewthat is multiplied to the incoming signal, essentially determining the strength of the connection | https://openstax.org/books/principles-data-science/pages/7-key-terms |
anonymization : act of removing personal identifying information from datasets and other forms of data to make sensitive information usable for analysis without the risk of exposing personal information | https://openstax.org/books/principles-data-science/pages/8-key-terms |
anonymous data : data that has been stripped of personally identifiable information (or never contained such information in the first place) | https://openstax.org/books/principles-data-science/pages/8-key-terms |
autonomy : in data science, the ideal that individuals maintain control over the decisions regarding the collection and use of their data | https://openstax.org/books/principles-data-science/pages/8-key-terms |
confidentiality : safeguarding of privacy and security of data by controlling access to it | https://openstax.org/books/principles-data-science/pages/8-key-terms |
cookies : small data files from websites that are deposited on usersâ hard disk to keep track of browsing and search history and to collect information about potential interests to tailor advertisements and product placement on websites | https://openstax.org/books/principles-data-science/pages/8-key-terms |
copyright : protection under the law for original creative work | https://openstax.org/books/principles-data-science/pages/8-key-terms |
cross-validation : comparison of the results of a model with different subsets of the data or with the entire dataset by repeatedly breaking the data into training and testing sets and evaluating the model's performance on different subsets of the data | https://openstax.org/books/principles-data-science/pages/8-key-terms |
data breach : the act of data being stolen by a malicious third party | https://openstax.org/books/principles-data-science/pages/8-key-terms |
data governance protocols : set of rules, policies, and procedures that enable precise control over data access while ensuring that it is safeguarded | https://openstax.org/books/principles-data-science/pages/8-key-terms |
data privacy : the assurance that individual data is collected, processed, and stored securely with respect for individuals' rights and preferences | https://openstax.org/books/principles-data-science/pages/8-key-terms |
data retention : how long personal data may be stored | https://openstax.org/books/principles-data-science/pages/8-key-terms |
data security : steps taken to keep data secure from unauthorized access or manipulation | https://openstax.org/books/principles-data-science/pages/8-key-terms |
data sharing : processes of allowing access to or transferring data from one entity (individual, organization, or system) to another | https://openstax.org/books/principles-data-science/pages/8-key-terms |
data source attribution : the practice of clearly identifying and acknowledging the sources employed in the visualizations and reporting of data | https://openstax.org/books/principles-data-science/pages/8-key-terms |
data sovereignty : laws that require data collected from a countryâs citizens to be stored and processed within its borders | https://openstax.org/books/principles-data-science/pages/8-key-terms |
digital divide : gap between those who have access to digital technologies, such as the internet and computers, and those who do not | https://openstax.org/books/principles-data-science/pages/8-key-terms |
encryption : the process of converting sensitive or confidential data into a code in order to protect it from unauthorized access or interception | https://openstax.org/books/principles-data-science/pages/8-key-terms |
ethics in data science : responsible collection, analysis, use, and dissemination of data | https://openstax.org/books/principles-data-science/pages/8-key-terms |
explainable AI (XAI) : set of processes, methodologies, and techniques designed to make artificial intelligence (AI) models, particularly complex ones like deep learning models, more understandable and interpretable to humans | https://openstax.org/books/principles-data-science/pages/8-key-terms |
fairness : absence of bias in the models and algorithms used to process data | https://openstax.org/books/principles-data-science/pages/8-key-terms |
Family Educational Rights and Privacy Act (FERPA) : legislation providing protections for student educational records and defining certain rights for parents regarding their childrenâs records | https://openstax.org/books/principles-data-science/pages/8-key-terms |
hashing : process of transforming data into a fixed-length value or string (called a hash), typically using an algorithm called a hash function | https://openstax.org/books/principles-data-science/pages/8-key-terms |
Health Insurance Portability and Accountability Act (HIPAA) : U.S. legislation requiring the safeguarding of sensitive information related to patient health | https://openstax.org/books/principles-data-science/pages/8-key-terms |
informed consent : the process of obtaining permission from a research subject indicating that they understand the scope of collecting data | https://openstax.org/books/principles-data-science/pages/8-key-terms |
intellectual property : original artistic works, trademarks and trade secrets, patents, and other creative output | https://openstax.org/books/principles-data-science/pages/8-key-terms |
k-anonymization : principle of ensuring that each record within a dataset is indistinguishable from at leastkâ 1 other records with respect to a specified set of identifying attributes or features | https://openstax.org/books/principles-data-science/pages/8-key-terms |
outlier detection : identification of observations that are significantly different from the rest of the data | https://openstax.org/books/principles-data-science/pages/8-key-terms |
personally identifiable information (PII) : information that directly and unambiguously identifies an individual | https://openstax.org/books/principles-data-science/pages/8-key-terms |
pseudonymization : act of replacing sensitive information in a dataset with artificial identifiers or codes while still maintaining its usefulness for analysis | https://openstax.org/books/principles-data-science/pages/8-key-terms |
regulatory compliance officer (RCO) : a trained individual responsible for confirming that a company or organization follows the laws, regulations, and policies that rule its functions to avoid legal and financial risks | https://openstax.org/books/principles-data-science/pages/8-key-terms |
transparency : being open and honest about how data is collected, stored, and used | https://openstax.org/books/principles-data-science/pages/8-key-terms |
universal design principles : set of guidelines aimed at creating products, environments, and systems that are accessible and usable by all people regardless of age, ability, or disability | https://openstax.org/books/principles-data-science/pages/8-key-terms |
3D visualization : a graph or display that shows information plotted along three dimensions, typically referred to as the x-axis, y-axis, and z-axis | https://openstax.org/books/principles-data-science/pages/9-key-terms |
bar graph : a chart that presents categorical data in a summarized form based on frequency or relative frequency | https://openstax.org/books/principles-data-science/pages/9-key-terms |
bin : an interval or range into which data points are grouped; often used to create histograms | https://openstax.org/books/principles-data-science/pages/9-key-terms |
binomial distribution : a probability distribution for discrete random variables where there are only two possible outcomes of an experiment | https://openstax.org/books/principles-data-science/pages/9-key-terms |
bivariate data : paired data in which each value of one variable is paired with a value of a second variable | https://openstax.org/books/principles-data-science/pages/9-key-terms |
boxplot (âbox-and-whisker plotâ) : a graphical display showing the five-number summary for a dataset: the min, first quartile, median, third quartile, and the max | https://openstax.org/books/principles-data-science/pages/9-key-terms |
choropleth graph : a graphical display where areas are shaded in proportion to the value of a variable being represented; choropleth maps are typically used to present spatial patterns in geographic regions | https://openstax.org/books/principles-data-science/pages/9-key-terms |
correlation heatmap : a visual representation of the correlation matrix that implements color coding to visualize those variables with stronger correlations and those variables with weaker correlations. | https://openstax.org/books/principles-data-science/pages/9-key-terms |
data visualization : the use of graphical displays, such as bar charts, histograms, and scatterplots, to help interpret patterns and trends in a dataset | https://openstax.org/books/principles-data-science/pages/9-key-terms |
discrete random variable : a random variable where there is only a finite number of values that the variable can take on | https://openstax.org/books/principles-data-science/pages/9-key-terms |
five-number summary : a summary of a dataset that includes the minimum, first quartile, median, third quartile, and maximum | https://openstax.org/books/principles-data-science/pages/9-key-terms |
geospatial data : data that describes the geographic location, shape, size, and other attributes relative to a location on the Earth's surface | https://openstax.org/books/principles-data-science/pages/9-key-terms |
Geospatial Information System (GIS) mapping : a tool for visualizing, analyzing, and interpreting spatial data that makes use of various types of geographical data, such as maps and satellite images | https://openstax.org/books/principles-data-science/pages/9-key-terms |
grid heatmap : a graphical representation of data where values are depicted as colors within a grid such as an (x,y) mapping; a grid heatmap is typically used to show correlations between two quantities | https://openstax.org/books/principles-data-science/pages/9-key-terms |
histogram : a graphical display of continuous data showing class intervals on the horizontal axis and frequency or relative frequency on the vertical axis | https://openstax.org/books/principles-data-science/pages/9-key-terms |
interquartile range (IQR) : a number that indicates the spread of the middle half, or middle 50%, of the data; the difference between the third quartile (Q3Q3) and the first quartile (Q1Q1) | https://openstax.org/books/principles-data-science/pages/9-key-terms |
line chart : a type of graph that uses lines to connect(x,y)(x,y)data points | https://openstax.org/books/principles-data-science/pages/9-key-terms |
median : the middle value in an ordered dataset | https://openstax.org/books/principles-data-science/pages/9-key-terms |
normal distribution : a bell-shaped distribution curve that is used to model many measurements, including IQ scores, salaries, heights, weights, blood pressures, etc. | https://openstax.org/books/principles-data-science/pages/9-key-terms |
outliers : data values that are significantly different from the other data values in a dataset | https://openstax.org/books/principles-data-science/pages/9-key-terms |
Pareto chart : a type of bar chart where the bars are arranged in order of decreasing height | https://openstax.org/books/principles-data-science/pages/9-key-terms |
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