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C5200 | Tokenization is the process of dividing text into a set of meaningful pieces. These pieces are called tokens. Depending on the task at hand, we can define our own conditions to divide the input text into meaningful tokens. | |
C5201 | 11 websites to find free, interesting datasetsFiveThirtyEight. BuzzFeed News. Kaggle. Socrata. Awesome-Public-Datasets on Github. Google Public Datasets. UCI Machine Learning Repository. Data.gov.More items | |
C5202 | How to Find a Sample Size Given a Confidence Interval and Width (unknown population standard deviation)za/2: Divide the confidence interval by two, and look that area up in the z-table: .95 / 2 = 0.475. E (margin of error): Divide the given width by 2. 6% / 2. : use the given percentage. 41% = 0.41. : subtract. from 1. | |
C5203 | Cohen came up with a mechanism to calculate a value which represents the level of agreement between judges negating the agreement by chance. You can see that balls which are agreed on by chance are removed both from agreed and total number of balls. And that is the whole intuition of Kappa value aka Kappa coefficient. | |
C5204 | training set—a subset to train a model. test set—a subset to test the trained model. | |
C5205 | Parameters are key to machine learning algorithms. In this case, a parameter is a function argument that could have one of a range of values. In machine learning, the specific model you are using is the function and requires parameters in order to make a prediction on new data. | |
C5206 | The z-test is best used for greater-than-30 samples because, under the central limit theorem, as the number of samples gets larger, the samples are considered to be approximately normally distributed. | |
C5207 | Correspondence analysis offers a potential means for communication researchers to examine, and better understand, relationships between categorical variables. Though traditionally not commonly used in communication research, potential applications for CA exist. | |
C5208 | 3:258:09Suggested clip · 111 secondsScatterplot - Equation of a Trend Line - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C5209 | Simple linear regression is commonly used in forecasting and financial analysis—for a company to tell how a change in the GDP could affect sales, for example. Microsoft Excel and other software can do all the calculations, but it's good to know how the mechanics of simple linear regression work. | |
C5210 | The purpose of factor analysis is to reduce many individual items into a fewer number of dimensions. Factor analysis can be used to simplify data, such as reducing the number of variables in regression models. | |
C5211 | Definition 1. A statistic d is called an unbiased estimator for a function of the parameter g(θ) provided that for every choice of θ, Eθd(X) = g(θ). Any estimator that not unbiased is called biased. Note that the mean square error for an unbiased estimator is its variance. | |
C5212 | The sparsity of a matrix can be quantified with a score, which is the number of zero values in the matrix divided by the total number of elements in the matrix. sparsity = count zero elements / total elements. 1. sparsity = count zero elements / total elements. Below is an example of a small 3 x 6 sparse matrix. | |
C5213 | To plot the learning curves, we need only a single error score per training set size, not 5.The learning_curve() function from scikit-learnDo the required imports from sklearn .Declare the features and the target.Use learning_curve() to generate the data needed to plot a learning curve. | |
C5214 | A confusion matrix is nothing but a table with two dimensions viz. “ Actual” and “Predicted” and furthermore, both the dimensions have “True Positives (TP)”, “True Negatives (TN)”, “False Positives (FP)”, “False Negatives (FN)” as shown below − | |
C5215 | A loss function is used to optimize the parameter values in a neural network model. Loss functions map a set of parameter values for the network onto a scalar value that indicates how well those parameter accomplish the task the network is intended to do. | |
C5216 | Algorithms are always unambiguous and are used as specifications for performing calculations, data processing, automated reasoning, and other tasks. As an effective method, an algorithm can be expressed within a finite amount of space and time, and in a well-defined formal language for calculating a function. | |
C5217 | Thus, the t-statistic measures how many standard errors the coefficient is away from zero. Generally, any t-value greater than +2 or less than – 2 is acceptable. The higher the t-value, the greater the confidence we have in the coefficient as a predictor. | |
C5218 | A confusion matrix, also known as error matrix is a table layout that is used to visualize the performance of a classification model where the true values are already known. | |
C5219 | The variables used to explain variations in the level of education are called exogenous. More generally, the variables that show differences we wish to explain are called endogenous, while the variables used to explain the differences are called exogenous. Often this goes along with a causal imagery. | |
C5220 | To calculate the standard error, follow these steps:Record the number of measurements (n) and calculate the sample mean (μ). Calculate how much each measurement deviates from the mean (subtract the sample mean from the measurement).Square all the deviations calculated in step 2 and add these together:More items• | |
C5221 | In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own. | |
C5222 | Information provides a way to quantify the amount of surprise for an event measured in bits. Entropy provides a measure of the average amount of information needed to represent an event drawn from a probability distribution for a random variable. | |
C5223 | The term convolution refers to the mathematical combination of two functions to produce a third function. It merges two sets of information. In the case of a CNN, the convolution is performed on the input data with the use of a filter or kernel (these terms are used interchangeably) to then produce a feature map. | |
C5224 | To measure test-retest reliability, you conduct the same test on the same group of people at two different points in time. Then you calculate the correlation between the two sets of results. | |
C5225 | Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training. Unlabeled data, when used in conjunction with a small amount of labeled data, can produce considerable improvement in learning accuracy. | |
C5226 | To write a null hypothesis, first start by asking a question. Rephrase that question in a form that assumes no relationship between the variables. In other words, assume a treatment has no effect. Write your hypothesis in a way that reflects this. | |
C5227 | Abstract. Network representation learning aims to embed the vertexes in a network into low-dimensional dense representations, in which similar vertices in the network should have “close” representations (usually measured by cosine similarity or Euclidean distance of their representations). | |
C5228 | A sampling frame is a list of all the items in your population. It's a complete list of everyone or everything you want to study. The difference between a population and a sampling frame is that the population is general and the frame is specific. | |
C5229 | False positive rate (FPR) is a measure of accuracy for a test: be it a medical diagnostic test, a machine learning model, or something else. In technical terms, the false positive rate is defined as the probability of falsely rejecting the null hypothesis. | |
C5230 | In signal processing, the Fourier transform can reveal important characteristics of a signal, namely, its frequency components. y k + 1 = ∑ j = 0 n - 1 ω j k x j + 1 . ω = e - 2 π i / n is one of n complex roots of unity where i is the imaginary unit. For x and y , the indices j and k range from 0 to n - 1 . | |
C5231 | Naive Bayes uses a similar method to predict the probability of different class based on various attributes. This algorithm is mostly used in text classification and with problems having multiple classes. | |
C5232 | According to Bezdek (1994), Computational Intelligence is a subset of Artificial Intelligence. There are two types of machine intelligence: the artificial one based on hard computing techniques and the computational one based on soft computing methods, which enable adaptation to many situations. | |
C5233 | The F-distribution arises from inferential statistics concerning population variances. More specifically, we use an F-distribution when we are studying the ratio of the variances of two normally distributed populations. | |
C5234 | The terms 'multivariate analysis' and 'multivariable analysis' are often used interchangeably in medical and health sciences research. However, multivariate analysis refers to the analysis of multiple outcomes whereas multivariable analysis deals with only one outcome each time [1]. | |
C5235 | The global facial recognition market size was valued at USD 3.4 billion in 2019 and is anticipated to expand at a CAGR of 14.5% from 2020 to 2027. The technology is improving, evolving, and expanding at an explosive rate. Technologies such as biometrics are extensively used in order to enhance security. | |
C5236 | To tell briefly, LDA imagines a fixed set of topics. Each topic represents a set of words. And the goal of LDA is to map all the documents to the topics in a way, such that the words in each document are mostly captured by those imaginary topics. | |
C5237 | Big Data is defined as data that is huge in size. Bigdata is a term used to describe a collection of data that is huge in size and yet growing exponentially with time. Examples of Big Data generation includes stock exchanges, social media sites, jet engines, etc. | |
C5238 | “The distinction between white label and private label are subtle,” he writes. “That's why these terms are so easily confused. Private label is a brand sold exclusively in one retailer, for example, Equate (WalMart). White label is a generic product, which is sold to multiple retailers like generic ibuprofen (Advil).” | |
C5239 | Model calibration is the process of adjustment of the model parameters and forcing within the margins of the uncertainties (in model parameters and / or model forcing) to obtain a model representation of the processes of interest that satisfies pre-agreed criteria (Goodness-of-Fit or Cost Function). | |
C5240 | The word2vec algorithm uses a neural network model to learn word associations from a large corpus of text. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. | |
C5241 | Entropy is a measure of randomness and disorder; high entropy means high disorder and low energy. As chemical reactions reach a state of equilibrium, entropy increases; and as molecules at a high concentration in one place diffuse and spread out, entropy also increases. | |
C5242 | Accuracy refers to how close measurements are to the "true" value, while precision refers to how close measurements are to each other. | |
C5243 | Differs from a true experiment in that the researchers do not have full experimental control. A quasi-experimental study that has at least one treatment group and one comparison group, but participants have not been randomly assigned to the 2 groups. You just studied 12 terms! | |
C5244 | An example of a nonlinear classifier is kNN. The decision boundaries of kNN (the double lines in Figure 14.6 ) are locally linear segments, but in general have a complex shape that is not equivalent to a line in 2D or a hyperplane in higher dimensions. | |
C5245 | GRU use less training parameters and therefore use less memory, execute faster and train faster than LSTM's whereas LSTM is more accurate on dataset using longer sequence. In short, if sequence is large or accuracy is very critical, please go for LSTM whereas for less memory consumption and faster operation go for GRU. | |
C5246 | When analyzing unstructured data and integrating the information with its structured counterpart, keep the following in mind:Choose the End Goal. Select Method of Analytics. Identify All Data Sources. Evaluate Your Technology. Get Real-Time Access. Use Data Lakes. Clean Up the Data. Retrieve, Classify and Segment Data.More items• | |
C5247 | R-squared is a goodness-of-fit measure for linear regression models. This statistic indicates the percentage of the variance in the dependent variable that the independent variables explain collectively. After fitting a linear regression model, you need to determine how well the model fits the data. | |
C5248 | Scikit Learn is a new easy-to-use interface for TensorFlow from Google based on the Scikit-learn fit/predict model. | |
C5249 | Generative adversarial networks (GANs) are an exciting recent innovation in machine learning. GANs are generative models: they create new data instances that resemble your training data. For example, GANs can create images that look like photographs of human faces, even though the faces don't belong to any real person. | |
C5250 | The idea behind dimensional analysis is that if an equation is to make sense, it must be dimensionally consistent, unlike the one above. However it doesn't work always - particularly when we're dealing with relations between more than 2 terms. | |
C5251 | Eigenvectors are a special set of vectors associated with a linear system of equations (i.e., a matrix equation) that are sometimes also known as characteristic vectors, proper vectors, or latent vectors (Marcus and Minc 1988, p. Each eigenvector is paired with a corresponding so-called eigenvalue. | |
C5252 | It is a primary goal of some artificial intelligence research and a common topic in science fiction and futures studies. AGI can also be referred to as strong AI, full AI, or general intelligent action. Some academic sources reserve the term "strong AI" for machines that can experience consciousness. | |
C5253 | Each observation in a time series can be forecast using all previous observations. We call these fitted values and they are denoted by ^yt|t−1 y ^ t | t − 1 , meaning the forecast of yt based on observations y1,…,yt−1 y 1 , … , y t − 1 . | |
C5254 | All three went to the same coaching institute, Allen, and were part of an elite Special Rankers Group (SRG) of 18 students. Belief in the "positive" effect of stress seemed almost a religion at Allen Jaipur. | |
C5255 | For example, if the researcher wanted a sample of 50,000 graduates using age range, the proportionate stratified random sample will be obtained using this formula: (sample size/population size) x stratum size. The table below assumes a population size of 180,000 MBA graduates per year. | |
C5256 | Federated learning enables multiple actors to build a common, robust machine learning model without sharing data, thus allowing to address critical issues such as data privacy, data security, data access rights and access to heterogeneous data. | |
C5257 | In Gradient Descent (GD), we perform the forward pass using ALL the train data before starting the backpropagation pass to adjust the weights. This is called (one epoch). In Stochastic Gradient Descent (SGD), we perform the forward pass using a SUBSET of the train set followed by backpropagation to adjust the weights. | |
C5258 | To calculate the variance follow these steps: Work out the Mean (the simple average of the numbers) Then for each number: subtract the Mean and square the result (the squared difference). Then work out the average of those squared differences. | |
C5259 | High Pass RL Filter An inductor, like a capacitor, is a reactive device. And this is why this circuit is a high-pass filter circuit. Low frequency signals, however, will go through the inductor, because inductors offer very low resistance to low-frequency, or Dc, signals. | |
C5260 | Variational Bayesian methods are primarily used for two purposes: To provide an analytical approximation to the posterior probability of the unobserved variables, in order to do statistical inference over these variables. | |
C5261 | Systematic sampling involves selecting fixed intervals from the larger population to create the sample. Cluster sampling divides the population into groups, then takes a random sample from each cluster. | |
C5262 | Offline evaluations test the effectiveness of recommender system algorithms on a certain dataset. Online evaluation attempts to evaluate recommender systems by a method called A/B testing where a part of users are served by recommender system A and the another part of users by recommender system B. | |
C5263 | Test for Significance of Regression. The test for significance of regression in the case of multiple linear regression analysis is carried out using the analysis of variance. The test is used to check if a linear statistical relationship exists between the response variable and at least one of the predictor variables. | |
C5264 | The standard use of “rollout” (also called a “playout”) is in regard to an execution of a policy from the current state when there is some uncertainty about the next state or outcome - it is one simulation from your current state. | |
C5265 | If μ is the average number of successes occurring in a given time interval or region in the Poisson distribution. Then the mean and the variance of the Poisson distribution are both equal to μ. Remember that, in a Poisson distribution, only one parameter, μ is needed to determine the probability of any given event. | |
C5266 | In an upper-tailed test the decision rule has investigators reject H0 if the test statistic is larger than the critical value. In a lower-tailed test the decision rule has investigators reject H0 if the test statistic is smaller than the critical value. | |
C5267 | Thus, the eigenvalues of a unitary matrix are unimodular, that is, they have norm 1, and hence can be written as eiα e i α for some α. α . U|v⟩=eiλ|v⟩,U|w⟩=eiμ|w⟩. | |
C5268 | While most PCs have a single operating system (OS) built-in, it's also possible to run two operating systems on one computer at the same time. The process is known as dual-booting, and it allows users to switch between operating systems depending on the tasks and programs they're working with. | |
C5269 | Two determine if two images are rotated versions of each other, one can either exhaustively rotate them in order to find out if the two match up at some angle, or alternatively extract features from the images that can then be compared to make the same decision. | |
C5270 | Gradient Descent is the most common optimization algorithm in machine learning and deep learning. On each iteration, we update the parameters in the opposite direction of the gradient of the objective function J(w) w.r.t the parameters where the gradient gives the direction of the steepest ascent. | |
C5271 | Linear regression is the most basic and commonly used predictive analysis. Regression estimates are used to describe data and to explain the relationship between one dependent variable and one or more independent variables. | |
C5272 | Uniform quantization may lead to either slope overload distortion or Granular noise. Thus we go for Non Uniform quantization because step size varies based on the message signal and it will be tracked with minimal amount of error. | |
C5273 | What they are & why they matter. Neural networks are computing systems with interconnected nodes that work much like neurons in the human brain. Using algorithms, they can recognize hidden patterns and correlations in raw data, cluster and classify it, and – over time – continuously learn and improve. | |
C5274 | 14:3148:20Suggested clip · 105 secondsMod-13 Lec-46 The Adjoint Operator - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C5275 | Business Analytics. Schools and Partners: ColumbiaX. MicroMasters ® Program (4 courses)Big Data. Schools and Partners: AdelaideX. MicroMasters ® Program (5 courses)Predictive Analytics using. Python. Schools and Partners: EdinburghX. MicroMasters ® Program (5 courses) | |
C5276 | The following are common methods:Mean imputation. Simply calculate the mean of the observed values for that variable for all individuals who are non-missing. Substitution. Hot deck imputation. Cold deck imputation. Regression imputation. Stochastic regression imputation. Interpolation and extrapolation. | |
C5277 | Ensemble learning combines the predictions from multiple neural network models to reduce the variance of predictions and reduce generalization error. Techniques for ensemble learning can be grouped by the element that is varied, such as training data, the model, and how predictions are combined. | |
C5278 | Generative Adversarial Networks takes up a game-theoretic approach, unlike a conventional neural network. The network learns to generate from a training distribution through a 2-player game. The two entities are Generator and Discriminator. These two adversaries are in constant battle throughout the training process. | |
C5279 | 2. Exponential Moving Average (EMA) The other type of moving average is the exponential moving average (EMA), which gives more weight to the most recent price points to make it more responsive to recent data points. | |
C5280 | The “trick” is that kernel methods represent the data only through a set of pairwise similarity comparisons between the original data observations x (with the original coordinates in the lower dimensional space), instead of explicitly applying the transformations ϕ(x) and representing the data by these transformed | |
C5281 | In Bayesian statistics, a maximum a posteriori probability (MAP) estimate is an estimate of an unknown quantity, that equals the mode of the posterior distribution. The MAP can be used to obtain a point estimate of an unobserved quantity on the basis of empirical data. | |
C5282 | Gradient Descent is an optimization algorithm for finding a local minimum of a differentiable function. You start by defining the initial parameter's values and from there gradient descent uses calculus to iteratively adjust the values so they minimize the given cost-function. | |
C5283 | Random sampling ensures that results obtained from your sample should approximate what would have been obtained if the entire population had been measured (Shadish et al., 2002). The simplest random sample allows all the units in the population to have an equal chance of being selected. | |
C5284 | If at the limit n → ∞ the estimator tend to be always right (or at least arbitrarily close to the target), it is said to be consistent. This notion is equivalent to convergence in probability defined below. | |
C5285 | Tests of Correlation: The validity of a test is measured by the strength of association, or correlation, between the results obtained by the test and by the criterion measure. | |
C5286 | A permutation test (also called a randomization test, re-randomization test, or an exact test) is a type of statistical significance test in which the distribution of the test statistic under the null hypothesis is obtained by calculating all possible values of the test statistic under all possible rearrangements of | |
C5287 | How do you create a decision tree?Start with your overarching objective/“big decision” at the top (root) Draw your arrows. Attach leaf nodes at the end of your branches. Determine the odds of success of each decision point. Evaluate risk vs reward. | |
C5288 | The statistics are presented in a definite form so they also help in condensing the data into important figures. So statistical methods present meaningful information. In other words statistics helps in simplifying complex data to simple-to make them understandable. | |
C5289 | Neural style transferTable of contents.Setup. Import and configure modules.Visualize the input.Fast Style Transfer using TF-Hub.Define content and style representations.Build the model.Calculate style.Extract style and content.More items | |
C5290 | The k-means clustering algorithm is one of the most widely used, effective, and best understood clustering methods. In this paper we propose a supervised learning approach to finding a similarity measure so that k-means provides the desired clusterings for the task at hand. | |
C5291 | Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. To find a local minimum of a function using gradient descent, we take steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point. | |
C5292 | The One-Class SVM A One-Class Support Vector Machine is an unsupervised learning algorithm that is trained only on the 'normal' data, in our case the negative examples. It learns the boundaries of these points and is therefore able to classify any points that lie outside the boundary as, you guessed it, outliers. | |
C5293 | "A Bayesian network is a probabilistic graphical model which represents a set of variables and their conditional dependencies using a directed acyclic graph." It is also called a Bayes network, belief network, decision network, or Bayesian model. | |
C5294 | If you select View/Residual Diagnostics/Correlogram-Q-statistics on the equation toolbar, EViews will display the autocorrelation and partial autocorrelation functions of the residuals, together with the Ljung-Box Q-statistics for high-order serial correlation. | |
C5295 | Nonresponse bias occurs when some respondents included in the sample do not respond. The key difference here is that the error comes from an absence of respondents instead of the collection of erroneous data. Most often, this form of bias is created by refusals to participate or the inability to reach some respondents. | |
C5296 | A discrete random variable has a countable number of possible values. The probability of each value of a discrete random variable is between 0 and 1, and the sum of all the probabilities is equal to 1. A continuous random variable takes on all the values in some interval of numbers. | |
C5297 | The most important difference between deep learning and traditional machine learning is its performance as the scale of data increases. When the data is small, deep learning algorithms don't perform that well. This is because deep learning algorithms need a large amount of data to understand it perfectly. | |
C5298 | Stratified random sampling is a method of sampling that involves the division of a population into smaller sub-groups known as strata. In stratified random sampling, or stratification, the strata are formed based on members' shared attributes or characteristics such as income or educational attainment. | |
C5299 | Optimizers are algorithms or methods used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses. Optimization algorithms or strategies are responsible for reducing the losses and to provide the most accurate results possible. |
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