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C6200 | In neural networks, a hidden layer is located between the input and output of the algorithm, in which the function applies weights to the inputs and directs them through an activation function as the output. In short, the hidden layers perform nonlinear transformations of the inputs entered into the network. | |
C6201 | DEFINITION 1. Given a set of active nodes and an ordering on active nodes, amorphous data-parallelism is the parallelism that arises from simultaneously processing active nodes, subject to neighborhood and ordering constraints. | |
C6202 | Inference over a Bayesian network can come in two forms. The first is simply evaluating the joint probability of a particular assignment of values for each variable (or a subset) in the network. We would calculate P(¬x | e) in the same fashion, just setting the value of the variables in x to false instead of true. | |
C6203 | The Random Variable is X = "The sum of the scores on the two dice". Let's count how often each value occurs, and work out the probabilities: 2 occurs just once, so P(X = 2) = 1/36. 3 occurs twice, so P(X = 3) = 2/36 = 1/18. | |
C6204 | Popular algorithms that can be used for binary classification include:Logistic Regression.k-Nearest Neighbors.Decision Trees.Support Vector Machine.Naive Bayes. | |
C6205 | The mean is the arithmetic average of a set of numbers, or distribution. A mean is computed by adding up all the values and dividing that score by the number of values. The Median is the number found at the exact middle of the set of values. | |
C6206 | A confusion matrix is a table that is often used to describe the performance of a classification model (or "classifier") on a set of test data for which the true values are known. The confusion matrix itself is relatively simple to understand, but the related terminology can be confusing. | |
C6207 | The exponential distribution is in continuous time what the geometric distribution is in discrete time. A positive integer random variable X has the geometric distribution with parameter p ∈ (0, 1] if: P(X = n) = p(1 − p)n−1, ∀n ≥ 1, or, equivalently, if: P(X>n) = (1 − p)n, ∀n ∈ N. | |
C6208 | Data augmentation is a strategy that enables practitioners to significantly increase the diversity of data available for training models, without actually collecting new data. Data augmentation techniques such as cropping, padding, and horizontal flipping are commonly used to train large neural networks. | |
C6209 | The Upsampling layer is a simple layer with no weights that will double the dimensions of input and can be used in a generative model when followed by a traditional convolutional layer. | |
C6210 | Decision trees is a non-linear classifier like the neural networks, etc. It is generally used for classifying non-linearly separable data. Even when you consider the regression example, decision tree is non-linear. | |
C6211 | K-S should be a high value (Max =1.0) when the fit is good and a low value (Min = 0.0) when the fit is not good. When the K-S value goes below 0.05, you will be informed that the Lack of fit is significant." I'm trying to get a limit value, but it's not very easy. | |
C6212 | A chi-square goodness-of-fit test can be conducted when there is one categorical variable with more than two levels. If there are exactly two categories, then a one proportion z test may be conducted. The levels of that categorical variable must be mutually exclusive. | |
C6213 | RELU activation solves this by having a gradient slope of 1, so during backpropagation, there isn't gradients passed back that are progressively getting smaller and smaller. but instead they are staying the same, which is how RELU solves the vanishing gradient problem. | |
C6214 | As a simple definition, linear function is a function which has same derivative for the inputs in its domain. ReLU is not linear. The simple answer is that ReLU 's output is not a straight line, it bends at the x-axis. | |
C6215 | A model is considered to be robust if its output and forecasts are consistently accurate even if one or more of the input variables or assumptions are drastically changed due to unforeseen circumstances. | |
C6216 | According to the central limit theorem, the mean of a sample of data will be closer to the mean of the overall population in question, as the sample size increases, notwithstanding the actual distribution of the data. In other words, the data is accurate whether the distribution is normal or aberrant. | |
C6217 | Simple logistic regression analysis refers to the regression application with one dichotomous outcome and one independent variable; multiple logistic regression analysis applies when there is a single dichotomous outcome and more than one independent variable. | |
C6218 | If a build-in function can be applied to a complete array, a vectorization is much faster than a loop appraoch. When large temporary arrays are required, the benefits of the vectorization can be dominated by the expensive allocation of the memory, when it does not match into the processor cache. | |
C6219 | In one shot learning, you get only 1 or a few training examples in some categories. In zero shot learning, you are not presented with every class label in training. So in some categories, you get 0 training examples. | |
C6220 | The Mann Whitney U test, sometimes called the Mann Whitney Wilcoxon Test or the Wilcoxon Rank Sum Test, is used to test whether two samples are likely to derive from the same population (i.e., that the two populations have the same shape). | |
C6221 | In our implementation of gradient descent, we have used a function compute_gradient(loss) that computes the gradient of a loss operation in our computational graph with respect to the output of every other node n (i.e. the direction of change for n along which the loss increases the most). | |
C6222 | By the way, in experimental research, random assignment is much more important than random selection; that's because the purpose of an experiment to establish cause and effect relationships. Random assignment "equates the groups" on all known and unknown extraneous variables at the start of the experiment. | |
C6223 | In statistics, econometrics, and related fields, multidimensional analysis (MDA) is a data analysis process that groups data into two categories: data dimensions and measurements. A data set consisting of the number of wins for several football teams over several years is a two-dimensional data set. | |
C6224 | Collaborative filtering (CF) is a technique used by recommender systems. In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). | |
C6225 | In active learning teachers are facilitators rather than one way providers of information. Other examples of active learning techniques include role-playing, case studies, group projects, think-pair-share, peer teaching, debates, Just-in-Time Teaching, and short demonstrations followed by class discussion. | |
C6226 | So by the definition of discrete and continuous random variables, a random variable cannot be both discrete and continuous. No. For a random variable to be discrete, there must a countable sequence such that . | |
C6227 | Unsupervised learning works by analyzing the data without its labels for the hidden structures within it, and through determining the correlations, and for features that actually correlate two data items. It is being used for clustering, dimensionality reduction, feature learning, density estimation, etc. | |
C6228 | The base rate fallacy occurs when prototypical or stereotypical factors are used for analysis rather than actual data. Because the student is volunteering in a hospital with a stroke center, he sees more patients who have experienced a stroke than would be expected in a hospital without a stroke center. | |
C6229 | The sample mean is a consistent estimator for the population mean. A consistent estimate has insignificant errors (variations) as sample sizes grow larger. More specifically, the probability that those errors will vary by more than a given amount approaches zero as the sample size increases. | |
C6230 | 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. | |
C6231 | 0:012:32Suggested clip · 101 secondsMultiple Logistic Regression - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C6232 | Disjoint events cannot happen at the same time. In other words, they are mutually exclusive. Put in formal terms, events A and B are disjoint if their intersection is zero: Disjoint events are disjointed, or not connected. Another way of looking at disjoint events are that they have no outcomes in common. | |
C6233 | The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high-dimensional spaces that do not occur in low-dimensional settings such as the three-dimensional physical space of everyday experience. | |
C6234 | The theorem and its generalizations can be used to prove results and solve problems in combinatorics, algebra, calculus, and many other areas of mathematics. The binomial theorem also helps explore probability in an organized way: A friend says that she will flip a coin 5 times. | |
C6235 | In computer science, an inverted index (also referred to as a postings file or inverted file) is a database index storing a mapping from content, such as words or numbers, to its locations in a table, or in a document or a set of documents (named in contrast to a forward index, which maps from documents to content). | |
C6236 | The goodness of fit test is a statistical hypothesis test to see how well sample data fit a distribution from a population with a normal distribution. Put differently, this test shows if your sample data represents the data you would expect to find in the actual population or if it is somehow skewed. | |
C6237 | The function fX(x) gives us the probability density at point x. It is the limit of the probability of the interval (x,x+Δ] divided by the length of the interval as the length of the interval goes to 0. Remember that P(x<X≤x+Δ)=FX(x+Δ)−FX(x). =dFX(x)dx=F′X(x),if FX(x) is differentiable at x. | |
C6238 | In computer science, specifically in algorithms related to pathfinding, a heuristic function is said to be admissible if it never overestimates the cost of reaching the goal, i.e. the cost it estimates to reach the goal is not higher than the lowest possible cost from the current point in the path. | |
C6239 | Correlation is a statistical measure that expresses the extent to which two variables are linearly related (meaning they change together at a constant rate). It's a common tool for describing simple relationships without making a statement about cause and effect. | |
C6240 | An allocation is Pareto efficient if there is no other allocation in which some other individual is better off and no individual is worse off. Notes: There is no connection between Pareto efficiency and equity! In particular, a Pareto efficient outcome may be very inequitable. | |
C6241 | Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. | |
C6242 | This type of index is called an inverted index, namely because it is an inversion of the forward index. In some search engines the index includes additional information such as frequency of the terms, e.g. how often a term occurs in each document, or the position of the term in each document. | |
C6243 | In class limit, the upper extreme value of the first class interval and the lower extreme value of the next class interval will not be equal. In class boundary, the upper extreme value of the first class interval and the lower extreme value of the next class interval will be equal. | |
C6244 | Two classes of digital filters are Finite Impulse Response (FIR) and Infinite Impulse Response (IIR). The term 'Impulse Response' refers to the appearance of the filter in the time domain. The mathematical difference between the IIR and FIR implementation is that the IIR filter uses some of the filter output as input. | |
C6245 | Abstract: The k-Nearest Neighbors (kNN) classifier is one of the most effective methods in supervised learning problems. It classifies unseen cases comparing their similarity with the training data. Fuzzy-kNN computes a fuzzy degree of membership of each instance to the classes of the problem. | |
C6246 | Data Augmentation in NLPSynonym Replacement: Randomly choose n words from the sentence that are not stop words. Random Insertion: Find a random synonym of a random word in the sentence that is not a stop word. Random Swap: Randomly choose two words in the sentence and swap their positions.More items | |
C6247 | Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. | |
C6248 | There are no acceptable limits for MSE except that the lower the MSE the higher the accuracy of prediction as there would be excellent match between the actual and predicted data set. This is as exemplified by improvement in correlation as MSE approaches zero. | |
C6249 | Programming, of course, means allocation in each case. In the linear programming model limited resources are Page 6 P-885 6-22-56 allocated to various activities. In dynamic programming resources are allocated at each of several time periods. | |
C6250 | Popular ML algorithms include: linear regression, logistic regression, SVMs, nearest neighbor, decision trees, PCA, naive Bayes classifier, and k-means clustering. Classical machine learning algorithms are used for a wide range of applications. | |
C6251 | The Wilcoxon rank sum test is a nonparametric test that may be used to assess whether the distributions of observations obtained between two separate groups on a dependent variable are systematically different from one another. | |
C6252 | Parametric statistics are based on assumptions about the distribution of population from which the sample was taken. Nonparametric statistics are not based on assumptions, that is, the data can be collected from a sample that does not follow a specific distribution. | |
C6253 | First of all, a starting pixel called as the seed is considered. The algorithm checks boundary pixel or adjacent pixels are colored or not. If the adjacent pixel is already filled or colored then leave it, otherwise fill it. The filling is done using four connected or eight connected approaches. | |
C6254 | P value. The Kruskal-Wallis test is a nonparametric test that compares three or more unmatched groups. If your samples are large, it approximates the P value from a Gaussian approximation (based on the fact that the Kruskal-Wallis statistic H approximates a chi-square distribution. | |
C6255 | An unbiased estimator is a statistics that has an expected value equal to the population parameter being estimated. Examples: The sample mean, is an unbiased estimator of the population mean, . The sample variance, is an unbiased estimator of the population variance, . | |
C6256 | Humans are error-prone and biased, but that doesn't mean that algorithms are necessarily better. But these systems can be biased based on who builds them, how they're developed, and how they're ultimately used. This is commonly known as algorithmic bias. | |
C6257 | Statistical inference comprises the application of methods to analyze the sample data in order to estimate the population parameters. The concept of normal (also called gaussian) sampling distribution has an important role in statistical inference, even when the population values are not normally distributed. | |
C6258 | If the function is a probability distribution, then the zeroth moment is the total probability (i.e. one), the first moment is the expected value, the second central moment is the variance, the third standardized moment is the skewness, and the fourth standardized moment is the kurtosis. | |
C6259 | Kernel method is used by SVM to perform a non-linear classification. They take low dimensional input space and convert them into high dimensional input space. It converts non-separable classes into the separable one, it finds out a way to separate the data on the basis of the data labels defined by us. | |
C6260 | The agglomerative clustering is the most common type of hierarchical clustering used to group objects in clusters based on their similarity. It's also known as AGNES (Agglomerative Nesting). The algorithm starts by treating each object as a singleton cluster. | |
C6261 | In mathematics, a tensor is an algebraic object that describes a (multilinear) relationship between sets of algebraic objects related to a vector space. Objects that tensors may map between include vectors and scalars, and even other tensors. | |
C6262 | Accuracy is well defined for any number of classes, so if you use this, a single plot should suffice. Precision and recall, however, are defined only for binary problems. | |
C6263 | Convolution is used in the mathematics of many fields, such as probability and statistics. In linear systems, convolution is used to describe the relationship between three signals of interest: the input signal, the impulse response, and the output signal. | |
C6264 | The results of the convenience sampling cannot be generalized to the target population because of the potential bias of the sampling technique due to under-representation of subgroups in the sample in comparison to the population of interest. The bias of the sample cannot be measured. | |
C6265 | Robust regression is an alternative to least squares regression when data is contaminated with outliers or influential observations and it can also be used for the purpose of detecting influential observations. Please note: The purpose of this page is to show how to use various data analysis commands. | |
C6266 | The joint behavior of two random variables X and Y is determined by the. joint cumulative distribution function (cdf):(1.1) FXY (x, y) = P(X ≤ x, Y ≤ y),where X and Y are continuous or discrete. For example, the probability. P(x1 ≤ X ≤ x2,y1 ≤ Y ≤ y2) = F(x2,y2) − F(x2,y1) − F(x1,y2) + F(x1,y1). | |
C6267 | The Shape of a Histogram A histogram is unimodal if there is one hump, bimodal if there are two humps and multimodal if there are many humps. A nonsymmetric histogram is called skewed if it is not symmetric. If the upper tail is longer than the lower tail then it is positively skewed. | |
C6268 | Dimensional Analysis (also called Factor-Label Method or the Unit Factor Method) is a problem-solving method that uses the fact that any number or expression can be multiplied by one without changing its value. It is a useful technique. | |
C6269 | Generally a cosine similarity between two documents is used as a similarity measure of documents. In Java, you can use Lucene (if your collection is pretty large) or LingPipe to do this. The basic concept would be to count the terms in every document and calculate the dot product of the term vectors. | |
C6270 | A feature detector is also referred to as a kernel or a filter. Intuitively, the matrix representation of the input image is multiplied element-wise with the feature detector to produce a feature map, also known as a convolved feature or an activation map. | |
C6271 | If A and B are two events in a sample space S, then the conditional probability of A given B is defined as P(A|B)=P(A∩B)P(B), when P(B)>0. | |
C6272 | In a statistical study, sampling methods refer to how we select members from the population to be in the study. If a sample isn't randomly selected, it will probably be biased in some way and the data may not be representative of the population. | |
C6273 | Difference Between Temporal and Spatial Databases A spatial database stores and allows queries of data defined by geometric space. Many spatial databases can represent simple coordinates, points, lines and polygons. A temporal database stores data relating to time whether past, present or future. | |
C6274 | 1- Find the remainder of n by moduling it with 4. 2- If rem = 0, then xor will be same as n. 3- If rem = 1, then xor will be 1. 4- If rem = 2, then xor will be n+1. | |
C6275 | In probability theory and statistics, a Gaussian process is a stochastic process (a collection of random variables indexed by time or space), such that every finite collection of those random variables has a multivariate normal distribution, i.e. every finite linear combination of them is normally distributed. | |
C6276 | While the multivariable model is used for the analysis with one outcome (dependent) and multiple independent (a.k.a., predictor or explanatory) variables,2,3 multivariate is used for the analysis with more than 1 outcomes (eg, repeated measures) and multiple independent variables. | |
C6277 | The higher the threshold, or closer to (0, 0), the higher the specificity and the lower the sensitivity. The lower the threshold, or closer to (1,1), the higher the sensitivity and lower the specificity. So which threshold value one should pick? | |
C6278 | 3.1 . Each bootstrap distribution is centered at the statistic from the corresponding sample rather than at the population mean μ. | |
C6279 | A data set is homogeneous if it is made up of things (i.e. people, cells or traits) that are similar to each other. For example a data set made up of 20-year-old college students enrolled in Physics 101 is a homogeneous sample. | |
C6280 | It's more of an approach than a process. Predictive analytics and machine learning go hand-in-hand, as predictive models typically include a machine learning algorithm. These models are then made up of algorithms. The algorithms perform the data mining and statistical analysis, determining trends and patterns in data. | |
C6281 | Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the response and explanatory variables. If the relationship is unknown and nonlinear, nonparametric regression models should be used. | |
C6282 | An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. | |
C6283 | The cutoff frequency for a high-pass filter is that frequency at which the output (load) voltage equals 70.7% of the input (source) voltage. Above the cutoff frequency, the output voltage is greater than 70.7% of the input, and vice versa. | |
C6284 | Disadvantage: Sigmoid: tend to vanish gradient (cause there is a mechanism to reduce the gradient as "a" increases, where "a" is the input of a sigmoid function. | |
C6285 | Treatment groups are the sets of participants in a research study that are exposed to some manipulation or intentional change in the independent variable of interest. They are an integral part of experimental research design that helps to measure effects as well as establish causality. | |
C6286 | Therefore, the maximum likelihood estimator is an unbiased estimator of . | |
C6287 | The One-sample t-test is used to compare a sample mean to a specific value. An “F Test” uses the F-distribution.Comparison Table Between T-test and F-test (in Tabular Form)Parameter of ComparisonT-testF-testTest statisticT = (mean - comparison value)/ Standard Error ~t(n-1)F = s21 / s22 ~ F(n1-1,n2-1)4 more rows | |
C6288 | The easiest way of estimating the semantic similarity between a pair of sentences is by taking the average of the word embeddings of all words in the two sentences, and calculating the cosine between the resulting embeddings. | |
C6289 | First, Cross-entropy (or softmax loss, but cross-entropy works better) is a better measure than MSE for classification, because the decision boundary in a classification task is large (in comparison with regression). For regression problems, you would almost always use the MSE. | |
C6290 | Multiclass classification with logistic regression can be done either through the one-vs-rest scheme in which for each class a binary classification problem of data belonging or not to that class is done, or changing the loss function to cross- entropy loss. By default, multi_class is set to 'ovr'. | |
C6291 | Rather than using the past values of the forecast variable in a regression, a moving average model uses past forecast errors in a regression-like model. While, the autoregressive model(AR) uses the past forecasts to predict future values. | |
C6292 | Just as correlation measures the extent of a linear relationship between two variables, autocorrelation measures the linear relationship between lagged values of a time series. The autocorrelation coefficients are plotted to show the autocorrelation function or ACF. The plot is also known as a correlogram. | |
C6293 | The number of bootstrap samples can be indicated with B (e.g. if you resample 10 times then B = 10). A star next to a statistic, like s* or x̄* indicates the statistic was calculated by resampling. A bootstrap statistic is sometimes denoted with a T, where T*b would be the Bth bootstrap sample statistic T. | |
C6294 | Direct Application of AM-GM to an Inequality The simplest way to apply AM-GM is to apply it immediately on all of the terms. For example, we know that for non-negative values, x + y 2 ≥ x y , x + y + z 3 ≥ x y z 3 , w + x + y + z 4 ≥ w x y z 4 . | |
C6295 | There is a thin line of demarcation amidst t-test and ANOVA, i.e. when the population means of only two groups is to be compared, the t-test is used, but when means of more than two groups are to be compared, ANOVA is preferred. | |
C6296 | 0:559:25Suggested clip · 121 secondsHow To Calculate Pearson's Correlation Coefficient (r) by Hand YouTubeStart of suggested clipEnd of suggested clip | |
C6297 | The dissimilarity matrix, using the euclidean metric, can be calculated with the command: daisy(agriculture, metric = "euclidean"). The result the of calculation will be displayed directly in the screen, and if you wanna reuse it you can simply assign it to an object: x <- daisy(agriculture, metric = "euclidean"). | |
C6298 | This means that the sum of two independent normally distributed random variables is normal, with its mean being the sum of the two means, and its variance being the sum of the two variances (i.e., the square of the standard deviation is the sum of the squares of the standard deviations). | |
C6299 | The sum of squared errors is a 'total' and is, therefore, affected by the number of data points. The variance is the 'average' variability but in units squared. The standard deviation is the average variation but converted back to the original units of measurement. |
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