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C8800 | All Redis data resides in-memory, in contrast to databases that store data on disk or SSDs. By eliminating the need to access disks, in-memory data stores such as Redis avoid seek time delays and can access data in microseconds. | |
C8801 | Data wrangling is the process of cleaning, structuring and enriching raw data into a desired format for better decision making in less time. This self-service model with data wrangling tools allows analysts to tackle more complex data more quickly, produce more accurate results, and make better decisions. | |
C8802 | An interpolated string is a string literal that might contain interpolation expressions. When an interpolated string is resolved to a result string, items with interpolation expressions are replaced by the string representations of the expression results. | |
C8803 | Different types of classifiersPerceptron.Naive Bayes.Decision Tree.Logistic Regression.K-Nearest Neighbor.Artificial Neural Networks/Deep Learning.Support Vector Machine. | |
C8804 | In the case where events A and B are independent (where event A has no effect on the probability of event B), the conditional probability of event B given event A is simply the probability of event B, that is P(B). P(A and B) = P(A)P(B|A). | |
C8805 | The least squares principle states that the SRF should be constructed (with the constant and slope values) so that the sum of the squared distance between the observed values of your dependent variable and the values estimated from your SRF is minimized (the smallest possible value). | |
C8806 | Pointwise mutual information (PMI), or point mutual information, is a measure of association used in information theory and statistics. In contrast to mutual information (MI) which builds upon PMI, it refers to single events, whereas MI refers to the average of all possible events. | |
C8807 | It is technically defined as "the nth root product of n numbers." The geometric mean must be used when working with percentages, which are derived from values, while the standard arithmetic mean works with the values themselves. The harmonic mean is best used for fractions such as rates or multiples. | |
C8808 | The Pearson's correlation coefficient is calculated as the covariance of the two variables divided by the product of the standard deviation of each data sample. It is the normalization of the covariance between the two variables to give an interpretable score. | |
C8809 | AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. Cognitive computing is a subfield of AI that strives for a natural, human-like interaction with machines. | |
C8810 | A non parametric test (sometimes called a distribution free test) does not assume anything about the underlying distribution (for example, that the data comes from a normal distribution). It usually means that you know the population data does not have a normal distribution. | |
C8811 | noun Mathematics. a mathematical operator with the property that applying it to a linear combination of two objects yields the same linear combination as the result of applying it to the objects separately. | |
C8812 | Pure serial correlation: occurs when the error terms are correlated and the regression equation is correctly specified. The most commonly assumed form of serial correlation is first-order serial correlation, in which one error term is a function of a previous error term. | |
C8813 | Clustering analysis is broadly used in many applications such as market research, pattern recognition, data analysis, and image processing. Clustering can also help marketers discover distinct groups in their customer base. And they can characterize their customer groups based on the purchasing patterns. | |
C8814 | 0:496:46Suggested clip · 116 secondsUnderstanding Statistical Inference - statistics help - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C8815 | IN preposition/subordinating conjunction. JJ adjective 'big' JJR adjective, comparative 'bigger' JJS adjective, superlative 'biggest' | |
C8816 | Semantic similarity is calculated based on two semantic vectors. An order vector is formed for each sentence which considers the syntactic similarity between the sentences. Finally, semantic similarity is calculated based on semantic vectors and order vectors. | |
C8817 | Within an artificial neural network, a neuron is a mathematical function that model the functioning of a biological neuron. Typically, a neuron compute the weighted average of its input, and this sum is passed through a nonlinear function, often called activation function, such as the sigmoid. | |
C8818 | Supervised Learning Algorithms: A classification model might look at the input data and try to predict labels like “sick” or “healthy.” Regression is used to predict the outcome of a given sample when the output variable is in the form of real values. | |
C8819 | Adjusting minor values in algorithms: This in turn would increase the bias of the model. Whereas, in the SVM algorithm, the trade-off can be changed by an increase in the C parameter that would influence the violations of the margin allowed in the training data. This will increase the bias but decrease the variance. | |
C8820 | The exponential distribution is the only continuous distribution that is memoryless (or with a constant failure rate). Geometric distribution, its discrete counterpart, is the only discrete distribution that is memoryless. | |
C8821 | The coefficient of variation is a better risk measure than the standard deviation alone because the CV adjusts for the size of the project. The CV measures the standard deviation divided by the mean and therefore puts the standard deviation into context. | |
C8822 | To convert this distance metric into the similarity metric, we can divide the distances of objects with the max distance, and then subtract it by 1 to score the similarity between 0 and 1. We will look at the example after discussing the cosine metric. | |
C8823 | Therefore, a Random Forest model does not scale very well for time-series data and might need to be constantly updated in Production or trained with some Random Data that lies outside our range of Training set. | |
C8824 | The language of computer science in general, and software development in particular, is laced with metaphor. Indurkhya [5] characterizes metaphor as “a description of an object or event, real or imagined, using concepts that cannot be applied to the object or event in a conventional way” (p. 18). | |
C8825 | There is a huge difference between classifiers and regressors. Classifiers predict one class from a predetermined list or probabilities of belonging to a class. Regressors predict some value, which could be almost anything. Differeng metrics are used for classification and regression. | |
C8826 | The agent during its course of learning experience various different situations in the environment it is in. These are called states. The agent while being in that state may choose from a set of allowable actions which may fetch different rewards(or penalties). | |
C8827 | the condition or quality of being true, correct, or exact; freedom from error or defect; precision or exactness; correctness. Chemistry, Physics. the extent to which a given measurement agrees with the standard value for that measurement. | |
C8828 | Cohen suggested the Kappa result be interpreted as follows: values ≤ 0 as indicating no agreement and 0.01–0.20 as none to slight, 0.21–0.40 as fair, 0.41– 0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1.00 as almost perfect agreement. | |
C8829 | Branches of Artificial Intelligence As AI CapabilitiesMachine learning. Neural Network. Robotics. Expert Systems.Fuzzy Logic. Natural Language Processing. | |
C8830 | Formally, a statistic T(X1,···,Xn) is said to be sufficient for θ if the conditional distribution of X1,···,Xn, given T = t, does not depend on θ for any value of t. In other words, given the value of T, we can gain no more knowledge about θ from knowing more about the probability distribution of X1,···,Xn. | |
C8831 | Difference between rule-based AI and machine learning Machine learning systems are probabilistic and rule-based AI models are deterministic. Machine learning systems require more data as compared to rule-based models. Rule-based AI models can operate with simple basic information and data. | |
C8832 | How To Develop Your Artificial Intelligence (AI) Strategy – With Handy TemplateStart with your AI strategic use cases. Identifying the cross-cutting issues for your AI use cases. Data strategy. Ethical and legal issues. Technology and infrastructure. Skills and capacity. Implementation. Change management.More items | |
C8833 | The major difference between using a Z score and a T statistic is that you have to estimate the population standard deviation. The T test is also used if you have a small sample size (less than 30). | |
C8834 | A relative frequency distribution shows the proportion of the total number of observations associated with each value or class of values and is related to a probability distribution, which is extensively used in statistics. | |
C8835 | Canonical correlation analysis (CCA) is very important in MVL, whose main idea is to map data from different views onto a common space with the maximum correlation. The traditional CCA can only be used to calculate the linear correlation between two views. | |
C8836 | While Kalman filter can be used for linear or linearized processes and measurement system, the particle filter can be used for nonlinear systems. Also, the uncertainty of Kalman filter is restricted to Gaussian distribution, while the particle filter can deal with non-Gaussian noise distribution. | |
C8837 | Definition: Stratified sampling is a type of sampling method in which the total population is divided into smaller groups or strata to complete the sampling process. The strata is formed based on some common characteristics in the population data. | |
C8838 | This problem is solved by Stochastic Gradient Descent. In SGD, it uses only a single sample, i.e., a batch size of one, to perform each iteration. The sample is randomly shuffled and selected for performing the iteration. | |
C8839 | By design, linear regression is, in some way, scale-invariant. Some authors have developed rank-regression techniques to handle non-linear re-scaling, using the same approach as in the previous section on clustering. | |
C8840 | 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. | |
C8841 | Adaptive resonance theory is a type of neural network technique developed by Stephen Grossberg and Gail Carpenter in 1987. The basic ART uses unsupervised learning technique. | |
C8842 | Variables that can only take on a finite number of values are called "discrete variables." All qualitative variables are discrete. Some quantitative variables are discrete, such as performance rated as 1,2,3,4, or 5, or temperature rounded to the nearest degree. | |
C8843 | How to calculate margin of errorGet the population standard deviation (σ) and sample size (n).Take the square root of your sample size and divide it into your population standard deviation.Multiply the result by the z-score consistent with your desired confidence interval according to the following table: | |
C8844 | A pooling layer is another building block of a CNN. Its function is to progressively reduce the spatial size of the representation to reduce the amount of parameters and computation in the network. Pooling layer operates on each feature map independently. The most common approach used in pooling is max pooling. | |
C8845 | Bayesian networks are a type of Probabilistic Graphical Model that can be used to build models from data and/or expert opinion. They can be used for a wide range of tasks including prediction, anomaly detection, diagnostics, automated insight, reasoning, time series prediction and decision making under uncertainty. | |
C8846 | The R-squared of the regression is the fraction of the variation in your dependent variable that is accounted for (or predicted by) your independent variables. (In regression with a single independent variable, it is the same as the square of the correlation between your dependent and independent variable.) | |
C8847 | Sample moments are those that are utilized to approximate the unknown population moments. Sample moments are calculated from the sample data. Such moments include mean, variance, skewness, and kurtosis. | |
C8848 | Entry level positions require at least a bachelor's degree while positions entailing supervision, leadership or administrative roles frequently require master's or doctoral degrees. Typical coursework involves study of: Various level of math, including probability, statistics, algebra, calculus, logic and algorithms. | |
C8849 | Bivariate analysis is one of the simplest forms of quantitative (statistical) analysis. It involves the analysis of two variables (often denoted as X, Y), for the purpose of determining the empirical relationship between them. Like univariate analysis, bivariate analysis can be descriptive or inferential. | |
C8850 | A generative adversarial network (GAN) is a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in 2014. Two neural networks contest with each other in a game (in the form of a zero-sum game, where one agent's gain is another agent's loss). | |
C8851 | The lognormal distribution is commonly used to model the lives of units whose failure modes are of a fatigue-stress nature. Since this includes most, if not all, mechanical systems, the lognormal distribution can have widespread application. | |
C8852 | Confusion matrices are used to visualize important predictive analytics like recall, specificity, accuracy, and precision. Confusion matrices are useful because they give direct comparisons of values like True Positives, False Positives, True Negatives and False Negatives. | |
C8853 | Joint entropy: H ( X , Y ) : = − Σ x ∈ J X Σ y ∈ J Y p ( x , y ) log p ( x , y ) . . | |
C8854 | It is calculated in the same way - by running the network forward over inputs xi and comparing the network outputs ˆyi with the ground truth values yi using a loss function e.g. J=1N∑Ni=1L(ˆyi,yi) where L is the individual loss function based somehow on the difference between predicted value and target. | |
C8855 | Normal distributions are symmetric around their mean. The mean, median, and mode of a normal distribution are equal. The area under the normal curve is equal to 1.0. Approximately 95% of the area of a normal distribution is within two standard deviations of the mean. | |
C8856 | Surface must be closed But unlike, say, Stokes' theorem, the divergence theorem only applies to closed surfaces, meaning surfaces without a boundary. For example, a hemisphere is not a closed surface, it has a circle as its boundary, so you cannot apply the divergence theorem. | |
C8857 | A linear classifier that the perceptron is categorized as is a classification algorithm, which relies on a linear predictor function to make predictions. Its predictions are based on a combination that includes weights and feature vector. | |
C8858 | Interpreting the ROC curve Classifiers that give curves closer to the top-left corner indicate a better performance. As a baseline, a random classifier is expected to give points lying along the diagonal (FPR = TPR). The closer the curve comes to the 45-degree diagonal of the ROC space, the less accurate the test. | |
C8859 | Flow Rate Calibration – Improve Print Accuracy3.1 1. Measure the Filament Diameter.3.2 2. Print a Hollow Test Cube.3.3 3. Measure the Cube Walls.3.4 4. Enter the new Flow Rate value in your slicer. | |
C8860 | In relation to out-group, a social group toward which a person feels a sense of competition or opposition. They both affect the opinions and behavior of individuals because In-groups and Out- groups are based on the idea that "we" have valued traits that "they" lack. | |
C8861 | Binomial counts successes in a fixed number of trials, while Negative binomial counts failures until a fixed number successes. The Bernoulli and Geometric distributions are the simplest cases of the Binomial and Negative Binomial distributions. | |
C8862 | Now, three variable case it is less clear for me. An intuitive definition for covariance function would be Cov(X,Y,Z)=E[(x−E[X])(y−E[Y])(z−E[Z])], but instead the literature suggests using covariance matrix that is defined as two variable covariance for each pair of variables. | |
C8863 | Content-based filtering, makes recommendations based on user preferences for product features. Collaborative filtering mimics user-to-user recommendations. It predicts users preferences as a linear, weighted combination of other user preferences. Both methods have limitations. | |
C8864 | Tensorflow is the more popular of the two. Tensorflow is typically used more in Deep Learning and Neural Networks. SciKit learn is more general Machine Learning. | |
C8865 | Similar to the distinction in philosophy between a priori and a posteriori, in Bayesian inference a priori denotes general knowledge about the data distribution before making an inference, while a posteriori denotes knowledge that incorporates the results of making an inference. | |
C8866 | Pruning reduces the size of decision trees by removing parts of the tree that do not provide power to classify instances. Decision trees are the most susceptible out of all the machine learning algorithms to overfitting and effective pruning can reduce this likelihood. | |
C8867 | Chi-square Test. The Pearson's χ2 test (after Karl Pearson, 1900) is the most commonly used test for the difference in distribution of categorical variables between two or more independent groups. | |
C8868 | Statistical data binning is a way to group numbers of more or less continuous values into a smaller number of "bins". For example, if you have data about a group of people, you might want to arrange their ages into a smaller number of age intervals (for example, grouping every five years together). | |
C8869 | Agents can be grouped into four classes based on their degree of perceived intelligence and capability :Simple Reflex Agents.Model-Based Reflex Agents.Goal-Based Agents.Utility-Based Agents.Learning Agent. | |
C8870 | Two approaches to avoiding overfitting are distinguished: pre-pruning (generating a tree with fewer branches than would otherwise be the case) and post-pruning (generating a tree in full and then removing parts of it). Results are given for pre-pruning using either a size or a maximum depth cutoff. | |
C8871 | LDA is a parametric model, and the parameter is number of topics. | |
C8872 | A generative model includes the distribution of the data itself, and tells you how likely a given example is. For example, models that predict the next word in a sequence are typically generative models (usually much simpler than GANs) because they can assign a probability to a sequence of words. | |
C8873 | Classic linear regression is one form of general linear model. But with a general linear model you can have any number of continuous or nominal independent variables and their interactions. | |
C8874 | Single-pattern algorithmsAlgorithmPreprocessing timeMatching timeKnuth–Morris–Pratt algorithmΘ(m)Θ(n)Boyer–Moore string-search algorithmΘ(m + k)best Ω(n/m), worst O(mn)Bitap algorithm (shift-or, shift-and, Baeza–Yates–Gonnet; fuzzy; agrep)Θ(m + k)O(mn)Two-way string-matching algorithm (glibc memmem/strstr)Θ(m)O(n+m)6 more rows | |
C8875 | Hypothesis Space (H): Hypothesis space is the set of all the possible legal hypothesis. This is the set from which the machine learning algorithm would determine the best possible (only one) which would best describe the target function or the outputs. | |
C8876 | The formula for calculating a z-score is is z = (x-μ)/σ, where x is the raw score, μ is the population mean, and σ is the population standard deviation. As the formula shows, the z-score is simply the raw score minus the population mean, divided by the population standard deviation. | |
C8877 | Coef. A regression coefficient describes the size and direction of the relationship between a predictor and the response variable. Coefficients are the numbers by which the values of the term are multiplied in a regression equation. | |
C8878 | A high-pass filter (HPF) is an electronic filter that passes signals with a frequency higher than a certain cutoff frequency and attenuates signals with frequencies lower than the cutoff frequency. The amount of attenuation for each frequency depends on the filter design. | |
C8879 | A Markov chain is ergodic if it is both irreducible and aperiodic. This condition is equivalent to the transition matrix being a primitive nonnegative matrix. | |
C8880 | Three of the most common applications of exponential and logarithmic functions have to do with interest earned on an investment, population growth, and carbon dating. | |
C8881 | A good maximum sample size is usually 10% as long as it does not exceed 1000. A good maximum sample size is usually around 10% of the population, as long as this does not exceed 1000. For example, in a population of 5000, 10% would be 500. In a population of 200,000, 10% would be 20,000. | |
C8882 | A deep neural net is a single independent model, whereas ensemble models are ensembles of many independent models. The primary connection between the two is dropout, a particular method of training deep neural nets that's inspired by ensemble methods. | |
C8883 | Second-Order/Pseudo-Second-Order Reaction For a Pseudo-Second-Order Reaction, the reaction rate constant k is replaced by the apparent reaction rate constant k'. If the reaction is not written out specifically to show a value of νA, the value is assumed to be 1 and is not shown in these equations. | |
C8884 | An intercept or offset from an origin. Bias (also known as the bias term) is referred to as b or w0 in machine learning models. | |
C8885 | In probability theory and statistics, skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The skewness value can be positive, zero, negative, or undefined. | |
C8886 | The need for a CNN with variable input dimensions FCN is a network that does not contain any “Dense” layers (as in traditional CNNs) instead it contains 1x1 convolutions that perform the task of fully connected layers (Dense layers). | |
C8887 | Bayesian OptimizationBuild a surrogate probability model of the objective function.Find the hyperparameters that perform best on the surrogate.Apply these hyperparameters to the true objective function.Update the surrogate model incorporating the new results.Repeat steps 2–4 until max iterations or time is reached. | |
C8888 | The Normal Distribution is a distribution that has most of the data in the center with decreasing amounts evenly distributed to the left and the right. Skewed Distribution is distribution with data clumped up on one side or the other with decreasing amounts trailing off to the left or the right. | |
C8889 | Inter-Rater Reliability MethodsCount the number of ratings in agreement. In the above table, that's 3.Count the total number of ratings. For this example, that's 5.Divide the total by the number in agreement to get a fraction: 3/5.Convert to a percentage: 3/5 = 60%. | |
C8890 | The bias error is an error from erroneous assumptions in the learning algorithm. High bias can cause an algorithm to miss the relevant relations between features and target outputs (underfitting). The variance is an error from sensitivity to small fluctuations in the training set. | |
C8891 | Quota sampling means to take a very tailored sample that's in proportion to some characteristic or trait of a population. Care is taken to maintain the correct proportions representative of the population. For example, if your population consists of 45% female and 55% male, your sample should reflect those percentages. | |
C8892 | The main limitation of random forest is that a large number of trees can make the algorithm too slow and ineffective for real-time predictions. In general, these algorithms are fast to train, but quite slow to create predictions once they are trained. | |
C8893 | A statistical model is a family of probability distributions, the central problem of statistical inference being to identify which member of the family generated the data currently of interest. | |
C8894 | A subquery is a select statement that is embedded in a clause of another select statement. A Correlated subquery is a subquery that is evaluated once for each row processed by the outer query or main query. | |
C8895 | A variable xj is said to be endogenous within the causal model M if its value is determined or influenced by one or more of the independent variables X (excluding itself). A purely endogenous variable is a factor that is entirely determined by the states of other variables in the system. | |
C8896 | One should always conduct a residual analysis to verify that the conditions for drawing inferences about the coefficients in a linear model have been met. Recall that, if a linear model makes sense, the residuals will: have a constant variance. | |
C8897 | Answer: An independent variable is exactly what it sounds like. It is a variable that stands alone and isn't changed by the other variables you are trying to measure. For example, someone's age might be an independent variable. | |
C8898 | Fewer than 1,000 steps a day is sedentary. 1,000 to 10,000 steps or about 4 miles a day is Lightly Active. 10,000 to 23,000 steps or 4 to 10 miles a day is considered Active. More than 23,000 steps or 10 miles a day is Highly active. | |
C8899 | There's no difference. They are two names for the same thing. They tend to be used in different contexts, though. You talk about the expected value of a random variable and the mean of a sample, population or probability distribution. |
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