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C10100 | The AUC value lies between 0.5 to 1 where 0.5 denotes a bad classifer and 1 denotes an excellent classifier. | |
C10101 | In short, fourier series is for periodic signals and fourier transform is for aperiodic signals. Fourier series is used to decompose signals into basis elements (complex exponentials) while fourier transforms are used to analyze signal in another domain (e.g. from time to frequency, or vice versa). | |
C10102 | Grid-searching is the process of scanning the data to configure optimal parameters for a given model. Grid-Search will build a model on each parameter combination possible. It iterates through every parameter combination and stores a model for each combination. | |
C10103 | A-squared is the test statistic for the Anderson-Darling Normality test. It is a measure of how closely a dataset follows the normal distribution. So if you get an A-squared that is fairly large, then you will get a small p-value and thus reject the null hypothesis. | |
C10104 | The sample mean is a consistent estimator for the population mean. A consistent estimate has insignificant errors (variations) as sample sizes grow larger. In other words, the more data you collect, a consistent estimator will be close to the real population parameter you're trying to measure. | |
C10105 | Efficiency: ReLu is faster to compute than the sigmoid function, and its derivative is faster to compute. This makes a significant difference to training and inference time for neural networks: only a constant factor, but constants can matter. Simplicity: ReLu is simple. | |
C10106 | The main motivation is to aggregate multiple low-level features in the neighborhood to gain invariance mainly in object recognition. Why do we use pooling layers in CNN? | |
C10107 | The RMSE is the square root of the variance of the residuals. It indicates the absolute fit of the model to the data–how close the observed data points are to the model's predicted values. Whereas R-squared is a relative measure of fit, RMSE is an absolute measure of fit. Lower values of RMSE indicate better fit. | |
C10108 | Machine Learning This phenomenon states that with a fixed number of training samples, the average (expected) predictive power of a classifier or regressor first increases as number of dimensions or features used is increased but beyond a certain dimensionality it starts deteriorating instead of improving steadily. | |
C10109 | Mean Absolute Error (MAE) is another loss function used for regression models. MAE is the sum of absolute differences between our target and predicted variables. So it measures the average magnitude of errors in a set of predictions, without considering their directions. | |
C10110 | The sum of a square matrix and its conjugate transpose. is Hermitian. The difference of a square matrix and its conjugate transpose. is skew-Hermitian. | |
C10111 | Advantages of Dimensionality Reduction It helps in data compression, and hence reduced storage space. It reduces computation time. It also helps remove redundant features, if any. | |
C10112 | Swarm-intelligence principles inspired by the collective insect societies are used for developing computer algorithms and motion control principles for robotics. The basic idea is that a swarm of individuals can coordinate and behave as a single entity that performs better than the individuals. | |
C10113 | A support vector machine is a machine learning model that is able to generalise between two different classes if the set of labelled data is provided in the training set to the algorithm. The main function of the SVM is to check for that hyperplane that is able to distinguish between the two classes. | |
C10114 | You can pass data between view controllers in Swift in 6 ways:By using an instance property (A → B)By using segues (for Storyboards)By using instance properties and functions (A ← B)By using the delegation pattern.By using a closure or completion handler.By using NotificationCenter and the Observer pattern. | |
C10115 | Non-hierarchical clustering is frequently referred to as k-means clustering. This type of clustering does not require all possible distances to be computed in a large data set. This technique is primarily used for the analysis of clusters in data mining. | |
C10116 | If you have n numbers in a group, the median is the (n + 1)/2 th value. For example, there are 7 numbers in the example above, so replace n by 7 and the median is the (7 + 1)/2 th value = 4th value. The 4th value is 6. On a histogram, the median value occurs where the whole histogram is divided into two equal parts. | |
C10117 | The Kruskal-Wallis H test (sometimes also called the "one-way ANOVA on ranks") is a rank-based nonparametric test that can be used to determine if there are statistically significant differences between two or more groups of an independent variable on a continuous or ordinal dependent variable. | |
C10118 | Most machine learning roles will require the use of Python or C/C++ (though Python is often preferred). Background in the theory behind machine learning algorithms and an understanding of how they can be efficiently implemented in terms of both space and time is critical. | |
C10119 | Here are the most common examples of multitasking in personal and professional settings:Responding to emails while listening to a podcast.Taking notes during a lecture.Completing paperwork while reading the fine print.Driving a vehicle while talking to someone.Talking on the phone while greeting someone.More items• | |
C10120 | The distributional hypothesis suggests that the more semantically similar two words are, the more distributionally similar they will be in turn, and thus the more that they will tend to occur in similar linguistic contexts. | |
C10121 | Yes. For a 1D signal, shift invariance of a filter implies the following. The following example illustrates the shift invariance (for all the signals, the sample at the origin is in bold, and zero padding is assumed). | |
C10122 | Tabular in this context simply means that we will store the Q function in a lookup table. I.e. we create a table where we store the Q value for each possible State and Move. | |
C10123 | recursion | |
C10124 | The simple moving average (SMA) is the average price of a security over a specific period. The exponential moving average (EMA) provides more weight to the most recent prices in an attempt to better reflect new market data. The difference between the two is noticeable when comparing long-term averages. | |
C10125 | It is believed that Facebook's new algorithm is based on the Vickrey-Clarke-Groves algorithm, which “operates as a closed auction.” Facebook's algorithm for ranking content on your News Feed is based on four factors: The Inventory of all posts available to display. Signals that tell Facebook what each post is. | |
C10126 | Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. Deep learning is a subfield of machine learning. While both fall under the broad category of artificial intelligence, deep learning is what powers the most human-like artificial intelligence. | |
C10127 | The relative efficiency of two procedures is the ratio of their efficiencies, although often this concept is used where the comparison is made between a given procedure and a notional "best possible" procedure. | |
C10128 | Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Descriptive statistics are typically distinguished from inferential statistics. With descriptive statistics you are simply describing what is or what the data shows. | |
C10129 | Data skewed to the right is usually a result of a lower boundary in a data set (whereas data skewed to the left is a result of a higher boundary). So if the data set's lower bounds are extremely low relative to the rest of the data, this will cause the data to skew right. Another cause of skewness is start-up effects. | |
C10130 | These are some of the most popular examples of artificial intelligence that's being used today. Everyone is familiar with Apple's personal assistant, Siri. She's the friendly voice-activated computer that we interact with on a daily basis. | |
C10131 | The loss function is used to optimize your model. This is the function that will get minimized by the optimizer. A metric is used to judge the performance of your model. | |
C10132 | To conclude, the important thing to remember about the odds ratio is that an odds ratio greater than 1 is a positive association (i.e., higher number for the predictor means group 1 in the outcome), and an odds ratio less than 1 is negative association (i.e., higher number for the predictor means group 0 in the outcome | |
C10133 | Quota sampling achieves a representative age distribution, but it isn't a random sample, because the sampling frame is unknown. Therefore, the sample may not be representative of the population. | |
C10134 | A neural network is either a system software or hardware that works similar to the tasks performed by neurons of human brain. Neural networks include various technologies like deep learning, and machine learning as a part of Artificial Intelligence (AI). | |
C10135 | A convolution is an integral that expresses the amount of overlap of one function as it is shifted over another function. . It therefore "blends" one function with another. | |
C10136 | Decision tree | |
C10137 | The model can only make recommendations based on existing interests of the user. In other words, the model has limited ability to expand on the users' existing interests. | |
C10138 | Mann-Whitney test | |
C10139 | Attention Mechanism in Neural Networks - 1. Introduction. Attention is arguably one of the most powerful concepts in the deep learning field nowadays. It is based on a common-sensical intuition that we “attend to” a certain part when processing a large amount of information. | |
C10140 | Decision theory is an interdisciplinary approach to arrive at the decisions that are the most advantageous given an uncertain environment. Decision theory brings together psychology, statistics, philosophy, and mathematics to analyze the decision-making process. | |
C10141 | The least squares method is a statistical procedure to find the best fit for a set of data points by minimizing the sum of the offsets or residuals of points from the plotted curve. Least squares regression is used to predict the behavior of dependent variables. | |
C10142 | Today, neural networks are used for solving many business problems such as sales forecasting, customer research, data validation, and risk management. For example, at Statsbot we apply neural networks for time-series predictions, anomaly detection in data, and natural language understanding. | |
C10143 | A dummy variable (aka, an indicator variable) is a numeric variable that represents categorical data, such as gender, race, political affiliation, etc. For example, suppose we are interested in political affiliation, a categorical variable that might assume three values - Republican, Democrat, or Independent. | |
C10144 | A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0). | |
C10145 | The use of computer algorithms plays an essential role in space search programs. We are in the age of algorithms because they solve our everyday tasks and we won't be able to live with them. They make our life more comfortable and, in the future, they will be able to predict our behavior. | |
C10146 | Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). Cluster analysis itself is not one specific algorithm, but the general task to be solved. | |
C10147 | Advantages. The main advantage of multivariate analysis is that since it considers more than one factor of independent variables that influence the variability of dependent variables, the conclusion drawn is more accurate. | |
C10148 | The beta distribution is a continuous probability distribution that can be used to represent proportion or probability outcomes. For example, the beta distribution might be used to find how likely it is that your preferred candidate for mayor will receive 70% of the vote. | |
C10149 | EdgeRank | |
C10150 | PCA attempts to find uncorrelated sources, where as ICA attempts to find independent sources. Both techniques try to obtain new sources by linearly combining the original sources. | |
C10151 | Output is defined as the act of producing something, the amount of something that is produced or the process in which something is delivered. An example of output is the electricity produced by a power plant. An example of output is producing 1,000 cases of a product. | |
C10152 | In our categorical case we would use a simple regression equation for each group to investigate the simple slopes. It is common practice to standardize or center variables to make the data more interpretable in simple slopes analysis; however, categorical variables should never be standardized or centered. | |
C10153 | “Bayesian statistics is a mathematical procedure that applies probabilities to statistical problems. It provides people the tools to update their beliefs in the evidence of new data.” | |
C10154 | Multiple Linear Regression Analysis consists of more than just fitting a linear line through a cloud of data points. It consists of three stages: 1) analyzing the correlation and directionality of the data, 2) estimating the model, i.e., fitting the line, and 3) evaluating the validity and usefulness of the model. | |
C10155 | The posterior probability is one of the quantities involved in Bayes' rule. It is the conditional probability of a given event, computed after observing a second event whose conditional and unconditional probabilities were known in advance. | |
C10156 | There are two major different types of uncertainty in deep learning: epistemic uncertainty and aleatoric uncertainty. Epistemic uncertainty describes what the model does not know because training data was not appropriate. Epistemic uncertainty is due to limited data and knowledge. | |
C10157 | Every parametric test has the assumption that the sample means are following a normal distribution. This is the case if the sample itself is normal distributed or if approximately if the sample size is big enough. | |
C10158 | RMSLE, or the Root Mean Square Logarithmic Error, is the ratio (the log) between the actual values in your data and predicted values in the model. | |
C10159 | Approach –Load dataset from source.Split the dataset into “training” and “test” data.Train Decision tree, SVM, and KNN classifiers on the training data.Use the above classifiers to predict labels for the test data.Measure accuracy and visualise classification. | |
C10160 | The mean (average) of a data set is found by adding all numbers in the data set and then dividing by the number of values in the set. The median is the middle value when a data set is ordered from least to greatest. The mode is the number that occurs most often in a data set. | |
C10161 | The k-nearest neighbors (KNN) algorithm is a simple, supervised machine learning algorithm that can be used to solve both classification and regression problems. | |
C10162 | Chi Square distributions are positively skewed, with the degree of skew decreasing with increasing degrees of freedom. As the degrees of freedom increases, the Chi Square distribution approaches a normal distribution. Figure 1 shows density functions for three Chi Square distributions. | |
C10163 | In statistics, the likelihood function (often simply called the likelihood) measures the goodness of fit of a statistical model to a sample of data for given values of the unknown parameters. | |
C10164 | Use the hypergeometric distribution with populations that are so small that the outcome of a trial has a large effect on the probability that the next outcome is an event or non-event. For example, in a population of 10 people, 7 people have O+ blood. | |
C10165 | For values of x > 0, the gamma function is defined using an integral formula as Γ(x) = Integral on the interval [0, ∞ ] of ∫ 0∞t x −1 e−t dt. The probability density function for the gamma distribution is given by. The mean of the gamma distribution is αβ and the variance (square of the standard deviation) is αβ2. | |
C10166 | Convenience sampling is a type of non-probability sampling, which doesn't include random selection of participants. The opposite is probability sampling, where participants are randomly selected, and each has an equal chance of being chosen. | |
C10167 | Bivariate analysis means the analysis of bivariate data. It is one of the simplest forms of statistical analysis, used to find out if there is a relationship between two sets of values. It usually involves the variables X and Y. | |
C10168 | The t distribution (aka, Student's t-distribution) is a probability distribution that is used to estimate population parameters when the sample size is small and/or when the population variance is unknown. | |
C10169 | When part of the memory network is activated, activation spreads along the associative pathways to related areas in memory. This spread of activation serves to make these related areas of the memory network more available for further cognitive processing (Balota & Lorch, 1986). | |
C10170 | Random Forest is less computationally expensive and does not require a GPU to finish training. A random forest can give you a different interpretation of a decision tree but with better performance. Neural Networks will require much more data than an everyday person might have on hand to actually be effective. | |
C10171 | Intuitively, two random variables X and Y are independent if knowing the value of one of them does not change the probabilities for the other one. In other words, if X and Y are independent, we can write P(Y=y|X=x)=P(Y=y), for all x,y. | |
C10172 | The probability of an outcome is interpreted as the long-run proportion of the time that the outcome would occur, if the experiment were repeated indefinitely. That is, probability is long-term relative frequency. | |
C10173 | The easiest way to convert categorical variables to continuous is by replacing raw categories with the average response value of the category. cutoff : minimum observations in a category. All the categories having observations less than the cutoff will be a different category. | |
C10174 | Most deep learning methods use neural network architectures, which is why deep learning models are often referred to as deep neural networks. A CNN convolves learned features with input data, and uses 2D convolutional layers, making this architecture well suited to processing 2D data, such as images. | |
C10175 | Random error varies unpredictably from one measurement to another, while systematic error has the same value or proportion for every measurement. Random errors are unavoidable, but cluster around the true value. | |
C10176 | Preparing Your Dataset for Machine Learning: 8 Basic Techniques That Make Your Data BetterArticulate the problem early.Establish data collection mechanisms.Format data to make it consistent.Reduce data.Complete data cleaning.Decompose data.Rescale data.Discretize data. | |
C10177 | “Candidate Sampling” training methods involve constructing a training task in which for each. training example. , we only need to evaluate. for a small set of candidate classes. | |
C10178 | When it comes to machine learning, topology is not as ubiquitous as local geometry, but in almost all cases where local geometry is useful so is topology. | |
C10179 | Pearson's product moment correlation coefficient (r) is given as a measure of linear association between the two variables: r² is the proportion of the total variance (s²) of Y that can be explained by the linear regression of Y on x. 1-r² is the proportion that is not explained by the regression. | |
C10180 | The comparison - wise error rate is the probability of a Type I error set by the experimentor for evaluating each comparison. The experiment - wise error rate is the probability of making at least one Type I error when performing the whole set of comparisons. | |
C10181 | – Validation set: A set of examples used to tune the parameters of a classifier, for example to choose the number of hidden units in a neural network. – Test set: A set of examples used only to assess the performance of a fully-specified classifier. These are the recommended definitions and usages of the terms. | |
C10182 | The set of all "normal" Turing machines, i.e., the set of all Turing machines, can compute all computable functions. The difference is that a single universal Turing machine can simulate the computation of all computable functions depending on how you interpret its input. | |
C10183 | To find the confidence interval in R, create a new data. frame with the desired value to predict. The prediction is made with the predict() function. The interval argument is set to 'confidence' to output the mean interval. | |
C10184 | API KPIs (Key Performance Indicators) Defining the key performance indicators (KPIs) for APIs being used is a critical part of understanding not just how they work but how well they can work and the impact they have on your services, users or partners. | |
C10185 | For the Wilcoxon test, a p-value is the probability of getting a test statistic as large or larger assuming both distributions are the same. In addition to a p-value we would like some estimated measure of how these distributions differ. The wilcox. test function provides this information when we set conf.int = TRUE . | |
C10186 | Simply put, homoscedasticity means “having the same scatter.” For it to exist in a set of data, the points must be about the same distance from the line, as shown in the picture above. The opposite is heteroscedasticity (“different scatter”), where points are at widely varying distances from the regression line. | |
C10187 | Rectifying activation functions were used to separate specific excitation and unspecific inhibition in the neural abstraction pyramid, which was trained in a supervised way to learn several computer vision tasks. | |
C10188 | MANOVA is useful in experimental situations where at least some of the independent variables are manipulated. It has several advantages over ANOVA. First, by measuring several dependent variables in a single experiment, there is a better chance of discovering which factor is truly important. | |
C10189 | The joint probability density function (joint pdf) is a function used to characterize the probability distribution of a continuous random vector. It is a multivariate generalization of the probability density function (pdf), which characterizes the distribution of a continuous random variable. | |
C10190 | The core idea is that we cannot know exactly how well an algorithm will work in practice (the true "risk") because we don't know the true distribution of data that the algorithm will work on, but we can instead measure its performance on a known set of training data (the "empirical" risk). | |
C10191 | Moment generating functions are a way to find moments like the mean(μ) and the variance(σ2). They are an alternative way to represent a probability distribution with a simple one-variable function. | |
C10192 | Software Testing MethodologiesFunctional vs. Non-functional Testing. Unit Testing. Unit testing is the first level of testing and is often performed by the developers themselves. Integration Testing. System Testing. Acceptance Testing. Performance Testing. Security Testing. Usability Testing.More items | |
C10193 | Shading units (or stream processors) are small processors within the graphics card that are responsible for processing different aspects of the image. This means that the more shading units that a graphics card has, the faster it will be able to allocate power to process the workload. | |
C10194 | 0:087:41Suggested clip · 120 secondsHow to Create a Multiple Regression Equation - Business Statistics YouTubeStart of suggested clipEnd of suggested clip | |
C10195 | Neural networks are designed to work just like the human brain does. In the case of recognizing handwriting or facial recognition, the brain very quickly makes some decisions. For example, in the case of facial recognition, the brain might start with “It is female or male? | |
C10196 | The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena. For example, heights, blood pressure, measurement error, and IQ scores follow the normal distribution. | |
C10197 | Start by learning key data analysis tools such as Microsoft Excel, Python, SQL and R. Excel is the most widely used spreadsheet program and is excellent for data analysis and visualization. Enroll in one of the free Excel courses and learn how to use this powerful software. | |
C10198 | Image recognition is the process of identifying and detecting an object or a feature in a digital image or video. This concept is used in many applications like systems for factory automation, toll booth monitoring, and security surveillance. Typical image recognition algorithms include: Optical character recognition. | |
C10199 | The defining characteristic of a Markov chain is that no matter how the process arrived at its present state, the possible future states are fixed. In other words, the probability of transitioning to any particular state is dependent solely on the current state and time elapsed. |
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