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C9200 | 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. | |
C9201 | Unlike the independent-samples t-test, the Mann-Whitney U test allows you to draw different conclusions about your data depending on the assumptions you make about your data's distribution. These different conclusions hinge on the shape of the distributions of your data, which we explain more about later. | |
C9202 | AlphaGo surprised the world with its so-called “move 37,” which human experts initially thought was a mistake, but which proved decisive in game two. Lee made his own impact with his “hand of God” play (move 78), which flummoxed the AI program and allowed Lee to win a single game. | |
C9203 | Multinomial logistic regression is a form of logistic regression used to predict a target variable have more than 2 classes. Now, there are two common methods to perform multi-class classification using the binary classification logistic regression algorithm: one-vs-all and one-vs-one. | |
C9204 | Pattern recognition is the process of classifying input data into objects or classes based on key features. Pattern recognition has applications in computer vision, radar processing, speech recognition, and text classification. | |
C9205 | SummaryUse the function cor. test(x,y) to analyze the correlation coefficient between two variables and to get significance level of the correlation.Three possible correlation methods using the function cor.test(x,y): pearson, kendall, spearman. | |
C9206 | AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time. | |
C9207 | Top 10 Machine Learning ApplicationsTraffic Alerts.Social Media.Transportation and Commuting.Products Recommendations.Virtual Personal Assistants.Self Driving Cars.Dynamic Pricing.Google Translate.More items• | |
C9208 | Skewness refers to distortion or asymmetry in a symmetrical bell curve, or normal distribution, in a set of data. If the curve is shifted to the left or to the right, it is said to be skewed. Skewness can be quantified as a representation of the extent to which a given distribution varies from a normal distribution. | |
C9209 | Disproportional vs. The main difference between the two sampling techniques is the proportion given to each stratum with respect to other strata. In proportional sampling, each stratum has the same sampling fraction while in disproportional sampling technique; the sampling fraction of each stratum varies. | |
C9210 | The above equation tells us that the value of a particular state is determined by the immediate reward plus the value of successor states when we are following a certain policy(π). | |
C9211 | The cumulative distribution function (CDF) of random variable X is defined as FX(x)=P(X≤x), for all x∈R.SolutionTo find the CDF, note that. To find P(2<X≤5), we can write P(2<X≤5)=FX(5)−FX(2)=3132−34=732. To find P(X>4), we can write P(X>4)=1−P(X≤4)=1−FX(4)=1−1516=116. | |
C9212 | In pattern recognition, information retrieval and classification (machine learning), precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the fraction of the total amount of relevant instances that were | |
C9213 | The negative binomial distribution is a probability distribution that is used with discrete random variables. This type of distribution concerns the number of trials that must occur in order to have a predetermined number of successes. | |
C9214 | Boosting is used to create a collection of predictors. In this technique, learners are learned sequentially with early learners fitting simple models to the data and then analysing data for errors. This process converts weak learners into better performing model. | |
C9215 | A Markov logic network is a first-order knowledge base with a weight attached to each formula, and can be viewed as a template for constructing Markov networks. Experiments with a real-world database and knowledge base illustrate the benefits of using MLNs over purely logical and purely probabilistic ap- proaches. | |
C9216 | If a variable can take on any value between its minimum value and its maximum value, it is called a continuous variable; otherwise, it is called a discrete variable. The number of heads could be any integer value between 0 and plus infinity. | |
C9217 | In particular, three datasets are commonly used in different stages of the creation of the model. The model is initially fit on a training dataset, which is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model. | |
C9218 | Quota sampling is different from stratified sampling, because in a stratified sample individuals within each stratum are selected at random. Quota sampling achieves a representative age distribution, but it isn't a random sample, because the sampling frame is unknown. | |
C9219 | Six Fundamental Methods to Generate a Random VariablePhysical sources.Empirical resampling.Pseudo random generators.Simulation/Game-play.Rejection Sampling.Transform methods. | |
C9220 | If a and b are two non-zero numbers, then the harmonic mean of a and b is a number H such that the numbers a, H, b are in H.P. We have H = 1/H = 1/2 (1/a + 1/b) ⇒ H = 2ab/a+b. | |
C9221 | Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model. | |
C9222 | Kalman filters combine two sources of information, the predicted states and noisy measurements, to produce optimal, unbiased estimates of system states. The filter is optimal in the sense that it minimizes the variance in the estimated states. | |
C9223 | In geometry, a hyperplane is a subspace whose dimension is one less than that of its ambient space. If a space is 3-dimensional then its hyperplanes are the 2-dimensional planes, while if the space is 2-dimensional, its hyperplanes are the 1-dimensional lines. | |
C9224 | Reinforcement Learning : Simple reward feedback is required for the agent to learn its behavior; this is known as the reinforcement signal. In the problem, an agent is supposed to decide the best action to select based on his current state. When this step is repeated, the problem is known as a Markov Decision Process. | |
C9225 | It is well known that maximum likelihood estimators are often biased, and it is of use to estimate the expected bias so that we can reduce the mean square errors of our parameter estimates. In both problems, the first-order bias is found to be linear in the parameter and the sample size. | |
C9226 | In statistics, an estimator is a rule for calculating an estimate of a given quantity based on observed data: thus the rule (the estimator), the quantity of interest (the estimand) and its result (the estimate) are distinguished. There are point and interval estimators. | |
C9227 | Introduction to K-means Clustering. K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups). The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable K. | |
C9228 | Interpret the key results for CovarianceIf both variables tend to increase or decrease together, the coefficient is positive.If one variable tends to increase as the other decreases, the coefficient is negative. | |
C9229 | An activation function is a node that you add to the output layer or between two layers of any neural network. It is also known as the transfer function. It is used to determine the output of neural network layer in between 0 to 1 or -1 to 1 etc. | |
C9230 | The accuracy of location determination is improved because WiFi radio signals are one of the best ways to determine where you are. This technique works more reliably than GPS in urban environments, indoors, and other places where GPS signals get distorted by radio interference. | |
C9231 | 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. | |
C9232 | The unmixed second-order partial derivatives, fxx f x x and fyy, f y y , tell us about the concavity of the traces. The mixed second-order partial derivatives, fxy f x y and fyx, f y x , tell us how the graph of f twists. | |
C9233 | Definition. A Turing Machine (TM) is a mathematical model which consists of an infinite length tape divided into cells on which input is given. After reading an input symbol, it is replaced with another symbol, its internal state is changed, and it moves from one cell to the right or left. | |
C9234 | The discriminator in a GAN is simply a classifier. It tries to distinguish real data from the data created by the generator. It could use any network architecture appropriate to the type of data it's classifying. Figure 1: Backpropagation in discriminator training. | |
C9235 | A studentized residual is calculated by dividing the residual by an estimate of its standard deviation. The standard deviation for each residual is computed with the observation excluded. For this reason, studentized residuals are sometimes referred to as externally studentized residuals. | |
C9236 | In data mining, association rules are useful for analyzing and predicting customer behavior. They play an important part in customer analytics, market basket analysis, product clustering, catalog design and store layout. Programmers use association rules to build programs capable of machine learning. | |
C9237 | If f (x, y) > T then f (x, y) = 0 else f (x, y) = 255 where f (x, y) = Coordinate Pixel Value T = Threshold Value. In OpenCV with Python, the function cv2. threshold is used for thresholding. | |
C9238 | 1 Answer. No! There is no limit whatsoever on the size of the output relative to the size of the input. But in most cases, a higher number of outputs is not necessary at all. | |
C9239 | General method. Optimal control deals with the problem of finding a control law for a given system such that a certain optimality criterion is achieved. An optimal control is a set of differential equations describing the paths of the control variables that minimize the cost function. | |
C9240 | In probability theory and statistics, Bayes's theorem (alternatively Bayes's law or Bayes's rule), named after Reverend Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions that might be related to the event. Bayesian inference is fundamental to Bayesian statistics. | |
C9241 | The decision boundary Let's suppose we define a line that is equal to zero along this decision boundary. For example, in the following graph, z=6−x1 represents a decision boundary for which any values of x1>6 will return a negative value for z and any values of x1<6 will return a positive value for z. | |
C9242 | Definition: Probability sampling is defined as a sampling technique in which the researcher chooses samples from a larger population using a method based on the theory of probability. For a participant to be considered as a probability sample, he/she must be selected using a random selection. Select your respondents. | |
C9243 | Computational photography is a digital image processing technique that uses algorithms to replace optical processes, and it seeks to improve image quality by using machine vision to identify the content of an image. “For example, we use AI to train algorithms about the features of people's faces.” | |
C9244 | A stratified sample is one that ensures that subgroups (strata) of a given population are each adequately represented within the whole sample population of a research study. For example, one might divide a sample of adults into subgroups by age, like 18–29, 30–39, 40–49, 50–59, and 60 and above. | |
C9245 | Like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables. | |
C9246 | Linear regressions are among the simplest types of predictive models. Other more complex predictive models include decision trees, k-means clustering and Bayesian inference, to name just a few potential methods. The most complex area of predictive modeling is the neural network. | |
C9247 | Similar to Correlation Coefficient, the range of values of MCC lie between -1 to +1. A model with a score of +1 is a perfect model and -1 is a poor model. | |
C9248 | Gaussian RBF(Radial Basis Function) is another popular Kernel method used in SVM models for more. RBF kernel is a function whose value depends on the distance from the origin or from some point. Gaussian Kernel is of the following format; ||X1 — X2 || = Euclidean distance between X1 & X2. | |
C9249 | 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. | |
C9250 | Understanding the Correlation Coefficient A value of exactly 1.0 means there is a perfect positive relationship between the two variables. For a positive increase in one variable, there is also a positive increase in the second variable. | |
C9251 | the expected rate of occurrences | |
C9252 | At a higher level, the chief difference between the L1 and the L2 terms is that the L2 term is proportional to the square of the β values, while the L1 norm is proportional the absolute value of the values in β. | |
C9253 | How to Handle Imbalanced DatasetChange the evaluation matrix. If we apply the wrong evaluation matrix on the imbalanced dataset, it can give us misleading results. Resample the dataset. Resample means to change the distribution of the imbalance classes in the dataset. Change the algorithm and approach to the problem. | |
C9254 | POS tags make it possible for automatic text processing tools to take into account which part of speech each word is. This facilitates the use of linguistic criteria in addition to statistics. | |
C9255 | Perceptron Learning Rule The Perceptron receives multiple input signals, and if the sum of the input signals exceeds a certain threshold, it either outputs a signal or does not return an output. In the context of supervised learning and classification, this can then be used to predict the class of a sample. | |
C9256 | In other words, as long as each sample contains a very large number of observations, the sampling distribution of the mean must be normal. So if we're going to assume one thing for all situations, it has to be a normal, because the normal is always correct for large samples. | |
C9257 | Statistics Definitions > A random walk is a sequence of discrete, fixed-length steps in random directions. Random walks may be 1-dimensional, 2-dimensional, or n-dimensional for any n. A random walk can also be confined to a lattice. | |
C9258 | Five tips to prevent confirmation bias Encourage and carefully consider critical views on the working hypothesis. Ensure that all stakeholders examine the primary data. Do not rely on analysis and summary from a single individual. Design experiments to actually test the hypothesis. | |
C9259 | 3:295:25Suggested clip · 42 secondsExcel Statistics 55.5: Bayes Theorem Posterior Probabilities - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C9260 | joint entropy is the amount of information in two (or more) random variables; conditional entropy is the amount of information in one random variable given we already know the other. | |
C9261 | Recurrent Neural Network(RNN) are a type of Neural Network where the output from previous step are fed as input to the current step. Thus RNN came into existence, which solved this issue with the help of a Hidden Layer. | |
C9262 | Backward elimination is a feature selection technique while building a machine learning model. It is used to remove those features that do not have a significant effect on the dependent variable or prediction of output. | |
C9263 | The cumulative distribution function (CDF) calculates the cumulative probability for a given x-value. Use the CDF to determine the probability that a random observation that is taken from the population will be less than or equal to a certain value. | |
C9264 | there are three general categories of learning that artificial intelligence (AI)/machine learning utilizes to actually learn. They are Supervised Learning, Unsupervised Learning and Reinforcement learning. The machine then maps the inputs and the outputs. | |
C9265 | The gradients carry information used in the RNN parameter update and when the gradient becomes smaller and smaller, the parameter updates become insignificant which means no real learning is done. Let's have a short reminder of how RNNs look like. | |
C9266 | Predictive modeling is the process of using known results to create, process, and validate a model that can be used to forecast future outcomes. It is a tool used in predictive analytics, a data mining technique that attempts to answer the question "what might possibly happen in the future?" | |
C9267 | Statistical Significance Definition Statistical significance is the likelihood that the difference in conversion rates between a given variation and the baseline is not due to random chance. It also means that there is a 5% chance that you could be wrong. | |
C9268 | Firstly, while the sample variance (using Bessel's correction) is an unbiased estimator of the population variance, its square root, the sample standard deviation, is a biased estimate of the population standard deviation; because the square root is a concave function, the bias is downward, by Jensen's inequality. | |
C9269 | The generator is a convolutional neural network and the discriminator is a deconvolutional neural network. The goal of the generator is to artificially manufacture outputs that could easily be mistaken for real data. The goal of the discriminator is to identify which outputs it receives have been artificially created. | |
C9270 | In artificial intelligence research, commonsense knowledge consists of facts about the everyday world, such as "Lemons are sour", that all humans are expected to know. Common sense knowledge also helps to solve problems in the face of incomplete information. | |
C9271 | Linear regression is used to find the best fitting line between all the points of your dataset (by computing the minimum of a given distance), it does not, in itself, reduce the dimensionality of your data. | |
C9272 | An ROC curve shows the relationship between clinical sensitivity and specificity for every possible cut-off. The ROC curve is a graph with: The x-axis showing 1 – specificity (= false positive fraction = FP/(FP+TN)) The y-axis showing sensitivity (= true positive fraction = TP/(TP+FN)) | |
C9273 | Systematic sampling is a type of probability sampling method in which sample members from a larger population are selected according to a random starting point but with a fixed, periodic interval. This interval, called the sampling interval, is calculated by dividing the population size by the desired sample size. | |
C9274 | Yes, it is ok to run a Pearson r correlation using two binary coded variables*. Pearson r has a special name in that situation (phi coefficient). There are some special issues when you look at correlations between binary or dichotomous variables. | |
C9275 | Coefficient of variation is a measure used to assess the total risk per unit of return of an investment. It is calculated by dividing the standard deviation of an investment by its expected rate of return. Coefficient of variation provides a standardized measure of comparing risk and return of different investments. | |
C9276 | Dimensional analysis provides you with an alternative approach to problem solving. Problems in which a measurement with one unit is converted to an equivalent measurement with another unit are easily solved using dimensional analysis. They form cations with positive charges equal to their group number. | |
C9277 | An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes (for example, determine whether a given email is "spam" or "non-spam"). This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns. | |
C9278 | 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. | |
C9279 | The sobel operator is very similar to Prewitt operator. It is also a derivate mask and is used for edge detection. Like Prewitt operator sobel operator is also used to detect two kinds of edges in an image: Vertical direction. | |
C9280 | Filters typically are applied to data in the data processing stage or the preprocessing stage. Filters enhance the clarity of the signal that's used for machine learning. | |
C9281 | Clipping path is the Photoshop technique — used with the Pen Tool — to remove the background from an image. Clipping path is generally used when the subject of the image has sharp, smooth edges. This allows the clipping path to stay straight. | |
C9282 | In deep multilayer Perceptron networks, exploding gradients can result in an unstable network that at best cannot learn from the training data and at worst results in NaN weight values that can no longer be updated. … exploding gradients can make learning unstable. | |
C9283 | ReLU is important because it does not saturate; the gradient is always high (equal to 1) if the neuron activates. As long as it is not a dead neuron, successive updates are fairly effective. ReLU is also very quick to evaluate. | |
C9284 | A positive coefficient indicates that as the value of the independent variable increases, the mean of the dependent variable also tends to increase. A negative coefficient suggests that as the independent variable increases, the dependent variable tends to decrease. | |
C9285 | The item response theory (IRT), also known as the latent response theory refers to a family of mathematical models that attempt to explain the relationship between latent traits (unobservable characteristic or attribute) and their manifestations (i.e. observed outcomes, responses or performance). | |
C9286 | Sometimes we want to know the probability of getting one result or another. When events are mutually exclusive and we want to know the probability of getting one event OR another, then we can use the OR rule. P(A or B) = P(A) + P(B) for mutually exclusive events. | |
C9287 | All Answers (8) A matrix is a two dimensional array of numbers (or values from some field or ring). A 2-rank tensor is a linear map from two vector spaces, over some field such as the real numbers, to that field. | |
C9288 | Whenever we train our own Neural Networks, we need to take care of something called the generalization of the Neural Network. This essentially means how good our model is at learning from the given data and applying the learnt information elsewhere. | |
C9289 | A fancy name for training: the selection of parameter values, which are optimal in some desired sense (eg. minimize an objective function you choose over a dataset you choose). The parameters are the weights and biases of the network. | |
C9290 | Choosing the Best Algorithm for your Classification Model.•Read the Data.• Create Dependent and Independent Datasets based on our Dependent and Independent features.•Split the Data into Training and Testing sets.• Train our Model for different Classification Algorithms namely XGB Classifier, Decision Tree, SVM Classifier, Random Forest Classifier.•Select the Best Algorithm. | |
C9291 | Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting. | |
C9292 | Structured data is highly specific and is stored in a predefined format, where unstructured data is a conglomeration of many varied types of data that are stored in their native formats. Structured data is commonly stored in data warehouses and unstructured data is stored in data lakes. | |
C9293 | Wikipedia defines Taguchi loss function as the graphical depiction of loss to describe a phenomenon affecting the value of products produced by a company. It emphasizes the need for incorporating quality and reliability at the design stage, prior to production. | |
C9294 | In probability theory, an event is an outcome or defined collection of outcomes of a random experiment. Since the collection of all possible outcomes to a random experiment is called the sample space, another definiton of event is any subset of a sample space. | |
C9295 | 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. | |
C9296 | Finally, the test dataset is a dataset used to provide an unbiased evaluation of a final model fit on the training dataset. If the data in the test dataset has never been used in training (for example in cross-validation), the test dataset is also called a holdout dataset. | |
C9297 | You can perform statistical analysis with the help of Excel. If it is not there, go to Excel → File → Options → Add-in and enable the Analysis ToolPak by selecting the Excel Add-ins option in manage tab and then, click GO. This will open a small window; select the Analysis ToolPak option and enable it. | |
C9298 | An adaptive filter is a system with a linear filter that has a transfer function controlled by variable parameters and a means to adjust those parameters according to an optimization algorithm. The closed loop adaptive filter uses feedback in the form of an error signal to refine its transfer function. | |
C9299 | Data preprocessing in Machine Learning refers to the technique of preparing (cleaning and organizing) the raw data to make it suitable for a building and training Machine Learning models. |
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