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C5700 | Greedy is an algorithm taking the best possible at the current stage without violating constraints. Often it does not produce optimal solution, but it always produces a feasible solution by definition. It is still used quite often in many areas because of its simplicity and speed. | |
C5701 | s2 (sample variance) is the best point estimate for population variance o2. s (sample standard deviation) is the best point estimate for the population standard deviation o. | |
C5702 | No because in simple tabular q learning you don't need to use neural networks. It's sufficient to keep your q values estimates in a lookup table. Experience replay was designed to alleviate problem arising from using deep neural networks on high dimensional state space such as atari screen doing Q learning (DQN). | |
C5703 | Stochastic processes are widely used as mathematical models of systems and phenomena that appear to vary in a random manner. | |
C5704 | The distribution of a categorical variable lists all of the values the variable takes and how often it takes each of these values. | |
C5705 | Computer vision is the process of understanding digital images and videos using computers. It seeks to automate tasks that human vision can achieve. This involves methods of acquiring, processing, analyzing, and understanding digital images, and extraction of data from the real world to produce information. | |
C5706 | Activation functions are mathematical equations that determine the output of a neural network. The function is attached to each neuron in the network, and determines whether it should be activated (“fired”) or not, based on whether each neuron's input is relevant for the model's prediction. | |
C5707 | William Sealy Gosset | |
C5708 | Propositional Logic converts a complete sentence into a symbol and makes it logical whereas in First-Order Logic relation of a particular sentence will be made that involves relations, constants, functions, and constants. | |
C5709 | The normal approximation gives us a very poor result without the continuity correction. We make a continuity correction when p is > 0.5. | |
C5710 | In probability theory, a probability density function (PDF), or density of a continuous random variable, is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the | |
C5711 | Dictionary learning is learning a set of atoms so that a given image can be well approximated by a sparse linear combination of these learned atoms, while deep learning methods aim at extracting deep semantic feature representations via a deep network. | |
C5712 | Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample. In double-stage sampling, you select a random sample of units from within the clusters. | |
C5713 | Instance-Based Learning: The raw training instances are used to make predictions. As such KNN is often referred to as instance-based learning or a case-based learning (where each training instance is a case from the problem domain). As such KNN is referred to as a non-parametric machine learning algorithm. | |
C5714 | Digital Signal Processors (DSP) take real-world signals like voice, audio, video, temperature, pressure, or position that have been digitized and then mathematically manipulate them. In the real-world, analog products detect signals such as sound, light, temperature or pressure and manipulate them. | |
C5715 | Log-likelihood is a measure of model fit. The higher the number, the better the fit. This is usually obtained from statistical output.AICc = -2(log-likelihood) + 2K + (2K(K+1)/(n-K-1))n = sample size,K= number of model parameters,Log-likelihood is a measure of model fit. | |
C5716 | Exclusive Class Interval: When the lower limit is included, but the upper limit is excluded, then it is an exclusive class interval. | |
C5717 | Convergence in probability implies convergence in distribution. In the opposite direction, convergence in distribution implies convergence in probability when the limiting random variable X is a constant. Convergence in probability does not imply almost sure convergence. | |
C5718 | If you are working on a classification problem, the best score is 100% accuracy. If you are working on a regression problem, the best score is 0.0 error. These scores are an impossible to achieve upper/lower bound. | |
C5719 | he confidence interval tells you more than just the possible range around the estimate. It also tells you about how stable the estimate is. A stable estimate is one that would be close to the same value if the survey were repeated. | |
C5720 | The Markov blanket of a node contains the node's parents, children and children's parents (see figure 4). When predicting the behavior of a specific node in the network, the nodes that have to be considered for this prediction are the nodes belonging to the Markov blanket of the chosen node ( Yap et al., 2008). | |
C5721 | The null hypothesis is a general statement that states that there is no relationship between two phenomenons under consideration or that there is no association between two groups. An alternative hypothesis is a statement that describes that there is a relationship between two selected variables in a study. | |
C5722 | Linear means something related to a line. A non-linear equation is such which does not form a straight line. It looks like a curve in a graph and has a variable slope value. The major difference between linear and nonlinear equations is given here for the students to understand it in a more natural way. | |
C5723 | Fitting a neural network involves using a training dataset to update the model weights to create a good mapping of inputs to outputs. Training a neural network involves using an optimization algorithm to find a set of weights to best map inputs to outputs. | |
C5724 | Confirmation bias is the tendency of people to favor information that confirms their existing beliefs or hypotheses. People display this bias when they gather or recall information selectively, or when they interpret it in a biased way. | |
C5725 | Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups similar objects into groups called clusters. The endpoint is a set of clusters, where each cluster is distinct from each other cluster, and the objects within each cluster are broadly similar to each other. | |
C5726 | Step 1: Prepare a table containing less than type cumulative frequency with the help of given frequencies. belongs. Class-interval of this cumulative frequency is the median class-interval. Step 3 : Find out the frequency f and lower limit l of this median class. | |
C5727 | Random forests is a robust algorithm that can be used for remotely sensed data classification and regression. Performance of random forests is on par with other machine learning algorithms but it is much easier to use and more forgiving with regard to over fitting and outliers than other algorithms. | |
C5728 | 3. OneWay ANOVA – Similar to a ttest, except that this test can be used to compare the means from THREE OR MORE groups (ttests can only compare TWO groups at a time, and for statistical reasons it is generally considered “illegal” to use ttests over and over again on different groups from a single experiment). | |
C5729 | The sampling rate determines the spatial resolution of the digitized image, while the quantization level determines the number of grey levels in the digitized image. The transition between continuous values of the image function and its digital equivalent is called quantization. | |
C5730 | Bias can creep into algorithms in several ways. AI systems learn to make decisions based on training data, which can include biased human decisions or reflect historical or social inequities, even if sensitive variables such as gender, race, or sexual orientation are removed. | |
C5731 | 1 Answer. Reinforcement learning is a collection of different approaches/solutions to problems framed as Markov Decision Processes. The Policy results from the RL model, so it is not input data. | |
C5732 | Spurious states are patterns , where is the set of patterns to be memorized. In other words, they correspond to local minima in the energy function that shouldn't be there. They can be composed of various combinations of the original patterns or simply the negation of any pattern in the original pattern set. | |
C5733 | To solve the problem using logistic regression we take two parameters w, which is n dimensional vector and b which is a real number. The logistic regression model to solve this is : Equation for Logistic Regression. We apply sigmoid function so that we contain the result of ŷ between 0 and 1 (probability value). | |
C5734 | Similarly, the probability density function of a continuous random variable can be obtained by differentiating the cumulative distribution. The c.d.f. can be used to find out the probability of a random variable being between two values: P(s ≤ X ≤ t) = the probability that X is between s and t. | |
C5735 | In statistics, bootstrapping is any test or metric that relies on random sampling with replacement. Bootstrapping allows assigning measures of accuracy (defined in terms of bias, variance, confidence intervals, prediction error or some other such measure) to sample estimates. 231 views. | |
C5736 | Cross Entropy is definitely a good loss function for Classification Problems, because it minimizes the distance between two probability distributions - predicted and actual. So cross entropy make sure we are minimizing the difference between the two probability. This is the reason. | |
C5737 | Inverted dropout is a variant of the original dropout technique developed by Hinton et al. Just like traditional dropout, inverted dropout randomly keeps some weights and sets others to zero. In contrast, traditional dropout requires scaling to be implemented during the test phase. | |
C5738 | The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems. | |
C5739 | Informally: a hat is an estimate that is sometimes calculated by the arithmetic mean, but can be some other type of estimate (median, mode, some kind of maximum likelihood estimate, etc.). Bar is an estimate that (usually) happens to be an arithmetic mean. | |
C5740 | Principal Component Analysis (PCA) is an unsupervised, non-parametric statistical technique primarily used for dimensionality reduction in machine learning. High dimensionality means that the dataset has a large number of features. PCA can also be used to filter noisy datasets, such as image compression. | |
C5741 | Automatic thresholding Select initial threshold value, typically the mean 8-bit value of the original image. Divide the original image into two portions; Pixel values that are less than or equal to the threshold; background. Pixel values greater than the threshold; foreground. | |
C5742 | Signal Detection Theory assumes that, given this situation, we make our judgment of whether the signal is present, or not, by setting up a Criterion value, β (beta). When a value is picked up that exceeds β, we respond that the signal is present. | |
C5743 | Bootstrapping is any test or metric that uses random sampling with replacement, and falls under the broader class of resampling methods. Bootstrapping assigns measures of accuracy (bias, variance, confidence intervals, prediction error, etc.) to sample estimates. | |
C5744 | 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. | |
C5745 | As far as I know, in Bag Of Words method, features are a set of words and their frequency counts in a document. In another hand, N-grams, for example unigrams does exactly the same, but it does not take into consideration the frequency of occurance of a word. | |
C5746 | It is one of the more common descriptive statistics functions used to calculate uncertainty.How to CalculateSubtract each value from the mean.Square each value in step 1.Add all of the values from step 2.Count the number of values and Subtract it by 1.Divide step 3 by step 4.Calculate the Square Root of step 5. | |
C5747 | 2:426:07Suggested clip · 113 secondsUnivariate analysis SPSS - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C5748 | 3 nodes | |
C5749 | Depth is the number of filters. Depth column (or fibre) is the set of neurons that are all pointing to the same receptive field. Stride has the objective of producing smaller output volumes spatially. For example, if a stride=2, the filter will shift by the amount of 2 pixels as it convolves around the input volume. | |
C5750 | In statistical theory, the field of high-dimensional statistics studies data whose dimension is larger than dimensions considered in classical multivariate analysis. In many applications, the dimension of the data vectors may be larger than the sample size. | |
C5751 | In Data Science, bias is a deviation from expectation in the data. More fundamentally, bias refers to an error in the data. But, the error is often subtle or goes unnoticed. | |
C5752 | The GRU is like a long short-term memory (LSTM) with a forget gate, but has fewer parameters than LSTM, as it lacks an output gate. GRU's performance on certain tasks of polyphonic music modeling, speech signal modeling and natural language processing was found to be similar to that of LSTM. | |
C5753 | Researchers can take a number of steps to account for regression to the mean and avoid making incorrect conclusions. The best way is to remove the effect of regression to the mean during the design stage by conducting a randomized controlled trial (RCT). | |
C5754 | Introduction to Association Rules Association rule is unsupervised learning where algorithm tries to learn without a teacher as data are not labelled. Association rule is descriptive not the predictive method, generally used to discover interesting relationship hidden in large datasets. | |
C5755 | The terms cost and loss functions almost refer to the same meaning. But, loss function mainly applies for a single training set as compared to the cost function which deals with a penalty for a number of training sets or the complete batch. The cost function is calculated as an average of loss functions. | |
C5756 | An embedding is a mapping of a discrete — categorical — variable to a vector of continuous numbers. In the context of neural networks, embeddings are low-dimensional, learned continuous vector representations of discrete variables. | |
C5757 | The cumulative distribution function, CDF, or cumulant is a function derived from the probability density function for a continuous random variable. It gives the probability of finding the random variable at a value less than or equal to a given cutoff. | |
C5758 | The final precision-recall curve metric is average precision (AP) and of most interest to us here. It is calculated as the weighted mean of precisions achieved at each threshold, with the increase in recall from the previous threshold used as the weight. | |
C5759 | Two random variables X and Y are said to be bivariate normal, or jointly normal, if aX+bY has a normal distribution for all a,b∈R. In the above definition, if we let a=b=0, then aX+bY=0. We agree that the constant zero is a normal random variable with mean and variance 0. | |
C5760 | Hierarchical Clustering Algorithm Also called Hierarchical cluster analysis or HCA is an unsupervised clustering algorithm which involves creating clusters that have predominant ordering from top to bottom. | |
C5761 | 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. | |
C5762 | The basic procedure is:State the null hypothesis H0 and the alternative hypothesis HA.Set the level of significance .Calculate the test statistic: z = p ^ − p o p 0 ( 1 − p 0 ) n.Calculate the p-value.Make a decision. Check whether to reject the null hypothesis by comparing p-value to . | |
C5763 | In statistics, a sequence (or a vector) of random variables is homoscedastic /ˌhoʊmoʊskəˈdæstɪk/ if all its random variables have the same finite variance. This is also known as homogeneity of variance. The complementary notion is called heteroscedasticity. | |
C5764 | Strictly speaking, a neural network (also called an “artificial neural network”) is a type of machine learning model that is usually used in supervised learning. A perceptron is a simplified model of a human neuron that accepts an input and performs a computation on that input. | |
C5765 | The Sobel filter is used for edge detection. It works by calculating the gradient of image intensity at each pixel within the image. It finds the direction of the largest increase from light to dark and the rate of change in that direction. | |
C5766 | Analysis of covariance (ANCOVA) is a general linear model which blends ANOVA and regression. Mathematically, ANCOVA decomposes the variance in the DV into variance explained by the CV(s), variance explained by the categorical IV, and residual variance. | |
C5767 | Data is the currency of applied machine learning. Resampling is a methodology of economically using a data sample to improve the accuracy and quantify the uncertainty of a population parameter. Resampling methods, in fact, make use of a nested resampling method. | |
C5768 | Bayesian networks and neural networks are not exclusive of each other. In fact, Bayesian networks are just another term for "directed graphical model". A neural networks is used to implemented p(x|z) and an approximation to its inverse: q(z|x)≈p(z|x). | |
C5769 | The solution involves four steps.Make sure the samples from each population are big enough to model differences with a normal distribution. Find the mean of the difference in sample proportions: E(p1 - p2) = P1 - P2 = 0.52 - 0.47 = 0.05.Find the standard deviation of the difference. Find the probability. | |
C5770 | Autocorrelation, also known as serial correlation, is the correlation of a signal with a delayed copy of itself as a function of delay. Informally, it is the similarity between observations as a function of the time lag between them. | |
C5771 | DeepMind | |
C5772 | How to Use K-means Cluster Algorithms in Predictive AnalysisPick k random items from the dataset and label them as cluster representatives.Associate each remaining item in the dataset with the nearest cluster representative, using a Euclidean distance calculated by a similarity function.Recalculate the new clusters' representatives.More items | |
C5773 | Entropy, the measure of a system's thermal energy per unit temperature that is unavailable for doing useful work. Because work is obtained from ordered molecular motion, the amount of entropy is also a measure of the molecular disorder, or randomness, of a system. | |
C5774 | When resources are limited, populations exhibit logistic growth. In logistic growth, population expansion decreases as resources become scarce, leveling off when the carrying capacity of the environment is reached, resulting in an S-shaped curve. | |
C5775 | Calculating the SVD consists of finding the eigenvalues and eigenvectors of AAT and ATA. The eigenvectors of ATA make up the columns of V , the eigenvectors of AAT make up the columns of U. Also, the singular values in S are square roots of eigenvalues from AAT or ATA. | |
C5776 | A posteriori comes from Latin and literally translates as “from the latter” or “from the one behind.” It's often applied to things involving inductive reasoning, which uses specific instances to arrive at a general principle or law (from effect to cause). | |
C5777 | There are two types of factor analyses, exploratory and confirmatory. Exploratory factor analysis (EFA) is method to explore the underlying structure of a set of observed variables, and is a crucial step in the scale development process. The first step in EFA is factor extraction. | |
C5778 | The Spearman correlation is the same as the Pearson correlation, but it is used on data from an ordinal scale. Which situation would be appropriate for obtaining a phi-coefficient with a Pearson test? | |
C5779 | The larger the sample size, the greater the likelihood that sample statistics will accurately reflect population parameters. The larger the sample size, the smaller the sampling error. | |
C5780 | In Semantic networks, we can represent our knowledge in the form of graphical networks. This network consists of nodes representing objects and arcs which describe the relationship between those objects. Semantic networks can categorize the object in different forms and can also link those objects. | |
C5781 | So, a highly significant intercept in your model is generally not a problem. By the same token, if the intercept is not significant you usually would not want to remove it from the model because by doing this you are creating a model that says that the response function must be zero when the predictors are all zero. | |
C5782 | You simply measure the number of correct decisions your classifier makes, divide by the total number of test examples, and the result is the accuracy of your classifier. It's that simple. | |
C5783 | Histograms are sometimes called Frequency Plots while boxplots are referred to as Box-and-Whisker Plots. Histograms and boxplots can be drawn either vertically or horizontally. A histogram is normally used for continuous data while a bar chart is a plot of count data. | |
C5784 | Empirical and priori probabilities generally do not vary from person to person, and they are often grouped as objective probabilities. Subjective probability is a probability based on personal or subjective judgment. | |
C5785 | Organizations that capitalize on big data stand apart from traditional data analysis environments in three key ways: They pay attention to data flows as opposed to stocks. They rely on data scientists and product and process developers rather than data analysts. | |
C5786 | Statisticians use variance to see how individual numbers relate to each other within a data set, rather than using broader mathematical techniques such as arranging numbers into quartiles. One drawback to variance is that it gives added weight to outliers, the numbers that are far from the mean. | |
C5787 | The Bayes theorem describes the probability of an event based on the prior knowledge of the conditions that might be related to the event. If we know the conditional probability , we can use the bayes rule to find out the reverse probabilities . | |
C5788 | 0:3213:58Suggested clip · 112 secondsSurvival Analysis in R - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C5789 | Model averaging refers to the practice of using several models at once for making predictions (the focus of our review), or for inferring parameters (the focus of other papers, and some recent controversy, see, e.g. Banner & Higgs, 2017). | |
C5790 | R is a very dynamic and versatile programming language for data science. This article deals with classification in R. Generally classifiers in R are used to predict specific category related information like reviews or ratings such as good, best or worst. Various Classifiers are: Decision Trees. | |
C5791 | 2.4. 7 Cosine Similarity Cosine similarity measures the similarity between two vectors of an inner product space. It is measured by the cosine of the angle between two vectors and determines whether two vectors are pointing in roughly the same direction. It is often used to measure document similarity in text analysis. | |
C5792 | 3 layers | |
C5793 | Entropy measures the expected (i.e., average) amount of information conveyed by identifying the outcome of a random trial. This implies that casting a die has higher entropy than tossing a coin because each outcome of a die toss has smaller probability (about ) than each outcome of a coin toss ( ). | |
C5794 | 2:528:15Suggested clip · 90 secondsUnit Conversion Using Dimensional Analysis Tutorial (Factor Label YouTubeStart of suggested clipEnd of suggested clip | |
C5795 | Decision tree builds classification or regression models in the form of a tree structure. It breaks down a data set into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes. | |
C5796 | Summary: Chaos theory is a mathematical theory that can be used to explain complex systems such as weather, astronomy, politics, and economics. Although many complex systems appear to behave in a random manner, chaos theory shows that, in reality, there is an underlying order that is difficult to see. | |
C5797 | Statistical Methods for Finding the Best Regression ModelAdjusted R-squared and Predicted R-squared: Generally, you choose the models that have higher adjusted and predicted R-squared values. P-values for the predictors: In regression, low p-values indicate terms that are statistically significant.More items• | |
C5798 | Standard error is used in inferential stats to see whether the sample stat that we get from one sample is larger or smaller than the average differences of the stat (variance or error) of certain stat due to chance. | |
C5799 | Similarly, if a matrix has two entries in each column, then it must have two rows. So, it follows that in order for matrix multiplication to be defined, the number of columns in the first matrix must be equal to the number of rows in the second matrix. |
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