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C4800 | A simple way to get true randomness is to use Random.org. The randomness comes from atmospheric noise, which for many purposes is better than the pseudo-random number algorithms typically used in computer programs. Since you're going to simulate randomness, you are going to end up using pseudorandom number generator. | |
C4801 | Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. | |
C4802 | A recursive system is a system in which current output depends on previous output(s) and input(s) but in non-recursive system current output does not depend on previous output(s). | |
C4803 | Clustering is considered unsupervised learning, because there's no labeled target variable in clustering. Clustering algorithms try to, well, cluster data points into similar groups (or… clusters) based on different characteristics of the data. | |
C4804 | Machine learning, a subset of artificial intelligence (AI), depends on the quality, objectivity and size of training data used to teach it. Machine learning bias generally stems from problems introduced by the individuals who design and/or train the machine learning systems. | |
C4805 | Even though it evaluates the upper tail area, the chi-square test is regarded as a two-tailed test (non-directional), since it is basically just asking if the frequencies differ. | |
C4806 | A residual plot is a graph that shows the residuals on the vertical axis and the independent variable on the horizontal axis. If the points in a residual plot are randomly dispersed around the horizontal axis, a linear regression model is appropriate for the data; otherwise, a nonlinear model is more appropriate. | |
C4807 | Normally distributed data The normal distribution is symmetric, so it has no skew (the mean is equal to the median). On a Q-Q plot normally distributed data appears as roughly a straight line (although the ends of the Q-Q plot often start to deviate from the straight line). | |
C4808 | The coefficient of variation (CV) is the ratio of the standard deviation to the mean. The higher the coefficient of variation, the greater the level of dispersion around the mean. The lower the value of the coefficient of variation, the more precise the estimate. | |
C4809 | Deep metric learning (DML) is an emerging field in metric learning by introducing deep neural network. Taking advantage of the nonlinear feature representation learning ability of deep learning and discrimination power of metric learning, DML is widely applied in various computer vision tasks. | |
C4810 | Bias machine learning can even be applied when interpreting valid or invalid results from an approved data model. Nearly all of the common machine learning biased data types come from our own cognitive biases. Some examples include Anchoring bias, Availability bias, Confirmation bias, and Stability bias. | |
C4811 | Definitions. The median (middle quartile) marks the mid-point of the data and is shown by the line that divides the box into two parts. Half the scores are greater than or equal to this value and half are less. The middle “box” represents the middle 50% of scores for the group. | |
C4812 | As you experiment with your algorithm to try and improve your model, your loss function will tell you if you're getting(or reaching) anywhere. At its core, a loss function is a measure of how good your prediction model does in terms of being able to predict the expected outcome(or value). | |
C4813 | However, experts expect that it won't be until 2060 until AGI has gotten good enough to pass a "consciousness test". In other words, we're probably looking at 40 years from now before we see an AI that could pass for a human. | |
C4814 | Assumptions. The assumptions of discriminant analysis are the same as those for MANOVA. The analysis is quite sensitive to outliers and the size of the smallest group must be larger than the number of predictor variables. Multivariate normality: Independent variables are normal for each level of the grouping variable. | |
C4815 | A Multi Layer Perceptron (MLP) contains one or more hidden layers (apart from one input and one output layer). While a single layer perceptron can only learn linear functions, a multi layer perceptron can also learn non – linear functions. | |
C4816 | A relative frequency distribution lists the data values along with the percent of all observations belonging to each group. These relative frequencies are calculated by dividing the frequencies for each group by the total number of observations. | |
C4817 | You often measure a continuous variable on a scale. For example, when you measure height, weight, and temperature, you have continuous data. With continuous variables, you can calculate and assess the mean, median, standard deviation, or variance. | |
C4818 | In robust statistics, robust regression is a form of regression analysis designed to overcome some limitations of traditional parametric and non-parametric methods. Regression analysis seeks to find the relationship between one or more independent variables and a dependent variable. | |
C4819 | ASSUMPTIONS. No formal distributional assumptions, random forests are non-parametric and can thus handle skewed and multi-modal data as well as categorical data that are ordinal or non-ordinal. | |
C4820 | Sample ROC plot: x-axis = (1-specificity), y-axis = sensitivity. The area under the ROC curve represents accuracy of a trial test. ROC curve AUC is determined by multiple cut-points of the trial test, it gives better estimate of accuracy. | |
C4821 | The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) evaluates algorithms for object detection and image classification at large scale. Another motivation is to measure the progress of computer vision for large scale image indexing for retrieval and annotation. | |
C4822 | Unsupervised learning is where you only have input data (X) and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. | |
C4823 | The two-way linear fixed effects regression (2FE) has become a default method for estimating causal effects from panel data. Many applied researchers use the 2FE estimator to adjust for unobserved unit-specific and time-specific confounders at the same time. | |
C4824 | Strongly Connected Components1) Create an empty stack 'S' and do DFS traversal of a graph. In DFS traversal, after calling recursive DFS for adjacent vertices of a vertex, push the vertex to stack. 2) Reverse directions of all arcs to obtain the transpose graph.3) One by one pop a vertex from S while S is not empty. Let the popped vertex be 'v'. | |
C4825 | The Bag-of-Words (BoW) framework is well-known in image classification. In the framework, there are two essential steps: 1) coding, which encodes local features by a visual vocabulary, and 2) pooling, which pools over the response of all features into image representation. | |
C4826 | Perceptron can have only one output , and output at perceptron can be used as inputs to several other perceptrons . Just like perceptron, sigmoid neuron has weights for each input and an overall bias ( say b ) . BUT the output is not 0 or 1 rather sigma * ( weight * inputs + bias ) , sigma = sigmoid function . | |
C4827 | Overfitting is a significant practical difficulty for decision tree models and many other predictive models. Overfitting happens when the learning algorithm continues to develop hypotheses that reduce training set error at the cost of an. increased test set error. | |
C4828 | The planning problem in Artificial Intelligence is about the decision making performed by intelligent creatures like robots, humans, or computer programs when trying to achieve some goal. In the following we discuss a number of ways of formalizing planning, and show how the planning problem can be solved automatically. | |
C4829 | An experimental design where one group of individuals in one treatment condition is compared to another group of individuals in a different treatment condtion is called a between-subjects experimental design. | |
C4830 | Generally, we use softmax activation instead of sigmoid with the cross-entropy loss because softmax activation distributes the probability throughout each output node. But, since it is a binary classification, using sigmoid is same as softmax. For multi-class classification use sofmax with cross-entropy. | |
C4831 | Hyperparameter tuning is searching the hyperparameter space for a set of values that will optimize your model architecture. This is different from tuning your model parameters where you search your feature space that will best minimize a cost function. | |
C4832 | A false positive means that the results say you have the condition you were tested for, but you really don't. With a false negative, the results say you don't have a condition, but you really do. | |
C4833 | 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. | |
C4834 | The Matthews correlation coefficient (MCC) or phi coefficient is used in machine learning as a measure of the quality of binary (two-class) classifications, introduced by biochemist Brian W. Matthews in 1975. | |
C4835 | Here trace of the matrix is the sum of the elements of the main diagonal i.e the diagonal from the upper left to the lower right of a matrix. Normal of the matrix is the square root of the sum of all the elements. To evaluate trace of the matrix, take sum of the main diagonal elements. | |
C4836 | Weight is the parameter within a neural network that transforms input data within the network's hidden layers. As an input enters the node, it gets multiplied by a weight value and the resulting output is either observed, or passed to the next layer in the neural network. | |
C4837 | Sampled signal is applied to adaptive transversal filter equalizer. Transversal filters are actually FIR discrete time filters. The object is to adapt the coefficients to minimize the noise and intersymbol interference (depending on the type of equalizer) at the output. | |
C4838 | K-means clustering is one of the simplest and popular unsupervised machine learning algorithms. A cluster refers to a collection of data points aggregated together because of certain similarities. You'll define a target number k, which refers to the number of centroids you need in the dataset. | |
C4839 | In statistics, Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. This model is popular because it models the Poisson heterogeneity with a gamma distribution. | |
C4840 | Abstract. Autoassociative neural networks are feedforward nets trained to produce an approximation of the identity mapping between network inputs and outputs using backpropagation or similar learning procedures. The key feature of an autoassociative network is a dimensional bottleneck between input and output. | |
C4841 | It is used to predict values of a continuous response variable using one or more explanatory variables and can also identify the strength of the relationships between these variables (these two goals of regression are often referred to as prediction and explanation). | |
C4842 | For the alternative formulation, where X is the number of trials up to and including the first success, the expected value is E(X) = 1/p = 1/0.1 = 10. For example 1 above, with p = 0.6, the mean number of failures before the first success is E(Y) = (1 − p)/p = (1 − 0.6)/0.6 = 0.67. | |
C4843 | How to Analyze a PhotographStep 1: Find an Image to Analyze. Find any high quality commercial image (stock photos, advertisement images, documentary stock, etc.). Step 2: Observe Your Image. Step 3: Analyzing People. Step 4: Analyzing Setting. Step 5: Looking at Generics Vs. Step 6: Looking at Colour. Step 7: Looking at Viewer's Positioning. | |
C4844 | Many problems in AI can be modeled as constraint satisfaction problems (CSPs). Hence the development of effective solution techniques for CSPs is an important research problem. Each constraint is defined over some subset of the original set of variables and restricts the values these variables can simultaneously take. | |
C4845 | There are three common types of basic production systems: the batch system, the continuous system, and the project system. In the batch system, general-purpose equipment and methods are used to produce small quantities of output (goods or services) with specifications that vary greatly from one batch to the next.3 days ago | |
C4846 | If your test statistic is positive, first find the probability that Z is greater than your test statistic (look up your test statistic on the Z-table, find its corresponding probability, and subtract it from one). Then double this result to get the p-value. | |
C4847 | A hierarchical model is a model in which lower levels are sorted under a hierarchy of successively higher-level units. Data is grouped into clusters at one or more levels, and the influence of the clusters on the data points contained in them is taken account in any statistical analysis. | |
C4848 | Inferential statistics helps to suggest explanations for a situation or phenomenon. It allows you to draw conclusions based on extrapolations, and is in that way fundamentally different from descriptive statistics that merely summarize the data that has actually been measured. | |
C4849 | Adam is a replacement optimization algorithm for stochastic gradient descent for training deep learning models. Adam combines the best properties of the AdaGrad and RMSProp algorithms to provide an optimization algorithm that can handle sparse gradients on noisy problems. | |
C4850 | Step 1: Divide your confidence level by 2: .95/2 = 0.475. Step 2: Look up the value you calculated in Step 1 in the z-table and find the corresponding z-value. The z-value that has an area of .475 is 1.96. Step 3: Divide the number of events by the number of trials to get the “P-hat” value: 24/160 = 0.15. | |
C4851 | Unlike the previous measures of variability, the variance includes all values in the calculation by comparing each value to the mean. To calculate this statistic, you calculate a set of squared differences between the data points and the mean, sum them, and then divide by the number of observations. | |
C4852 | Because there are infinite values that X could assume, the probability of X taking on any one specific value is zero. Therefore we often speak in ranges of values (p(X>0) = . 50). The normal distribution is one example of a continuous distribution. | |
C4853 | Reliability refers to the extent that the instrument yields the same results over multiple trials. Validity refers to the extent that the instrument measures what it was designed to measure. Construct validity uses statistical analyses, such as correlations, to verify the relevance of the questions. | |
C4854 | Beta diversity measures the change in diversity of species from one environment to another. In simpler terms, it calculates the number of species that are not the same in two different environments. There are also indices which measure beta diversity on a normalized scale, usually from zero to one. | |
C4855 | This is a form of hypothesis testing and it is used to optimize a particular feature of a business. It is called A/B testing and refers to a way of comparing two versions of something to figure out which performs better. | |
C4856 | ♦ Error rate: proportion of errors made over the. whole set of instances. ● Resubstitution error: error rate obtained. from training data. ● Resubstitution error is usually quite. | |
C4857 | In probability theory, convolution is a mathematical operation that allows to derive the distribution of a sum of two random variables from the distributions of the two summands. In the case of continuous random variables, it is obtained by integrating the product of their probability density functions (pdfs). | |
C4858 | β and γ are themselves learnable parameters that are updated during network training. Batch normalization layers normalize the activations and gradients propagating through a neural network, making network training an easier optimization problem. | |
C4859 | Eigenanalysis is a mathematical operation on a square, symmetric matrix. A square matrix has the same number of rows as columns. A symmetric matrix is the same if you switch rows and columns. Distance and similarity matrices are nearly always square and symmetric. | |
C4860 | Variance is calculated by calculating an expected return and summing a weighted average of the squared deviations from the mean return. | |
C4861 | Simple random sampling is where individuals are chosen completely by chance from a population. The addition of SRS increases the chance a guilty person will be found. | |
C4862 | To visualize the weights, you can use a tf. image_summary() op to transform a convolutional filter (or a slice of a filter) into a summary proto, write them to a log using a tf. train. SummaryWriter , and visualize the log using TensorBoard. | |
C4863 | We have a bias when, rather than being neutral, we have a preference for (or aversion to) a person or group of people. Thus, we use the term “implicit bias” to describe when we have attitudes towards people or associate stereotypes with them without our conscious knowledge. | |
C4864 | At the point of non-differentiability, you can assign the derivative of the function at the point “right next” to the singularity and the algorithm will work fine. For example, in ReLU we can give the derivative of the function at zero as 0. | |
C4865 | A Markov chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. | |
C4866 | Posterior Distribution = Prior Distribution + Likelihood Function (“new evidence”)Interval estimates for parameters,Point estimates for parameters,Prediction inference for future data,Probabilistic evaluations for your hypothesis. | |
C4867 | Multinomial logistic regression is used to predict categorical placement in or the probability of category membership on a dependent variable based on multiple independent variables. The independent variables can be either dichotomous (i.e., binary) or continuous (i.e., interval or ratio in scale). | |
C4868 | Hidden Markov models have been around for a pretty long time (1970s at least). It's a misnomer to call them machine learning algorithms. It is most useful, IMO, for state sequence estimation, which is not a machine learning problem since it is for a dynamical process, not a static classification task. | |
C4869 | Discrete distributions have a countable number of outcomes, which means that the potential outcomes can be put into a list. The list may be finite or infinite; the Poisson distribution is a discrete distribution whose list {0, 1, 2, } is infinite. | |
C4870 | The F ratio is the ratio of two mean square values. If the null hypothesis is true, you expect F to have a value close to 1.0 most of the time. A large F ratio means that the variation among group means is more than you'd expect to see by chance. | |
C4871 | SVMs don't output probabilities natively, but probability calibration methods can be used to convert the output to class probabilities. For many problems, it is convenient to get a probability P(y=1∣x), i.e. a classification that not only gives an answer, but also a degree of certainty about the answer. | |
C4872 | To minimize or avoid performance bias, investigators can consider cluster stratification of patients, in which all patients having an operation by one surgeon or at one hospital are placed into the same study group, as opposed to placing individual patients into groups. | |
C4873 | Before you can figure out if you have a left tailed test or right tailed test, you have to make sure you have a single tail to begin with. A tail in hypothesis testing refers to the tail at either end of a distribution curve. Area under a normal distribution curve. Two tails (both left and right) are shaded. | |
C4874 | Any kappa below 0.60 indicates inadequate agreement among the raters and little confidence should be placed in the study results.Kappa Coefficient Interpretation.Value of kLevel of agreement% of data that are reliable0.40 - 0.59Weak15 - 35%0.60 - 0.79Moderate35 - 63%0.80 - 0.90Strong64 - 81%Above 0.90Almost Perfect82 - 100%2 more rows | |
C4875 | Linear models describe a continuous response variable as a function of one or more predictor variables. They can help you understand and predict the behavior of complex systems or analyze experimental, financial, and biological data. | |
C4876 | Logarithmic Loss, or simply Log Loss, is a classification loss function often used as an evaluation metric in Kaggle competitions. Log Loss quantifies the accuracy of a classifier by penalising false classifications. | |
C4877 | Moments in mathematical statistics involve a basic calculation. These calculations can be used to find a probability distribution's mean, variance, and skewness. Using this formula requires us to be careful with our order of operations. | |
C4878 | In everyday use, AC voltages (and currents) are always given as RMS values because this allows a sensible comparison to be made with steady DC voltages (and currents), such as from a battery. For example, a 6V AC supply means 6V RMS with the peak voltage about 8.6V. | |
C4879 | Imbalanced data sets are a special case for classification problem where the class distribution is not uniform among the classes. Typically, they are composed by two classes: The majority (negative) class and the minority (positive) class. | |
C4880 | Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data. | |
C4881 | Limitations of Hypothesis testing in ResearchThe tests should not be used in a mechanical fashion. Test do not explain the reasons as to why does the difference exist, say between the means of the two samples. Results of significance tests are based on probabilities and as such cannot be expressed with full certainty.More items | |
C4882 | Max Pooling Layer Maximum pooling, or max pooling, is a pooling operation that calculates the maximum, or largest, value in each patch of each feature map. | |
C4883 | The standard normal or z-distribution assumes that you know the population standard deviation. The t-distribution is based on the sample standard deviation. | |
C4884 | Def: A uniform random permutation is one in which each of the n! possible permutations are equally likely. Def Given a set of n elements, a k-permutation is a sequence containing k of the n elements. | |
C4885 | A data structure is a collection of data type 'values' which are stored and organized in such a way that it allows for efficient access and modification. When we think of data structures, there are generally four forms: Linear: arrays, lists. Tree: binary, heaps, space partitioning etc. | |
C4886 | Despite having similar aims and processes, there are two main differences between them: Machine learning works out predictions and recalibrates models in real-time automatically after design. Meanwhile, predictive analytics works strictly on “cause” data and must be refreshed with “change” data. | |
C4887 | Classification is one of the important areas of research in the field of data mining and neural network is one of the widely used techniques for classification. ANN has many advantages but it has some hindrances like long training time, high computational cost, and adjustment of weight. | |
C4888 | The purpose of statistical inference is to estimate this sample to sample variation or uncertainty. | |
C4889 | Biased but consistent , it approaches the correct value, and so it is consistent. ), these are both negatively biased but consistent estimators. | |
C4890 | To construct a histogram, the first step is to "bin" (or "bucket") the range of values—that is, divide the entire range of values into a series of intervals—and then count how many values fall into each interval. The bins are usually specified as consecutive, non-overlapping intervals of a variable. | |
C4891 | Non-linearity in neural networks simply mean that the output at any unit cannot be reproduced from a linear function of the input. | |
C4892 | A vector is an element of a vector space. Assuming you're talking about an abstract vector space, which has an addition and scalar multiplication satisfying a number of properties, then a vector space is what we call a set which satisfies those properties. | |
C4893 | Random event/process/variable: an event/process that is not and cannot be made exact and, consequently, whose outcome cannot be predicted, e.g., the sum of the numbers on two rolled dice. 5. Probability: an estimate of the likelihood that a random event will produce a certain outcome. | |
C4894 | The significance level, also denoted as alpha or α, is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference. | |
C4895 | Use Regression to Analyze a Wide Variety of Relationships Include continuous and categorical variables. Use polynomial terms to model curvature. Assess interaction terms to determine whether the effect of one independent variable depends on the value of another variable. | |
C4896 | Data binning is the process of grouping individual data values into specific bins or groups according to defined criteria. For example, census data can be binned into defined age groups. | |
C4897 | Gradient images are created from the original image (generally by convolving with a filter, one of the simplest being the Sobel filter) for this purpose. Each pixel of a gradient image measures the change in intensity of that same point in the original image, in a given direction. | |
C4898 | Stochastic Gradient Descent (SGD) addresses both of these issues by following the negative gradient of the objective after seeing only a single or a few training examples. The use of SGD In the neural network setting is motivated by the high cost of running back propagation over the full training set. | |
C4899 | 2:537:37Suggested clip · 108 secondsPrepare your dataset for machine learning (Coding TensorFlow YouTubeStart of suggested clipEnd of suggested clip |
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