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C8900 | (Note that how a support vector machine classifies points that fall on a boundary line is implementation dependent. In our discussions, we have said that points falling on the line will be considered negative examples, so the classification equation is w . u + b ≤ 0.) | |
C8901 | 2 AnswersUse weight regularization. It tries to keep weights low which very often leads to better generalization. Corrupt your input (e.g., randomly substitute some pixels with black or white). Expand your training set. Pre-train your layers with denoising critera. Experiment with network architecture. | |
C8902 | Control Charts: A discrete distribution is one in which the data can only take on certain values, for example integers. A continuous distribution is one in which data can take on any value within a specified range (which may be infinite). | |
C8903 | Whereas the normal distribution is the sum/difference of lots of things, the lognormal (because it is the log transform) is the product/quotient of lots of things. So if you are multiplying a bunch of variables together, the resultant distribution approaches lognormal as the number of variables gets large. | |
C8904 | Coefficient of correlation is “R” value which is given in the summary table in the Regression output. In other words Coefficient of Determination is the square of Coefficeint of Correlation. R square or coeff. of determination shows percentage variation in y which is explained by all the x variables together. | |
C8905 | This binary classifier for multiclass can be used with one-vs-all or all-vs-all reduction method. Here you can go with logistic regression, decision tree algorithms. You can go with algorithms like Naive Bayes, Neural Networks and SVM to solve multi class problem. | |
C8906 | The population mean of the distribution of sample means is the same as the population mean of the distribution being sampled from. Thus as the sample size increases, the standard deviation of the means decreases; and as the sample size decreases, the standard deviation of the sample means increases. | |
C8907 | To reiterate parameter sharing occurs when a feature map is generated from the result of the convolution between a filter and input data from a unit within a plane in the conv layer. All units within this layer plane share the same weights; hence it is called weight/parameter sharing. | |
C8908 | A posterior probability, in Bayesian statistics, is the revised or updated probability of an event occurring after taking into consideration new information. In statistical terms, the posterior probability is the probability of event A occurring given that event B has occurred. | |
C8909 | Triplet loss is a loss function for machine learning algorithms where a baseline (anchor) input is compared to a positive (truthy) input and a negative (falsy) input. This can be avoided by posing the problem as a similarity learning problem instead of a classification problem. | |
C8910 | Convolutional neural networks (CNNs, or ConvNets) are essential tools for deep learning, and are especially suited for analyzing image data. For example, you can use CNNs to classify images. To predict continuous data, such as angles and distances, you can include a regression layer at the end of the network. | |
C8911 | A probability histogram is a graph that shows the probability of each outcome on the y -axis. | |
C8912 | Categorical variables require special attention in regression analysis because, unlike dichotomous or continuous variables, they cannot by entered into the regression equation just as they are. Instead, they need to be recoded into a series of variables which can then be entered into the regression model. | |
C8913 | Natural numbers are a part of the number system which includes all the positive integers from 1 till infinity and are also used for counting purpose. It does not include zero (0). In fact, 1,2,3,4,5,6,7,8,9…., are also called counting numbers. | |
C8914 | 0:002:42Suggested clip · 110 secondsNormal distribution moment generating function - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C8915 | Difference Between Cross Correlation and Autocorrelation Cross correlation happens when two different sequences are correlated. Autocorrelation is the correlation between two of the same sequences. In other words, you correlate a signal with itself. | |
C8916 | Simply put, an activation function is a function that is added into an artificial neural network in order to help the network learn complex patterns in the data. When comparing with a neuron-based model that is in our brains, the activation function is at the end deciding what is to be fired to the next neuron. | |
C8917 | To estimate the oriented bounding box, you need to train the network with objects and their oriented bounding boxes. For that, you need to modify the bounding box regression head of the network. Frustum PointNet[2] employs such regression but for the 3D bounding boxes. It can easily be extended for the 2D use cases. | |
C8918 | Convolutional layers in a convolutional neural network systematically apply learned filters to input images in order to create feature maps that summarize the presence of those features in the input. A pooling layer is a new layer added after the convolutional layer. | |
C8919 | The values of the kernel filters are learned automatically by the neural network through the training process, and the filters kernels which results in the features that are most efficient for the particular classification or the detection are automatically learned. | |
C8920 | "Correlation is not causation" means that just because two things correlate does not necessarily mean that one causes the other. Correlations between two things can be caused by a third factor that affects both of them. | |
C8921 | Mathematical expectation, also known as the expected value, is the summation or integration of a possible values from a random variable. It is also known as the product of the probability of an event occurring, denoted P(x), and the value corresponding with the actual observed occurrence of the event. | |
C8922 | YOUR preferred learning style is the way in which YOU learn best. Three learning styles that are often identified in students are the Auditory Learning Style, the Visual Learning Style, and theTactile/Kinesthetic Learning Style. Read about each of these learning styles to identify YOUR preferred learning style. | |
C8923 | Discriminant analysis is statistical technique used to classify observations into non-overlapping groups, based on scores on one or more quantitative predictor variables. For example, a doctor could perform a discriminant analysis to identify patients at high or low risk for stroke. | |
C8924 | In machine learning, scoring is the process of applying an algorithmic model built from a historical dataset to a new dataset in order to uncover practical insights that will help solve a business problem. The second stage is scoring, in which you apply the trained model to a new dataset. | |
C8925 | Qualities of a Good Sampling Frame Include all individuals in the target population. Exclude all individuals not in the target population. Includes accurate information that can be used to contact selected individuals. | |
C8926 | Iterable is an object, which one can iterate over. It generates an Iterator when passed to iter() method. Iterator is an object, which is used to iterate over an iterable object using __next__() method. Note that every iterator is also an iterable, but not every iterable is an iterator. | |
C8927 | Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. | |
C8928 | In two-dimensional signals like digital images, frequencies are rate of change of grey scale value (intensity of pixel) with respect to space. This is also called Spatial frequency . Convert the cosine values represented by the red dots into greyscale (0-255), such that -1 maps to 0 and 1 maps to 255. | |
C8929 | H (hypothesis set): A space of possible hypotheses for mapping inputs to outputs that can be searched, often constrained by the choice of the framing of the problem, the choice of model and the choice of model configuration. | |
C8930 | The neuron is the basic working unit of the brain, a specialized cell designed to transmit information to other nerve cells, muscle, or gland cells. Neurons are cells within the nervous system that transmit information to other nerve cells, muscle, or gland cells. Most neurons have a cell body, an axon, and dendrites. | |
C8931 | Proof: The integers Z are countable because the function f : Z → N given by f(n) = 2n if n is non-negative and f(n) = 3− n if n is negative, is an injective function. | |
C8932 | In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a probability distribution by maximizing a likelihood function, so that under the assumed statistical model the observed data is most probable. | |
C8933 | The standard deviation (SD) measures the amount of variability, or dispersion, from the individual data values to the mean, while the standard error of the mean (SEM) measures how far the sample mean of the data is likely to be from the true population mean. SD is the dispersion of individual data values. | |
C8934 | If you want to control for the effects of some variables on some dependent variable, you just include them into the model. Say, you make a regression with a dependent variable y and independent variable x. You think that z has also influence on y too and you want to control for this influence. | |
C8935 | The consequences of making a type I error mean that changes or interventions are made which are unnecessary, and thus waste time, resources, etc. Type II errors typically lead to the preservation of the status quo (i.e. interventions remain the same) when change is needed. | |
C8936 | 1:2713:04Suggested clip · 104 secondsEstimated Mean Average and Cumulative Frequency Graphs 10A2 YouTubeStart of suggested clipEnd of suggested clip | |
C8937 | To calculate the standard deviation of those numbers:Work out the Mean (the simple average of the numbers)Then for each number: subtract the Mean and square the result.Then work out the mean of those squared differences.Take the square root of that and we are done! | |
C8938 | Typically, a regression analysis is done for one of two purposes: In order to predict the value of the dependent variable for individuals for whom some information concerning the explanatory variables is available, or in order to estimate the effect of some explanatory variable on the dependent variable. | |
C8939 | It is possible to find the correlation between a categorical variable and a continuous variable using the analysis of covariance technique. | |
C8940 | According to this link LDA is a generative classifier. Also, the motto of LDA is to model a discriminant function to classify. | |
C8941 | Connected components, in a 2D image, are clusters of pixels with the same value, which are connected to each other through either 4-pixel, or 8-pixel connectivity. We offer several user-friendly ways to segment, and then rapidly calculate and display the connected components of 2D and 3D segmentations. | |
C8942 | Examples of multivariate regression Example 1. A researcher has collected data on three psychological variables, four academic variables (standardized test scores), and the type of educational program the student is in for 600 high school students. A doctor has collected data on cholesterol, blood pressure, and weight. | |
C8943 | 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 algorithm works iteratively to assign each data point to one of K groups based on the features that are provided. | |
C8944 | 100 | |
C8945 | The method involves asking individuals to state their preference over hypothetical alternative scenarios, goods or services. Each alternative is described by several attributes and the responses are used to determine whether preferences are significantly influenced by the attributes and also their relative importance. | |
C8946 | Steps in Data Exploration and Preprocessing:Identification of variables and data types.Analyzing the basic metrics.Non-Graphical Univariate Analysis.Graphical Univariate Analysis.Bivariate Analysis.Variable transformations.Missing value treatment.Outlier treatment.More items• | |
C8947 | General steps to calculate the mean squared error from a set of X and Y values:Find the regression line.Insert your X values into the linear regression equation to find the new Y values (Y').Subtract the new Y value from the original to get the error.Square the errors.Add up the errors.Find the mean. | |
C8948 | 5) In general, practice, choosing the value of k is k = sqrt(N) where N stands for the number of samples in your training dataset . | |
C8949 | But the size of the input image of a Convolutional network should not be less than the input, so padding is done. To calculate padding, input_size + 2 * padding_size-(filter_size-1). For above case, (50+(2*1)-(3–1) = 52–2 = 50) which gives as a same input size. | |
C8950 | EPA's Positive Matrix Factorization (PMF) Model is a mathematical receptor model developed by EPA scientists that provides scientific support for the development and review of air and water quality standards, exposure research and environmental forensics. | |
C8951 | LDA tends to be a better than QDA when you have a small training set. In contrast, QDA is recommended if the training set is very large, so that the variance of the classifier is not a major issue, or if the assumption of a common covariance matrix for the K classes is clearly untenable. | |
C8952 | Random numbers are sets of digits (i.e., 0, 1, 2, 3, 4, 5, 6, 7, 8, 9) arranged in random order. Because they are randomly ordered, no individual digit can be predicted from knowledge of any other digit or group of digits. | |
C8953 | Concept shift is closely related to concept drift. This occurs when a model learned from data sampled from one distribution needs to be applied to data drawn from another. | |
C8954 | Linear regression is a way to model the relationship between two variables. The equation has the form Y= a + bX, where Y is the dependent variable (that's the variable that goes on the Y axis), X is the independent variable (i.e. it is plotted on the X axis), b is the slope of the line and a is the y-intercept. | |
C8955 | Make a new calculated column based on the mathematical form (shape) of your data. Plot a new graph using your new calculated column of data on one of your axes. If the new graph (using the calculated column) is straight, you have succeeded in linearizing your data. Draw a best fit line USING A RULER! | |
C8956 | Generative adversarial networks (GANs) have been widely used and have achieved competitive results in semi-supervised learning. We first prove that, given a fixed generator, optimizing the discriminator of GAN-SSL is equivalent to optimizing that of supervised learning. | |
C8957 | Conditional probabilities can be reversed using Bayes' theorem. Conditional probabilities can be displayed in a conditional probability table. | |
C8958 | More generally, survival analysis involves the modelling of time to event data; in this context, death or failure is considered an "event" in the survival analysis literature – traditionally only a single event occurs for each subject, after which the organism or mechanism is dead or broken. | |
C8959 | Cohen's d is an effect size used to indicate the standardised difference between two means. It can be used, for example, to accompany reporting of t-test and ANOVA results. It is also widely used in meta-analysis. Cohen's d is an appropriate effect size for the comparison between two means. | |
C8960 | Motivation involves the biological, emotional, social, and cognitive forces that activate behavior. In everyday usage, the term "motivation" is frequently used to describe why a person does something. | |
C8961 | 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• | |
C8962 | The Spearman's rank-order correlation is the nonparametric version of the Pearson product-moment correlation. Spearman's correlation coefficient, (ρ, also signified by rs) measures the strength and direction of association between two ranked variables. | |
C8963 | False-negative results on the QFT-GIT test for patients with latent and active TB disease have been reported with a frequency of 4–38% [22]. | |
C8964 | Machine learning can be automated when it involves the same activity again and again. However, the fundamental nature of machine learning deals with the opposite: variable conditions. In this regard, machine learning needs to be able to function independently and with different solutions to match different demands. | |
C8965 | When a document needs modelling by LDA, the following steps are carried out initially:The number of words in the document are determined.A topic mixture for the document over a fixed set of topics is chosen.A topic is selected based on the document's multinomial distribution.More items• | |
C8966 | Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. The key idea is to set the target outcomes for this next model in order to minimize the error. | |
C8967 | Linear filtering is the filtering method in which the value of output pixel is linear combinations of the neighbouring input pixels. it can be done with convolution. For examples, mean/average filters or Gaussian filtering. A non-linear filtering is one that cannot be done with convolution or Fourier multiplication. | |
C8968 | Quite simply, an insignificant coefficient means that the independent variable has no effect on the dependent variable, that is, its effect is statistically equal to zero (according to the results). The effect of independent variable is too little to actually affect the dependent variable most of the time. | |
C8969 | The Laplacian is a 2-D isotropic measure of the 2nd spatial derivative of an image. The Laplacian is often applied to an image that has first been smoothed with something approximating a Gaussian smoothing filter in order to reduce its sensitivity to noise, and hence the two variants will be described together here. | |
C8970 | Posterior probability is a conditional probability, but more specifically implies the probability of a particular parameter value(s) from a given parameter space when a given set of observations (say Xi) have been observed. | |
C8971 | Laws that concern data are highly relevant for AI, since those laws can impact the use and growth of AI systems. However, no countries yet have specific laws in place around ethical and responsible AI. Time will tell whether or not companies will self-monitor or if governments will step in to more formally regulate. | |
C8972 | Simply put, an activation function is a function that is added into an artificial neural network in order to help the network learn complex patterns in the data. When comparing with a neuron-based model that is in our brains, the activation function is at the end deciding what is to be fired to the next neuron. | |
C8973 | PCA finds a lower dimensional representation of the data that minimizes the squared reconstruction error. If you have irrelevant features (often the case in text classification), PCA counts errors in those with equal importance as errors in words that are important for your classification. | |
C8974 | ARMA stands for “Autoregressive Moving Average” and ARIMA stands for “Autoregressive Integrated Moving Average.” The only difference, then, is the “integrated” part. Integrated refers to the number of times needed to difference a series in order to achieve stationarity, which is required for ARMA models to be valid. | |
C8975 | Use simple logistic regression when you have one nominal variable and one measurement variable, and you want to know whether variation in the measurement variable causes variation in the nominal variable. | |
C8976 | Overfitting occurs when a model tries to predict a trend in data that is too noisy. This is the caused due to an overly complex model with too many parameters. A model that is overfitted is inaccurate because the trend does not reflect the reality present in the data. | |
C8977 | The determinant of a matrix is a special value that is calculated from a square matrix. It can help you determine whether a matrix has an inverse, find the area of a triangle, and let you know if the system of equations has a unique solution. Determinants are also used in calculus and linear algebra. | |
C8978 | A decision tree is built on an entire dataset, using all the features/variables of interest, whereas a random forest randomly selects observations/rows and specific features/variables to build multiple decision trees from and then averages the results. | |
C8979 | It means that there is no absolute good or bad threshold, however you can define it based on your DV. For a datum which ranges from 0 to 1000, an RMSE of 0.7 is small, but if the range goes from 0 to 1, it is not that small anymore. | |
C8980 | Normal distributions are symmetric, unimodal, and asymptotic, and the mean, median, and mode are all equal. A normal distribution is perfectly symmetrical around its center. That is, the right side of the center is a mirror image of the left side. There is also only one mode, or peak, in a normal distribution. | |
C8981 | The normal distribution is a probability function that describes how the values of a variable are distributed. It is a symmetric distribution where most of the observations cluster around the central peak and the probabilities for values further away from the mean taper off equally in both directions. | |
C8982 | Definition: In simple words, data mining is defined as a process used to extract usable data from a larger set of any raw data. It implies analysing data patterns in large batches of data using one or more software. Data mining is also known as Knowledge Discovery in Data (KDD). | |
C8983 | There are three types of layers in a convolutional neural network: convolutional layer, pooling layer, and fully connected layer. Each of these layers has different parameters that can be optimized and performs a different task on the input data. Features of a convolutional layer. | |
C8984 | For a 2x2 table, the null hypothesis may equivalently be written in terms of the probabilities themselves, or the risk difference, the relative risk, or the odds ratio. In each case, the null hypothesis states that there is no difference between the two groups. | |
C8985 | A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning. | |
C8986 | The beginnings of modern AI can be traced to classical philosophers' attempts to describe human thinking as a symbolic system. But the field of AI wasn't formally founded until 1956, at a conference at Dartmouth College, in Hanover, New Hampshire, where the term "artificial intelligence" was coined. | |
C8987 | Association between two variables means the values of one variable relate in some way to the values of the other. Association is usually measured by correlation for two continuous variables and by cross tabulation and a Chi-square test for two categorical variables. | |
C8988 | Classical statistics uses techniques such as Ordinary Least Squares and Maximum Likelihood – this is the conventional type of statistics that you see in most textbooks covering estimation, regression, hypothesis testing, confidence intervals, etc. In fact Bayesian statistics is all about probability calculations! | |
C8989 | Optimal control focuses on a subset of problems, but solves these problems very well, and has a rich history. RL can be thought of as a way of generalizing or extending ideas from optimal control to non-traditional control problems. | |
C8990 | 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. | |
C8991 | A time series is a dataset whose unit of analysis is a time period, rather than a person. Regression is an analytic tool that attempts to predict one variable, y as a function of one or more x variables. It can be used to analyze both time-series and static data. | |
C8992 | A new study suggests that the placebo effect may work in reverse. A new study suggests that the placebo effect may work in reverse. In the past, placebos have been given to participants in studies to detect whether the participant would still feel the effects of the “drug” they thought they were being given. | |
C8993 | Mathematics Behind PCATake the whole dataset consisting of d+1 dimensions and ignore the labels such that our new dataset becomes d dimensional.Compute the mean for every dimension of the whole dataset.Compute the covariance matrix of the whole dataset.Compute eigenvectors and the corresponding eigenvalues.More items | |
C8994 | 1. A Simple Way of Solving an Object Detection Task (using Deep Learning)First, we take an image as input:Then we divide the image into various regions:We will then consider each region as a separate image.Pass all these regions (images) to the CNN and classify them into various classes.More items• | |
C8995 | To prevent selection bias, investigators should anticipate and analyze all the confounders important for the outcome studied. They should use an adequate method of randomization and allocation concealment and they should report these methods in their trial. | |
C8996 | The term 'univariate' implies that forecasting is based on a sample of time series observations of the exchange rate without taking into account the effect of the other variables such as prices and interest rates. | |
C8997 | The normal distribution is a probability distribution. It is also called Gaussian distribution because it was first discovered by Carl Friedrich Gauss. It is often called the bell curve, because the graph of its probability density looks like a bell. Many values follow a normal distribution. | |
C8998 | 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. | |
C8999 | A ranked variable is an ordinal variable; a variable where every data point can be put in order (1st, 2nd, 3rd, etc.). You may not know an exact value of any of your points, but you know which comes after the other. |
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