_id stringlengths 2 6 | text stringlengths 3 395 | title stringclasses 1 value |
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C9400 | stratified k-fold cross-validation | |
C9401 | Let X = fft(x) . Both x and X have length N . Suppose X has two peaks at n0 and N-n0 . Then the sinusoid frequency is f0 = fs*n0/N Hertz.Replace all coefficients of the FFT with their square value (real^2+imag^2). Take the iFFT.Find the largest peak in the iFFT. | |
C9402 | “The advantages of bootstrapping are that it is a straightforward way to derive the estimates of standard errors and confidence intervals, and it is convenient since it avoids the cost of repeating the experiment to get other groups of sampled data. | |
C9403 | Intersection over Union is an evaluation metric used to measure the accuracy of an object detector on a particular dataset. The ground-truth bounding boxes (i.e., the hand labeled bounding boxes from the testing set that specify where in the image our object is). The predicted bounding boxes from our model. | |
C9404 | AI is designed to draw conclusions on data, understand concepts, become self-learning and even interact with humans. Data analytics refers to technologies that study data and draw patterns. Furthermore, when it comes to data analytics, it is not a single product. | |
C9405 | Seven Techniques for Data Dimensionality ReductionDimensionality ReductionReduction RateAuCMissing Values Ratio71%82%Low Variance Filter73%82%High Correlation Filter74%82%PCA62%72%4 more rows | |
C9406 | Yes, although 'linear regression' refers to any approach to model the relationship between one or more variables, OLS is the method used to find the simple linear regression of a set of data. | |
C9407 | In computer programming, an iterator is an object that enables a programmer to traverse a container, particularly lists. Various types of iterators are often provided via a container's interface. An iterator is behaviorally similar to a database cursor. | |
C9408 | In fitting a neural network, backpropagation computes the gradient of the loss function with respect to the weights of the network for a single input–output example, and does so efficiently, unlike a naive direct computation of the gradient with respect to each weight individually. | |
C9409 | mAP (mean average precision) is the average of AP. In some context, we compute the AP for each class and average them. But in some context, they mean the same thing. For example, under the COCO context, there is no difference between AP and mAP. | |
C9410 | Average pooling method smooths out the image and hence the sharp features may not be identified when this pooling method is used. Max pooling selects the brighter pixels from the image. It is useful when the background of the image is dark and we are interested in only the lighter pixels of the image. | |
C9411 | Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users. It is one of the steps in data analysis or data science. | |
C9412 | - Categorical Variable Transformation: is turning a categorical variable to a numeric variable. Categorical variable transformation is mandatory for most of the machine learning models because they can handle only numeric values. | |
C9413 | The (statistical) design of experiments (DOE) is an efficient procedure for planning experiments so that the data obtained can be analyzed to yield valid and objective conclusions. DOE begins with determining the objectives of an experiment and selecting the process factors for the study. | |
C9414 | If your learning rate is set too low, training will progress very slowly as you are making very tiny updates to the weights in your network. However, if your learning rate is set too high, it can cause undesirable divergent behavior in your loss function. | |
C9415 | If adjacent residuals are correlated, one residual can predict the next residual. In statistics, this is known as autocorrelation. This correlation represents explanatory information that the independent variables do not describe. Models that use time-series data are susceptible to this problem. | |
C9416 | Spatiotemporal, or spatial temporal, is used in data analysis when data is collected across both space and time. It describes a phenomenon in a certain location and time — for example, shipping movements across a geographic area over time (see above example image). | |
C9417 | In simple terms, a quantile is where a sample is divided into equal-sized, adjacent, subgroups (that's why it's sometimes called a “fractile“). The median cuts a distribution into two equal areas and so it is sometimes called 2-quantile. Quartiles are also quantiles; they divide the distribution into four equal parts. | |
C9418 | Essentially, the process goes as follows:Select k centroids. These will be the center point for each segment.Assign data points to nearest centroid.Reassign centroid value to be the calculated mean value for each cluster.Reassign data points to nearest centroid.Repeat until data points stay in the same cluster. | |
C9419 | A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. The t-test is one of many tests used for the purpose of hypothesis testing in statistics. Calculating a t-test requires three key data values. | |
C9420 | Simple linear regression is commonly used in forecasting and financial analysis—for a company to tell how a change in the GDP could affect sales, for example. | |
C9421 | Not only are nose strips bad for those with sensitive skin, they also worsen other skin conditions. Pore strips exacerbate rosacea-prone skin , especially if they contain irritating ingredients like alcohol and astringents. They also aggravate extremely dry skin, eczema and psoriasis . | |
C9422 | A Boltzmann Machine is a network of symmetrically connected, neuron- like units that make stochastic decisions about whether to be on or off. Boltz- mann machines have a simple learning algorithm that allows them to discover interesting features in datasets composed of binary vectors. | |
C9423 | It is not appropriate because the regression line models the trend of the given data, and it is not known if the trend continues beyond the range of those data. | |
C9424 | Named Entity Recognition can automatically scan entire articles and reveal which are the major people, organizations, and places discussed in them. Knowing the relevant tags for each article help in automatically categorizing the articles in defined hierarchies and enable smooth content discovery. | |
C9425 | Active learning: Reinforces important material, concepts, and skills. Provides more frequent and immediate feedback to students. Provides students with an opportunity to think about, talk about, and process course material. | |
C9426 | So, the total number of parameters are “(n*m*l+1)*k”. Pooling Layer: There are no parameters you could learn in pooling layer. This layer is just used to reduce the image dimension size. Fully-connected Layer: In this layer, all inputs units have a separable weight to each output unit. | |
C9427 | Deep learning is an artificial intelligence (AI) function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Also known as deep neural learning or deep neural network. | |
C9428 | Cluster analysis divides data into groups (clusters) that are meaningful, useful, or both. If meaningful groups are the goal, then the clusters should capture the natural structure of the data. In some cases, however, cluster analysis is only a useful starting point for other purposes, such as data summarization. | |
C9429 | Systematic random sampling is the random sampling method that requires selecting samples based on a system of intervals in a numbered population. For example, Lucas can give a survey to every fourth customer that comes in to the movie theater. | |
C9430 | K-NN is a lazy learner because it doesn't learn a discriminative function from the training data but “memorizes” the training dataset instead. For example, the logistic regression algorithm learns its model weights (parameters) during training time. A lazy learner does not have a training phase. | |
C9431 | To put put it bluntly, Artificial intelligence (AI) relies on machines, whereas Collective Intelligence (CI) relies on people. AI stands for the simulation of human intelligence by machines, computers or software systems. In fact, artificial and collective intelligence can -and should – reinforce each other. | |
C9432 | To see the accuracy of clustering process by using K-Means clustering method then calculated the square error value (SE) of each data in cluster 2. The value of square error is calculated by squaring the difference of the quality score or GPA of each student with the value of centroid cluster 2. | |
C9433 | They are defined as follows: Bias: Bias describes how well a model matches the training set. A model with high bias won't match the data set closely, while a model with low bias will match the data set very closely. Typically models with high bias have low variance, and models with high variance have low bias. | |
C9434 | To give you two ideas:A Kolmogorov-Smirnov test is a non-parametric test, that measures the "distance" between two cumulative/empirical distribution functions.The Kullback-Leibler divergence measures the "distance" between two distributions in the language of information theory as a change in entropy. | |
C9435 | Consistency refers to logical and numerical coherence. Context: An estimator is called consistent if it converges in probability to its estimand as sample increases (The International Statistical Institute, "The Oxford Dictionary of Statistical Terms", edited by Yadolah Dodge, Oxford University Press, 2003). | |
C9436 | Neural networks generally perform supervised learning tasks, building knowledge from data sets where the right answer is provided in advance. The networks then learn by tuning themselves to find the right answer on their own, increasing the accuracy of their predictions. | |
C9437 | - Chad Orzel - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C9438 | Input means to provide the program with some data to be used in the program and Output means to display data on screen or write the data to a printer or a file. C programming language provides many built-in functions to read any given input and to display data on screen when there is a need to output the result. | |
C9439 | Logistic regression is a supervised learning classification algorithm used to predict the probability of a target variable. The nature of target or dependent variable is dichotomous, which means there would be only two possible classes. Mathematically, a logistic regression model predicts P(Y=1) as a function of X. | |
C9440 | A support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text. So you're working on a text classification problem. | |
C9441 | In probability theory, an experiment or trial (see below) is any procedure that can be infinitely repeated and has a well-defined set of possible outcomes, known as the sample space. An experiment is said to be random if it has more than one possible outcome, and deterministic if it has only one. | |
C9442 | Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self driving cars. | |
C9443 | Principal component analysis aims at reducing a large set of variables to a small set that still contains most of the information in the large set. The technique of principal component analysis enables us to create and use a reduced set of variables, which are called principal factors. | |
C9444 | A pooling or subsampling layer often immediately follows a convolution layer in CNN. Its role is to downsample the output of a convolution layer along both the spatial dimensions of height and width. | |
C9445 | Statistical machine learning merges statistics with the computational sciences---computer science, systems science and optimization. Moreover, by its interdisciplinary nature, statistical machine learning helps to forge new links among these fields. | |
C9446 | LSTM networks are well-suited to classifying, processing and making predictions based on time series data, since there can be lags of unknown duration between important events in a time series. LSTMs were developed to deal with the vanishing gradient problem that can be encountered when training traditional RNNs. | |
C9447 | The classic example of experimenter bias is that of "Clever Hans", an Orlov Trotter horse claimed by his owner von Osten to be able to do arithmetic and other tasks. | |
C9448 | When you want to learn about the probability of two events occurring together, you're multiplying because it means “expanding the possibilities.” Because: Now, the possibilities are four, not two. It means it's harder to hit two heads twice, which is intuitively true. | |
C9449 | Training loss is the error on the training set of data. Validation loss is the error after running the validation set of data through the trained network. Train/valid is the ratio between the two. Unexpectedly, as the epochs increase both validation and training error drop. | |
C9450 | It is well known that correlation does not prove causation. What is less well known is that causation can exist when correlation is zero. The upshot of these two facts is that, in general and without additional information, correlation reveals literally nothing about causation. | |
C9451 | A data set is bimodal if it has two modes. This means that there is not a single data value that occurs with the highest frequency. Instead, there are two data values that tie for having the highest frequency. | |
C9452 | Define the population. Choose the relevant stratification. List the population. List the population according to the chosen stratification. Choose your sample size. Calculate a proportionate stratification. Use a simple random or systematic sample to select your sample. | |
C9453 | The linear relationship between exposure (either continuous or categorical) and a continuous outcome can be assessed by using linear regression analysis. | |
C9454 | The regular regression coefficients that you see in your statistical output describe the relationship between the independent variables and the dependent variable. After all, a larger coefficient signifies a greater change in the mean of the independent variable. | |
C9455 | With the LassoCV, RidgeCV, and Linear Regression machine learning algorithms.Define the problem.Gather the data.Clean & Explore the data.Model the data.Evaluate the model.Answer the problem. | |
C9456 | 0:0012:40Suggested clip · 82 secondsCommon Source Amplifiers - Gain Equation - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C9457 | Formally, the Quartile Deviation is equal to the half of the Inter-Quartile Range and thus we can write it as – Q d = Q 3 – Q 1 2 Q_d = \frac{Q_3 – Q_1}{2} Qd=2Q3–Q1 Therefore, we also call it the Semi Inter-Quartile Range. The Quartile Deviation doesn't take into account the extreme points of the distribution. | |
C9458 | Statistical researchers often use a linear relationship to predict the (average) numerical value of Y for a given value of X using a straight line (called the regression line). If you know the slope and the y-intercept of that regression line, then you can plug in a value for X and predict the average value for Y. | |
C9459 | Compare r to the appropriate critical value in the table. If r is not between the positive and negative critical values, then the correlation coefficient is significant. If r is significant, then you may want to use the line for prediction. Suppose you computed r=0.801 using n=10 data points. | |
C9460 | Each 'particle' is in fact a guess about the initial location of the robot. But as the filter gathers more detail, it can eliminate some guesses. The robot will then "refine" its initial guess, by generating additional guesses: it will also guess that its initial location may have been (2.1,3.2), or (1.9,3). | |
C9461 | Listen to pronunciation. (NOR-mul raynj) In medicine, a set of values that a doctor uses to interpret a patient's test results. The normal range for a given test is based on the results that are seen in 95% of the healthy population. | |
C9462 | Optimization Toolbox™ provides functions for finding parameters that minimize or maximize objectives while satisfying constraints. The toolbox lets you perform design optimization tasks, including parameter estimation, component selection, and parameter tuning. | |
C9463 | The ratio scale of measurement is the most informative scale. However, zero on the Kelvin scale is absolute zero. This makes the Kelvin scale a ratio scale. For example, if one temperature is twice as high as another as measured on the Kelvin scale, then it has twice the kinetic energy of the other temperature. | |
C9464 | Numerical data is a data type expressed in numbers, rather than natural language description. Sometimes called quantitative data,numerical data is always collected in number form. This characteristic is one of the major ways of identifying numerical data. | |
C9465 | Different types of the convolution layersSimple Convolution.1x1 Convolutions.Flattened Convolutions.Spatial and Cross-Channel convolutions.Depthwise Separable Convolutions.Grouped Convolutions.Shuffled Grouped Convolutions. | |
C9466 | The scale-invariant feature transform (SIFT) is a feature detection algorithm in computer vision to detect and describe local features in images. Each cluster of 3 or more features that agree on an object and its pose is then subject to further detailed model verification and subsequently outliers are discarded. | |
C9467 | Credit card tokenization substitutes sensitive customer data with a one-time alphanumeric ID that has no value or connection to the account's owner. This randomly generated token is used to access, pass, transmit and retrieve customer's credit card information safely. | |
C9468 | If correlation =1, then it shows there exists a directly proportional relationship between the two variables and if the same is - 1 then it denotes that there exists a inversely proportional relation between the two two variables and if we fit a regression line for the same then we'll get a straight line having | |
C9469 | The One Sample t Test compares a sample mean to a hypothesized population mean to determine whether the two means are significantly different. | |
C9470 | When to use the sample or population standard deviation Therefore, if all you have is a sample, but you wish to make a statement about the population standard deviation from which the sample is drawn, you need to use the sample standard deviation. | |
C9471 | Data Structure - Depth First TraversalRule 1 − Visit the adjacent unvisited vertex. Mark it as visited. Display it. Push it in a stack.Rule 2 − If no adjacent vertex is found, pop up a vertex from the stack. (It will pop up all the vertices from the stack, which do not have adjacent vertices.)Rule 3 − Repeat Rule 1 and Rule 2 until the stack is empty. | |
C9472 | 1 Answer. For binary classification, it should give the same results, because softmax is a generalization of sigmoid for a larger number of classes. | |
C9473 | DEEP LEARNING" document. It is a short State of the Art on two kinds of interesting neural network algorithms: Recurrent Neural Networks and Long Short-Term Memory. It also describes a set of open source tools for this deep learning approach. | |
C9474 | 0:278:54Suggested clip · 121 secondsDeriving Engineering Equations Using Dimensional Analysis YouTubeStart of suggested clipEnd of suggested clip | |
C9475 | The chi-square goodness of fit test is appropriate when the following conditions are met: The sampling method is simple random sampling. The variable under study is categorical. The expected value of the number of sample observations in each level of the variable is at least 5. | |
C9476 | In the development of the probability function for a discrete random variable, two conditions must be satisfied: (1) f(x) must be nonnegative for each value of the random variable, and (2) the sum of the probabilities for each value of the random variable must equal one. | |
C9477 | Q-learning is a model-free reinforcement learning algorithm to learn quality of actions telling an agent what action to take under what circumstances. "Q" names the function that the algorithm computes with the maximum expected rewards for an action taken in a given state. | |
C9478 | Keras is a neural networks library written in Python that is high-level in nature – which makes it extremely simple and intuitive to use. It works as a wrapper to low-level libraries like TensorFlow or Theano high-level neural networks library, written in Python that works as a wrapper to TensorFlow or Theano. | |
C9479 | Centering predictor variables is one of those simple but extremely useful practices that is easily overlooked. It's almost too simple. Centering simply means subtracting a constant from every value of a variable. The effect is that the slope between that predictor and the response variable doesn't change at all. | |
C9480 | Transfer learning is useful when you have insufficient data for a new domain you want handled by a neural network and there is a big pre-existing data pool that can be transferred to your problem. | |
C9481 | Question: 1. When A Value Of Y Is Calculated Using The Regression Equation (Y_hat), It Is Called: -the Fitted Value -the Estimated Value -the Predicted Value -all Of The Above 2. | |
C9482 | The log likelihood This means that if the value on the x-axis increases, the value on the y-axis also increases (see figure below). This is important because it ensures that the maximum value of the log of the probability occurs at the same point as the original probability function. | |
C9483 | Information entropy is a concept from information theory. It tells how much information there is in an event. In general, the more certain or deterministic the event is, the less information it will contain. The "average ambiguity" or Hy(x) meaning uncertainty or entropy. H(x) represents information. | |
C9484 | Classification is a data mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data. For example, a classification model could be used to identify loan applicants as low, medium, or high credit risks. | |
C9485 | Statistical knowledge helps you use the proper methods to collect the data, employ the correct analyses, and effectively present the results. Statistics is a crucial process behind how we make discoveries in science, make decisions based on data, and make predictions. | |
C9486 | When most people hear the term artificial intelligence, the first thing they usually think of is robots. Artificial intelligence is based on the principle that human intelligence can be defined in a way that a machine can easily mimic it and execute tasks, from the most simple to those that are even more complex. | |
C9487 | The sample correlation coefficient, denoted r, For example, a correlation of r = 0.9 suggests a strong, positive association between two variables, whereas a correlation of r = -0.2 suggest a weak, negative association. A correlation close to zero suggests no linear association between two continuous variables. | |
C9488 | The range containing values that are consistent with the null hypothesis is the "acceptance region"; the other range, in which the null hypothesis is rejected, is the rejection region (or critical region). | |
C9489 | Marginal probability effects are the partial effects of each explanatory variable on. the probability that the observed dependent variable Yi = 1, where in probit. models. | |
C9490 | Continuous learning Another way to keep your models up-to-date is to have an automated system to continuously evaluate and retrain your models. This type of system is often referred to as continuous learning, and may look something like this: Save new training data as you receive it. | |
C9491 | In a dataset, a training set is implemented to build up a model, while a test (or validation) set is to validate the model built. Data points in the training set are excluded from the test (validation) set. | |
C9492 | Now we'll check out the proven way to improve the performance(Speed and Accuracy both) of neural network models:Increase hidden Layers. Change Activation function. Change Activation function in Output layer. Increase number of neurons. Weight initialization. More data. Normalizing/Scaling data.More items• | |
C9493 | As a general rule of thumb: If skewness is less than -1 or greater than 1, the distribution is highly skewed. If skewness is between -1 and -0.5 or between 0.5 and 1, the distribution is moderately skewed. If skewness is between -0.5 and 0.5, the distribution is approximately symmetric. | |
C9494 | Prior probability represents what is originally believed before new evidence is introduced, and posterior probability takes this new information into account. A posterior probability can subsequently become a prior for a new updated posterior probability as new information arises and is incorporated into the analysis. | |
C9495 | Usually a pattern recognition system uses training samples from known categories to form a decision rule for unknown patterns. Clustering methods simply try to group similar patterns into clusters whose members are more similar to each other (according to some distance measure) than to members of other clusters. | |
C9496 | There is only one way to roll two 6's on a pair of dice: the first die must be a 6 and the second die must be a 6. The probability is 1/6 × 1/6 = 1/36. There are 3 ways in which to get at least one 6 in the roll of two dice. The first is to roll 6 on both dice, which we already determined has a probability of 1/36. | |
C9497 | Just like the mean value, the median also represents the location of a set of numerical data by means of a single number. Roughly speaking, the median is the value that splits the individual data into two halves: the (approximately) 50% largest and 50% lowest data in the collective. | |
C9498 | Predictive modeling is a form of artificial intelligence that uses data mining and probability to forecast or estimate more granular, specific outcomes. For example, predictive modeling could help identify customers who are likely to purchase our new One AI software over the next 90 days. | |
C9499 | Example 1: Fair Dice Roll The number of desired outcomes is 3 (rolling a 2, 4, or 6), and there are 6 outcomes in total. The a priori probability for this example is calculated as follows: A priori probability = 3 / 6 = 50%. Therefore, the a priori probability of rolling a 2, 4, or 6 is 50%. |
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