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C2300 | Dropout is a regularization technique for neural network models proposed by Srivastava, et al. in their 2014 paper Dropout: A Simple Way to Prevent Neural Networks from Overfitting (download the PDF). Dropout is a technique where randomly selected neurons are ignored during training. They are “dropped-out” randomly. | |
C2301 | The term general linear model (GLM) usually refers to conventional linear regression models for a continuous response variable given continuous and/or categorical predictors. It includes multiple linear regression, as well as ANOVA and ANCOVA (with fixed effects only). | |
C2302 | The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena. For example, heights, blood pressure, measurement error, and IQ scores follow the normal distribution. | |
C2303 | When the population contains higher dimensions or more random variables, a matrix is used to describe the relationship between different dimensions. In a more easy-to-understand way, covariance matrix is to define the relationship in the entire dimensions as the relationships between every two random variables. | |
C2304 | In statistics, the logistic model (or logit model) is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. Each object being detected in the image would be assigned a probability between 0 and 1, with a sum of one. | |
C2305 | Sequence Modeling is the task of predicting what word/letter comes next. Unlike the FNN and CNN, in sequence modeling, the current output is dependent on the previous input and the length of the input is not fixed. | |
C2306 | The mean Average Precision or mAP score is calculated by taking the mean AP over all classes and/or overall IoU thresholds, depending on different detection challenges that exist. In PASCAL VOC2007 challenge, AP for one object class is calculated for an IoU threshold of 0.5. | |
C2307 | Two key benefits of Stochastic Gradient Descent are efficiency and the ease of implementation. In a situation when data is less, classifiers in the module are scaled to problems with more than 10^5 training examples and more than 10^5 features. | |
C2308 | A simple perceptron. Each input is connected to the neuron, shown in gray. Each connection has a weight, the value of which evolves over time, and is used to modify the input. Weighted inputs are summed, and this sum determines the output of the neuron, which is a classification (in this case, either 0 or 1). | |
C2309 | Given that very large datasets are often used to train deep learning neural networks, the batch size is rarely set to the size of the training dataset. Smaller batch sizes are used for two main reasons: Smaller batch sizes are noisy, offering a regularizing effect and lower generalization error. | |
C2310 | Different classifiers are then added on top of this feature extractor to classify images.Support Vector Machines. It is a supervised machine learning algorithm used for both regression and classification problems. Decision Trees. K Nearest Neighbor. Artificial Neural Networks. Convolutional Neural Networks. | |
C2311 | A probability distribution is a list of outcomes and their associated probabilities. A function that represents a discrete probability distribution is called a probability mass function. A function that represents a continuous probability distribution is called a probability density function. | |
C2312 | Definition. Conceptually, a data stream is a sequence of data items that collectively describe one or more underlying signals. A stream model explains how to reconstruct the underlying signals from individual stream items. Thus, understanding the model is a prerequisite for stream processing and stream mining. | |
C2313 | A statistic is a characteristic of a sample. Generally, a statistic is used to estimate the value of a population parameter. For instance, suppose we selected a random sample of 100 students from a school with 1000 students. The average height of the sampled students would be an example of a statistic. | |
C2314 | Expectations via joint densities: Given a function of x and y (e.g., g(x, y) = xy, or g(x, y) = x, etc.), E(g(X, Y )) = ∫∫ g(x, y)f(x, y)dxdy. Independence: X and Y are called independent if the joint p.d.f. is the product of the individual p.d.f.'s, i.e., if f(x, y) = fX(x)fY (y) for all x, y. | |
C2315 | The critical value approach and the P-value approach give the same results when testing hypotheses. The P-value is the probability of obtaining a test statistic as extreme as the one for the current sample under the assumption that the null hypothesis is true. | |
C2316 | In machine learning, the hinge loss is a loss function used for training classifiers. The hinge loss is used for "maximum-margin" classification, most notably for support vector machines (SVMs). For an intended output t = ±1 and a classifier score y, the hinge loss of the prediction y is defined as. | |
C2317 | The beam search strategy generates the translation word by word from left-to-right while keeping a fixed number (beam) of active candidates at each time step. By increasing the beam size, the translation performance can increase at the expense of significantly reducing the decoder speed. | |
C2318 | 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. | |
C2319 | In an analogy to standard deviation, taking the square root of MSE yields the root-mean-square error or root-mean-square deviation (RMSE or RMSD), which has the same units as the quantity being estimated; for an unbiased estimator, the RMSE is the square root of the variance, known as the standard error. | |
C2320 | How to choose the size of the convolution filter or Kernel size1x1 kernel size is only used for dimensionality reduction that aims to reduce the number of channels. It captures the interaction of input channels in just one pixel of feature map. 2x2 and 4x4 are generally not preferred because odd-sized filters symmetrically divide the previous layer pixels around the output pixel. | |
C2321 | Quantization is representing the sampled values of the amplitude by a finite set of levels, which means converting a continuous-amplitude sample into a discrete-time signal. The discrete amplitudes of the quantized output are called as representation levels or reconstruction levels. | |
C2322 | Decision tree algorithms use information gain to split a node. Both gini and entropy are measures of impurity of a node. A node having multiple classes is impure whereas a node having only one class is pure. Entropy in statistics is analogous to entropy in thermodynamics where it signifies disorder. | |
C2323 | The Loss Function is one of the important components of Neural Networks. Loss is nothing but a prediction error of Neural Net. And the method to calculate the loss is called Loss Function. In simple words, the Loss is used to calculate the gradients. And gradients are used to update the weights of the Neural Net. | |
C2324 | Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. | |
C2325 | Three keys to managing bias when building AIChoose the right learning model for the problem. There's a reason all AI models are unique: Each problem requires a different solution and provides varying data resources. Choose a representative training data set. Monitor performance using real data. | |
C2326 | The mean used here is referred to as the arithmetic mean – the sum of all values divided by the number of cases. When working with grouped data, this mean is sometimes referred to as the weighted mean or, more properly, the weighted arithmetic mean. Ungrouped and group methods. | |
C2327 | 1). Now the difference is that Confusion Matrix is used to evaluate the performance of a classifier, and it tells how accurate a classifier is in making predictions about classification, and contingency table is used to evaluate association rules. | |
C2328 | Decision Tree node splitting is an important step, the core issue is how to choose the splitting attribute. 5, the splitting criteria is calculating information gain of each attribute, then the attribute with the maximum information gain or information gain ratio is selected as splitting attribute. | |
C2329 | 'Inverse matrix is a measure of how tightly clustered the variables are around the mean (the diagonal elements) and the extent to which they do not co-vary with the other variables (the off-diagonal elements). Thus, the higher the diagonal element, the tighter the variable is clustered around the mean. | |
C2330 | The purpose of hypothesis testing is to determine whether there is enough statistical evidence in favor of a certain belief, or hypothesis, about a parameter. | |
C2331 | Non-random Variables. A non-random (deterministic, non-stochastic variable) is one whose value is known ahead of time or one whose past value is known. EX: Tomorrow's date, yesterday's temperature. Randomness & Time are linked. | |
C2332 | An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”. | |
C2333 | Partition divides large amount of data into multiple slices based on value of a table column(s). Bucketing decomposes data into more manageable or equal parts. With partitioning, there is a possibility that you can create multiple small partitions based on column values. | |
C2334 | Generally, every constraint satisfaction problem which has clear and well-defined constraints on any objective solution, that incrementally builds candidate to the solution and abandons a candidate (“backtracks”) as soon as it determines that the candidate cannot possibly be completed to a valid solution, can be solved | |
C2335 | Model Decay (also Model Failure) is an informal characterization of pathologies of models already deployed (in operation), whereby the model performance may deteriorate to the point of the model not being any longer fit for purpose. | |
C2336 | Normalization is a technique often applied as part of data preparation for machine learning. The goal of normalization is to change the values of numeric columns in the dataset to a common scale, without distorting differences in the ranges of values. For machine learning, every dataset does not require normalization. | |
C2337 | A Part-Of-Speech Tagger (POS Tagger) is a piece of software that reads text in some language and assigns parts of speech to each word (and other token), such as noun, verb, adjective, etc., although generally computational applications use more fine-grained POS tags like 'noun-plural'. | |
C2338 | the t-test is robust against non-normality; this test is in doubt only when there can be serious outliers (long-tailed distributions – note the finite variance assumption); or when sample sizes are small and distributions are far from normal. 10 / 20 Page 20 . . . | |
C2339 | 1 Answers found. A recursive filter has a system in which the output is directly dependent on one or more of its past outputs. But in a non recursive filter the system followed is the one in which the output is independent of any of the past outputs like, the feed-forward system where the system is having no feedback. | |
C2340 | Sudharsan also noted that deep meta reinforcement learning will be the future of artificial intelligence where we will implement artificial general intelligence (AGI) to build a single model to master a wide variety of tasks. Thus each model will be capable to perform a wide range of complex tasks. | |
C2341 | The Bellman equation is important because it gives us the ability to describe the value of a state s, with the value of the s' state, and with an iterative approach that we will present in the next post, we can calculate the values of all states. | |
C2342 | Dissimilarity Measure Numerical measure of how different two data objects are range from 0 (objects are alike) to (objects are different) Proximity refers to a similarity or dissimilarity. | |
C2343 | You can zoom in TensorBoard by dragging on the chart. You can also make the display larger by pressing the small blue box in the lower-left corner of the chart. | |
C2344 | In statistics, a Poisson distribution is a statistical distribution that shows how many times an event is likely to occur within a specified period of time. It is used for independent events which occur at a constant rate within a given interval of time. | |
C2345 | With dropout (dropout rate less than some small value), the accuracy will gradually increase and loss will gradually decrease first(That is what is happening in your case). When you increase dropout beyond a certain threshold, it results in the model not being able to fit properly. | |
C2346 | Association Rule Mining, as the name suggests, association rules are simple If/Then statements that help discover relationships between seemingly independent relational databases or other data repositories. Most machine learning algorithms work with numeric datasets and hence tend to be mathematical. | |
C2347 | When two events are dependent events, one event influences the probability of another event. A dependent event is an event that relies on another event to happen first. | |
C2348 | Because the Lagrangian multiplier can be considered as a penalty term, and a negative penalty does not make sense. When is zero you are not violating any constraint, however when is infinity you have to satisfy the constraint (i.e - ) or else your objective function will be unbounded. | |
C2349 | A frequency is the number of times a data value occurs. For example, if ten students score 80 in statistics, then the score of 80 has a frequency of 10. Frequency is often represented by the letter f. A frequency chart is made by arranging data values in ascending order of magnitude along with their frequencies. | |
C2350 | The logit model uses something called the cumulative distribution function of the logistic distribution. The probit model uses something called the cumulative distribution function of the standard normal distribution to define f(∗). Both functions will take any number and rescale it to fall between 0 and 1. | |
C2351 | The distribution of a variable is a description of the relative numbers of times each possible outcome will occur in a number of trials. If the measure is a Radon measure (which is usually the case), then the statistical distribution is a generalized function in the sense of a generalized function. | |
C2352 | 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. | |
C2353 | In simple linear regression a single independent variable is used to predict the value of a dependent variable. In multiple linear regression two or more independent variables are used to predict the value of a dependent variable. The difference between the two is the number of independent variables. | |
C2354 | Softmax is an activation function that outputs the probability for each class and these probabilities will sum up to one. Cross Entropy loss is just the sum of the negative logarithm of the probabilities. They are both commonly used together in classifications. | |
C2355 | The moment generating function (MGF) of a random variable X is a function MX(s) defined as MX(s)=E[esX]. We say that MGF of X exists, if there exists a positive constant a such that MX(s) is finite for all s∈[−a,a]. Before going any further, let's look at an example. | |
C2356 | Here are 13 ways you can naturally increase your eagerness to learn and keep feeding your curiosity to stay on your learning goals.Just Show Your Eagerness. Stay Updated. Don't Stop Developing Your Skills. Look for Challenges. Learn Lateral Thinking. Be Open to New Experiences. Start to Be Interesting. Gain Initial Knowledge.More items• | |
C2357 | When those data are biased, model accuracy and fidelity are compromised. Biased models can limit credibility with important stakeholders. At worst, biased models will actively discriminate against certain groups of people. Being aware of these risks allows a Data Scientist to better eliminate bias. | |
C2358 | Systematic random samplingCalculate the sampling interval (the number of households in the population divided by the number of households needed for the sample)Select a random start between 1 and sampling interval.Repeatedly add sampling interval to select subsequent households. | |
C2359 | Conditional random fields (CRFs) are a class of statistical modeling method often applied in pattern recognition and machine learning and used for structured prediction. Whereas a classifier predicts a label for a single sample without considering "neighboring" samples, a CRF can take context into account. | |
C2360 | Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns. | |
C2361 | By using these midpoints as the categorical response values, the researcher can easily calculate averages. Granted, this average will only be an estimate or a “ballpark” value but is still extremely useful for the purpose of data analysis. | |
C2362 | A Type II error is committed when we fail to believe a true condition. Continuing our shepherd and wolf example. Again, our null hypothesis is that there is “no wolf present.” A type II error (or false negative) would be doing nothing (not “crying wolf”) when there is actually a wolf present. | |
C2363 | AUC and accuracy are fairly different things. For a given choice of threshold, you can compute accuracy, which is the proportion of true positives and negatives in the whole data set. AUC measures how true positive rate (recall) and false positive rate trade off, so in that sense it is already measuring something else. | |
C2364 | Support Vector Machine can also be used as a regression method, maintaining all the main features that characterize the algorithm (maximal margin). In the case of regression, a margin of tolerance (epsilon) is set in approximation to the SVM which would have already requested from the problem. | |
C2365 | The bolder the probabilities, the better will be your Log Loss — closer to zero. It is a measure of uncertainty (you may call it entropy), so a low Log Loss means a low uncertainty/entropy of your model. | |
C2366 | In unsupervised learning, there is no training data set and outcomes are unknown. Essentially the AI goes into the problem blind – with only its faultless logical operations to guide it. | |
C2367 | Optimizing Neural Networks — Where to Start?Start with learning rate;Then try number of hidden units, mini-batch size and momentum term;Lastly, tune number of layers and learning rate decay. | |
C2368 | You have not been infected with COVID-19 previously.You had COVID-19 in the past but you did not develop or have not yet developed detectable antibodies.The result may be wrong, known as a false negative. | |
C2369 | There are a number of equations that can generate an S curve, the most common is logistics function with the equation (in Excel notation): S(x) = (1/(1+exp(-kx))^a is the simple form of the equation, where the minimum value is 0 and the maximum value is 1, k and a both >0 and control the shape. | |
C2370 | If we use non - standard units then we may not be able to express our measurement internationally as mainly standard units are used and accepted internationally. The non- standard units do not have the same dimensions all over the world. | |
C2371 | The mn Rule Consider an experiment that is performed in two stages. If the first stage can be accomplished in m different ways and for each of these ways, the second stage can be accomplished in n different ways, then there are to- tal mn different ways to accomplish the experiment. | |
C2372 | Independent and dependent variablesThe independent variable is the cause. Its value is independent of other variables in your study.The dependent variable is the effect. Its value depends on changes in the independent variable. | |
C2373 | The “moments” of a random variable (or of its distribution) are expected values of powers or related functions of the random variable. The rth moment of X is E(Xr). In particular, the first moment is the mean, µX = E(X). The mean is a measure of the “center” or “location” of a distribution. | |
C2374 | Prior probability, in Bayesian statistical inference, is the probability of an event before new data is collected. This is the best rational assessment of the probability of an outcome based on the current knowledge before an experiment is performed. | |
C2375 | The task of object localization is to predict the object in an image as well as its boundaries. Simply, object localization aims to locate the main (or most visible) object in an image while object detection tries to find out all the objects and their boundaries. | |
C2376 | Extreme Value AnalysisFocus on univariate methods.Visualize the data using scatterplots, histograms and box and whisker plots and look for extreme values.Assume a distribution (Gaussian) and look for values more than 2 or 3 standard deviations from the mean or 1.5 times from the first or third quartile.More items• | |
C2377 | The name suggests that layers are fully connected (dense) by the neurons in a network layer. In other words, the dense layer is a fully connected layer, meaning all the neurons in a layer are connected to those in the next layer. | |
C2378 | split testing | |
C2379 | This paper explains that to be a potential confounder, a variable needs to satisfy all three of the following criteria: (1) it must have an association with the disease, that is, it should be a risk factor for the disease; (2) it must be associated with the exposure, that is, it must be unequally distributed between | |
C2380 | Option D is correct. Q25. Instead of trying to achieve absolute zero error, we set a metric called bayes error which is the error we hope to achieve. | |
C2381 | Neural networks take input data, train themselves to recognize patterns found in the data, and then predict the output for a new set of similar data. Therefore, a neural network can be thought of as the functional unit of deep learning, which mimics the behavior of the human brain to solve complex data-driven problems. | |
C2382 | The resulting image after applying Canny operator (b). The primary advantages of the Sobel operator lie in its simplicity. The Sobel method provides a approximation to the gradient magnitude. Another advantage of the Sobel operator is it can detect edges and their orientations. | |
C2383 | So, the rule of thumb is: use linear SVMs (or logistic regression) for linear problems, and nonlinear kernels such as the Radial Basis Function kernel for non-linear problems. | |
C2384 | Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g. differentiable or subdifferentiable). | |
C2385 | A probability distribution may be either discrete or continuous. A discrete distribution means that X can assume one of a countable (usually finite) number of values, while a continuous distribution means that X can assume one of an infinite (uncountable) number of different values. | |
C2386 | To visualize a small data set containing multiple categorical (or qualitative) variables, you can create either a bar plot, a balloon plot or a mosaic plot. These methods make it possible to analyze and visualize the association (i.e. correlation) between a large number of qualitative variables. | |
C2387 | The idea is to separate the image into two parts; the background and foreground.Select initial threshold value, typically the mean 8-bit value of the original image.Divide the original image into two portions; Find the average mean values of the two new images.Calculate the new threshold by averaging the two means.More items | |
C2388 | Both methods are data dependent. Statistical Learning is math intensive which is based on the coefficient estimator and requires a good understanding of your data. On the other hand, Machine Learning identifies patterns from your dataset through the iterations which require a way less of human effort. | |
C2389 | In statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a | |
C2390 | Apriori is useable with large datasets and Eclat is better suited to small and medium datasets. Apriori scans the original (real) dataset, whereas Eclat scan the currently generated dataset. Apriori is slower than Eclat. | |
C2391 | In statistics, the Huber loss is a loss function used in robust regression, that is less sensitive to outliers in data than the squared error loss. A variant for classification is also sometimes used. | |
C2392 | Stepwise regression is an appropriate analysis when you have many variables and you're interested in identifying a useful subset of the predictors. In Minitab, the standard stepwise regression procedure both adds and removes predictors one at a time. | |
C2393 | quality | |
C2394 | to safeguard against the researcher problem of experimenter bias, researchers employ blind observers, single and double blind study, and placebos. to control for ethnocentrism, they use cross cultural sampling. | |
C2395 | 0:404:05Suggested clip · 100 secondsHow to interpret a survival plot - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C2396 | Some business analysts at claim that AI is a game changer for the personal device market. By 2020, about 60 percent of personal-device technology vendors will depend on AI-enabled Cloud platforms to deliver enhanced functionality and personalized services. AI technology will deliver an “emotional user experience.” | |
C2397 | Anomaly Detection MethodsSupervised methods. As the name suggests, this anomaly detection method requires the existence of a labeled dataset that contains both normal and anomalous data points. Unsupervised methods. Intrusion detection. Mobile sensor data. Network server or app failure. Statistical Process Control. | |
C2398 | Not including the null hypothesis in your research is considered very bad practice by the scientific community. If you set out to prove an alternate hypothesis without considering it, you are likely setting yourself up for failure. At a minimum, your experiment will likely not be taken seriously. | |
C2399 | Sequence-to-sequence learning (Seq2Seq) is about training models to convert sequences from one domain (e.g. sentences in English) to sequences in another domain (e.g. the same sentences translated to French). |
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