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C7800 | It is not a stretch to say in a few weeks of study you could be familiar with the framework API. However, if you want to present novel ideas in deep learning through research it would likely take 1 - 4 years given your background. | |
C7801 | Instead of data distribution across several programs, ERP consolidates information in one place for better decision-making, including data accumulated from sources across the supply chain. It also boosts the quality of a company's data, resulting in more reliable information for decision-making. | |
C7802 | The first step would be to get comfortable with the concepts of permutations and combinations. Step 1- learn permutations and combinations from 11th class NCERT.Step 2 - practice as many questions as you can on this topic . Step 3 - once you have done that, read probability from 11th NCERT.More items | |
C7803 | It also makes life easier because we only need one table (the Standard Normal Distribution Table), rather than doing calculations individually for each value of mean and standard deviation. | |
C7804 | The mean squared error tells you how close a regression line is to a set of points. It does this by taking the distances from the points to the regression line (these distances are the “errors”) and squaring them. The squaring is necessary to remove any negative signs. It also gives more weight to larger differences. | |
C7805 | p = randperm( n ) returns a row vector containing a random permutation of the integers from 1 to n without repeating elements. p = randperm( n , k ) returns a row vector containing k unique integers selected randomly from 1 to n . | |
C7806 | While machine learning is based on the idea that machines should be able to learn and adapt through experience, AI refers to a broader idea where machines can execute tasks "smartly." Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. | |
C7807 | The t-value measures the size of the difference relative to the variation in your sample data. Put another way, T is simply the calculated difference represented in units of standard error. The greater the magnitude of T, the greater the evidence against the null hypothesis. | |
C7808 | The hidden-curriculum concept is based on the recognition that students absorb lessons in school that may or may not be part of the formal course of study—for example, how they should interact with peers, teachers, and other adults; how they should perceive different races, groups, or classes of people; or what ideas | |
C7809 | Feature Selection. Feature selection is for filtering irrelevant or redundant features from your dataset. The key difference between feature selection and extraction is that feature selection keeps a subset of the original features while feature extraction creates brand new ones. | |
C7810 | Explanation: The objective of perceptron learning is to adjust weight along with class identification. | |
C7811 | Latent classes divide the cases into their respective dimensions in relation to the variable. For example, cluster analysis groups similar cases and puts them into one group. The numbers of clusters in the cluster analysis are called the latent classes. In SEM, the number of constructs is called the latent classed. | |
C7812 | The hazard rate refers to the rate of death for an item of a given age (x). It is part of a larger equation called the hazard function, which analyzes the likelihood that an item will survive to a certain point in time based on its survival to an earlier time (t). | |
C7813 | Each kernel function (the part) just takes in your input and compares it to some and tells you how much it matches. All those kernels each output their match amount which is then put together via a weighted linear combination. So yeah, it's template matching. | |
C7814 | The Poisson Distribution is a tool used in probability theory statistics. It is used to test if a statement regarding a population parameter is correct. Hypothesis testing to predict the amount of variation from a known average rate of occurrence, within a given time frame. | |
C7815 | If x is a lognormally distributed random variable, then y = ln(x) is a normally distributed random variable. The location parameter is equal to the mean of the logarithm of the data points, and the shape parameter is equal to the standard deviation of the logarithm of the data points. | |
C7816 | 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. | |
C7817 | A Seq2Seq model is a model that takes a sequence of items (words, letters, time series, etc) and outputs another sequence of items. The encoder captures the context of the input sequence in the form of a hidden state vector and sends it to the decoder, which then produces the output sequence. | |
C7818 | Types of predictive modelsForecast models. A forecast model is one of the most common predictive analytics models. Classification models. Outliers Models. Time series model. Clustering Model. The need for massive training datasets. Properly categorising data. | |
C7819 | What i.i.d. assumption states is that random variables are independent and identically distributed. You can formally define what does it mean, but informally it says that all the variables provide the same kind of information independently of each other (you can read also about related exchangeability). | |
C7820 | The AUC for the ROC can be calculated using the roc_auc_score() function. Like the roc_curve() function, the AUC function takes both the true outcomes (0,1) from the test set and the predicted probabilities for the 1 class. It returns the AUC score between 0.0 and 1.0 for no skill and perfect skill respectively. | |
C7821 | In a hypothesis test, we:Evaluate the null hypothesis, typically denoted with H0. Always write the alternative hypothesis, typically denoted with Ha or H1, using less than, greater than, or not equals symbols, i.e., (≠, >, or <).More items | |
C7822 | The normal distribution is a continuous probability distribution. This has several implications for probability. The total area under the normal curve is equal to 1. The probability that a normal random variable X equals any particular value is 0. | |
C7823 | Now we'll check out the proven way to improve the accuracy of a model:Add more data. Having more data is always a good idea. Treat missing and Outlier values. Feature Engineering. Feature Selection. Multiple algorithms. Algorithm Tuning. Ensemble methods. | |
C7824 | Use In Exponential Distributions It is defined as the reciprocal of the scale parameter and indicates how quickly decay of the exponential function occurs. When the rate parameter = 1, there is no decay. Values close to 1 (e.g. 0.8 or 0.9) indicate a slow decay. | |
C7825 | Examine the table and note that a "Z" score of 0.0 lists a probability of 0.50 or 50%, and a "Z" score of 1, meaning one standard deviation above the mean, lists a probability of 0.8413 or 84%. | |
C7826 | Therefore, the probability of committing a type II error is 2.5%. | |
C7827 | Machine learning algorithms are almost always optimized for raw, detailed source data. Thus, the data environment must provision large quantities of raw data for discovery-oriented analytics practices such as data exploration, data mining, statistics, and machine learning. | |
C7828 | An ROC curve shows the relationship between clinical sensitivity and specificity for every possible cut-off. The ROC curve is a graph with: The x-axis showing 1 – specificity (= false positive fraction = FP/(FP+TN)) The y-axis showing sensitivity (= true positive fraction = TP/(TP+FN)) | |
C7829 | The Chi-square test is intended to test how likely it is that an observed distribution is due to chance. It is also called a "goodness of fit" statistic, because it measures how well the observed distribution of data fits with the distribution that is expected if the variables are independent. | |
C7830 | "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. | |
C7831 | A distinction of sampling bias (albeit not a universally accepted one) is that it undermines the external validity of a test (the ability of its results to be generalized to the rest of the population), while selection bias mainly addresses internal validity for differences or similarities found in the sample at hand. | |
C7832 | In probability theory, a log-normal (or lognormal) distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed. A log-normal process is the statistical realization of the multiplicative product of many independent random variables, each of which is positive. | |
C7833 | The p-values is affected by the sample size. Larger the sample size, smaller is the p-values. Increasing the sample size will tend to result in a smaller P-value only if the null hypothesis is false. | |
C7834 | Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset. | |
C7835 | The probability of committing a type II error is equal to one minus the power of the test, also known as beta. The power of the test could be increased by increasing the sample size, which decreases the risk of committing a type II error. | |
C7836 | How to find the mean of the probability distribution: StepsStep 1: Convert all the percentages to decimal probabilities. For example: Step 2: Construct a probability distribution table. Step 3: Multiply the values in each column. Step 4: Add the results from step 3 together. | |
C7837 | Generative Adversarial Networks takes up a game-theoretic approach, unlike a conventional neural network. The network learns to generate from a training distribution through a 2-player game. The two entities are Generator and Discriminator. These two adversaries are in constant battle throughout the training process. | |
C7838 | For a classification problem Random Forest gives you probability of belonging to class. SVM gives you distance to the boundary, you still need to convert it to probability somehow if you need probability. SVM gives you "support vectors", that is points in each class closest to the boundary between classes. | |
C7839 | Loss function characterizes how well the model performs over the training dataset, regularization term is used to prevent overfitting [7], and λ balances between the two. Conventionally, λ is called hyperparameter. Different ML algorithms use different loss functions and/or regularization terms. | |
C7840 | If we want to indicate the uncertainty around the estimate of the mean measurement, we quote the standard error of the mean. The standard error is most useful as a means of calculating a confidence interval. For a large sample, a 95% confidence interval is obtained as the values 1.96×SE either side of the mean. | |
C7841 | Joint probability is calculated by multiplying the probability of event A, expressed as P(A), by the probability of event B, expressed as P(B). For example, suppose a statistician wishes to know the probability that the number five will occur twice when two dice are rolled at the same time. | |
C7842 | It is a Softmax activation plus a Cross-Entropy loss. If we use this loss, we will train a CNN to output a probability over the C classes for each image. It is used for multi-class classification. | |
C7843 | In statistics, a contingency table (also known as a cross tabulation or crosstab) is a type of table in a matrix format that displays the (multivariate) frequency distribution of the variables. They provide a basic picture of the interrelation between two variables and can help find interactions between them. | |
C7844 | Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces. | |
C7845 | When used as nouns, quantile means one of the class of values of a variate which divides the members of a batch or sample into equal-sized subgroups of adjacent values or a probability distribution into distributions of equal probability, whereas quartile means any of the three points that divide an ordered | |
C7846 | While PCA is based on Euclidean distances, PCoA can handle (dis)similarity matrices calculated from quantitative, semi-quantitative, qualitative, and mixed variables. When the distance metric is Euclidean, PCoA is equivalent to Principal Components Analysis. | |
C7847 | Linear regression models are used to show or predict the relationship between two variables or factors. The factor that is being predicted (the factor that the equation solves for) is called the dependent variable. | |
C7848 | A histogram (graph) of these values provides the sampling distribution of the statistic. The law of large numbers holds that as n increases, a statistic such as the sample mean (X) converges to its true mean (f)—that is, the sampling distribution of the mean collapses on the population mean. | |
C7849 | (algorithm) The assignment of start and end times to a set of tasks, subject to certain constraints. | |
C7850 | Increase the sample size. Often, the most practical way to decrease the margin of error is to increase the sample size. Reduce variability. The less that your data varies, the more precisely you can estimate a population parameter. Use a one-sided confidence interval. Lower the confidence level. | |
C7851 | TL;DR: Entropy is not quantized. Entropy is often stated to be the logarithm of the number of Quantum States accessible to the system. Entropy is often stated to be the logarithm of the number of Quantum States accessible to the system. | |
C7852 | The area to the left of x (point of interest) is equal to probability of the x-axis variable being less than the value of x (point of interest). The probability density is the y-axis. The PDF works for discrete and continuous data distributions. | |
C7853 | The law of averages is often mistaken by many people as the law of large numbers, but there is a big difference. The law of averages is a spurious belief that any deviation in expected probability will have to average out in a small sample of consecutive experiments, but this is not necessarily true. | |
C7854 | Analysis methods you might considerNegative binomial regression – Negative binomial regression can be used for over-dispersed count data, that is when the conditional variance exceeds the conditional mean. Poisson regression – Poisson regression is often used for modeling count data.More items | |
C7855 | Regression attempts to establish how X causes Y to change and the results of the analysis will change if X and Y are swapped. With correlation, the X and Y variables are interchangeable. Correlation is a single statistic, whereas regression produces an entire equation. | |
C7856 | The coefficient of determination is a measurement used to explain how much variability of one factor can be caused by its relationship to another related factor. This correlation, known as the "goodness of fit," is represented as a value between 0.0 and 1.0. | |
C7857 | The sampling distribution assumes that the null hypothesis is true. When we compare an obtained test statistic to the sampling distribution, we're asking how likely it is that we would get that statistic if we were sampling from a population that has the null hypothesis characteristics (e.g., P = 0.50). | |
C7858 | Artificial neural networks (ANN) is the key tool of machine learning. Neural networks (NN) constitute both input & output layer, as well as a hidden layer containing units that change input into output so that output layer can utilise the value. | |
C7859 | On average the interpolation search makes about log(log(n)) comparisons (if the elements are uniformly distributed), where n is the number of elements to be searched. In the worst case (for instance where the numerical values of the keys increase exponentially) it can make up to O(n) comparisons. | |
C7860 | In mathematics, the membership function of a fuzzy set is a generalization of the indicator function for classical sets. In fuzzy logic, it represents the degree of truth as an extension of valuation. | |
C7861 | Using the change of variable x=λy, we can show the following equation that is often useful when working with the gamma distribution: Γ(α)=λα∫∞0yα−1e−λydyfor α,λ>0.For any positive real number α:Γ(α)=∫∞0xα−1e−xdx;∫∞0xα−1e−λxdx=Γ(α)λα,for λ>0;Γ(α+1)=αΓ(α);Γ(n)=(n−1)!, for n=1,2,3,⋯;Γ(12)=√π. | |
C7862 | Hypothesis Testing > Results from a statistical tests will fall into one of two regions: the rejection region— which will lead you to reject the null hypothesis, or the acceptance region, where you provisionally accept the null hypothesis. | |
C7863 | As Justin Rising points out, the order statistics are clearly not independent of each other. . If the observations are independent and identically distributed from a continuous distribution, then any ordering of the samples is equally likely. | |
C7864 | The Wilcoxon signed rank test is a nonparametric test that compares the median of a set of numbers against a hypothetical median. The Wilcoxon rank sum test is a nonparametric test to compare two unmatched groups. It is equivalent to the Mann-Whitney test. | |
C7865 | A trimmed mean (similar to an adjusted mean) is a method of averaging that removes a small designated percentage of the largest and smallest values before calculating the mean. The use of a trimmed mean helps eliminate the influence of outliers or data points on the tails that may unfairly affect the traditional mean. | |
C7866 | So, for example, if our random variable were the number obtained by rolling a fair 3-sided die, the expected value would be (1 * 1/3) + (2 * 1/3) + (3 * 1/3) = 2. | |
C7867 | Average Linkage is a type of hierarchical clustering in which the distance between one cluster and another cluster is considered to be equal to the average distance from any member of one cluster to any member of the other cluster. | |
C7868 | Data Analysis. Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. An essential component of ensuring data integrity is the accurate and appropriate analysis of research findings. | |
C7869 | Deep learning is a subset of machine learning so technically machine learning is required for machine learning. However, it is not necessary for you to learn the machine learning algorithms that are not a part of machine learning in order to learn deep learning. | |
C7870 | Definition. The class intervals are the subsets into which the data is grouped. The width of the class intervals will be a compromise between having intervals short enough so that not all of the observations fall in the same interval, but long enough so that you do not end up with only one observation per interval. | |
C7871 | 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. | |
C7872 | A Markov chain is a mathematical system that experiences transitions from one state to another according to certain probabilistic rules. The defining characteristic of a Markov chain is that no matter how the process arrived at its present state, the possible future states are fixed. | |
C7873 | Collaborative filtering (CF) is a technique used by recommender systems. For example, a collaborative filtering recommendation system for television tastes could make predictions about which television show a user should like given a partial list of that user's tastes (likes or dislikes). | |
C7874 | TensorFlow Lite inferenceAndroid Platform.iOS Platform.Linux Platform. | |
C7875 | Feature Selection. The key difference between feature selection and extraction is that feature selection keeps a subset of the original features while feature extraction creates brand new ones. | |
C7876 | A point to remember is that the main purpose of acceptance sampling is to decide whether or not the lot is likely to be acceptable, not to estimate the quality of the lot. Acceptance sampling is employed when one or several of the following hold: Testing is destructive. The cost of 100% inspection is very high. | |
C7877 | Multi-view data is common in real-world datasets, where different views describe distinct perspec- tives. Multi-view data is prevalent in many real-world applications. For instance, the same news can be obtained from various language sources; an image can be described by different low level visual features. | |
C7878 | 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. | |
C7879 | In an upper-tailed test the decision rule has investigators reject H0 if the test statistic is larger than the critical value. In a lower-tailed test the decision rule has investigators reject H0 if the test statistic is smaller than the critical value. | |
C7880 | ReLu bounded negative outputs to 0 & above. This works well in hidden layers than the final output layer. It is not typical, since in this case, the ouput value is not bounded in a range. | |
C7881 | The biggest advantage of Deep Learning is that we do not need to manually extract features from the image. The network learns to extract features while training. You just feed the image to the network (pixel values). | |
C7882 | Type I and II Errors and Significance Levels. Rejecting the null hypothesis when it is in fact true is called a Type I error. Most people would not consider the improvement practically significant. Caution: The larger the sample size, the more likely a hypothesis test will detect a small difference. | |
C7883 | The main downside was that it was a pretty large network in terms of the number of parameters to be trained. VGG-19 neural network which is bigger then VGG-16, but because VGG-16 does almost as well as the VGG-19 a lot of people will use VGG-16. | |
C7884 | How Change Detection WorksDeveloper updates the data model, e.g. by updating a component binding.Angular detects the change.Change detection checks every component in the component tree from top to bottom to see if the corresponding model has changed.If there is a new value, it will update the component's view (DOM) | |
C7885 | The reason for using L1 norm to find a sparse solution is due to its special shape. It has spikes that happen to be at sparse points. Using it to touch the solution surface will very likely to find a touch point on a spike tip and thus a sparse solution. | |
C7886 | Adaptive Resonance Theory, or ART, algorithms overcome the computational problems of back propagation and Deep Learning. These biological models exemplify complementary computing, and use local laws for match learning and mismatch learning that avoid the problems of Deep Learning. | |
C7887 | There are three types of proposition: fact, value and policy. | |
C7888 | If the hazard ratio is less than 1, then the predictor is protective (i.e., associated with improved survival) and if the hazard ratio is greater than 1, then the predictor is associated with increased risk (or decreased survival). | |
C7889 | Bayes' theorem is a formula that describes how to update the probabilities of hypotheses when given evidence. It follows simply from the axioms of conditional probability, but can be used to powerfully reason about a wide range of problems involving belief updates. | |
C7890 | A classic use of a statistical test occurs in process control studies. For example, suppose that we are interested in ensuring that photomasks in a production process have mean linewidths of 500 micrometers. The null hypothesis, in this case, is that the mean linewidth is 500 micrometers. | |
C7891 | With stratified sampling, the sample includes elements from each stratum. With cluster sampling, in contrast, the sample includes elements only from sampled clusters. Multistage sampling. With multistage sampling, we select a sample by using combinations of different sampling methods. | |
C7892 | 2 Multivariate Data. Multivariate data contains, at each sample point, multiple scalar values that represent different simulated or measured quantities. | |
C7893 | From Wikipedia, the free encyclopedia. Cohen's kappa coefficient (κ) is a statistic that is used to measure inter-rater reliability (and also Intra-rater reliability) for qualitative (categorical) items. | |
C7894 | The metric our intuition tells us we should maximize is known in statistics as recall, or the ability of a model to find all the relevant cases within a dataset. The precise definition of recall is the number of true positives divided by the number of true positives plus the number of false negatives. | |
C7895 | Feature selection is for filtering irrelevant or redundant features from your dataset. The key difference between feature selection and extraction is that feature selection keeps a subset of the original features while feature extraction creates brand new ones. | |
C7896 | Some of the most popular methods for outlier detection are:Z-Score or Extreme Value Analysis (parametric)Probabilistic and Statistical Modeling (parametric)Linear Regression Models (PCA, LMS)Proximity Based Models (non-parametric)Information Theory Models.More items | |
C7897 | There is really only one advantage to using a random forest over a decision tree: It reduces overfitting and is therefore more accurate. | |
C7898 | The number of hidden neurons should be between the size of the input layer and the size of the output layer. The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer. The number of hidden neurons should be less than twice the size of the input layer. | |
C7899 | The singular value decomposition (SVD) provides another way to factorize a matrix, into singular vectors and singular values. The SVD is used widely both in the calculation of other matrix operations, such as matrix inverse, but also as a data reduction method in machine learning. |
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