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C6700 | Interpolation is also used to simplify complicated functions by sampling data points and interpolating them using a simpler function. Polynomials are commonly used for interpolation because they are easier to evaluate, differentiate, and integrate - known as polynomial interpolation. | |
C6701 | Batch Normalization during inference During testing or inference phase we can't apply the same batch-normalization as we did during training because we might pass only sample at a time so it doesn't make sense to find mean and variance on a single sample. | |
C6702 | If the mean more accurately represents the center of the distribution of your data, and your sample size is large enough, use a parametric test. If the median more accurately represents the center of the distribution of your data, use a nonparametric test even if you have a large sample size. | |
C6703 | A* achieves better performance by using heuristics to guide its search. A* combines the advantages of Best-first Search and Uniform Cost Search: ensure to find the optimized path while increasing the algorithm efficiency using heuristics. If h(n)=0, then A* turns to be Uniform-Cost Search. | |
C6704 | Dimensionality reduction refers to techniques that reduce the number of input variables in a dataset. Large numbers of input features can cause poor performance for machine learning algorithms. Dimensionality reduction is a general field of study concerned with reducing the number of input features. | |
C6705 | Normal distributions come up time and time again in statistics. A normal distribution has some interesting properties: it has a bell shape, the mean and median are equal, and 68% of the data falls within 1 standard deviation. | |
C6706 | F-test is used either for testing the hypothesis about the equality of two population variances or the equality of two or more population means. The equality of two population means was dealt with t-test. Besides a t-test, we can also apply F-test for testing equality of two population means. | |
C6707 | In linear regression the independent variables can be categorical and/or continuous. But, when you fit the model if you have more than two category in the categorical independent variable make sure you are creating dummy variables. | |
C6708 | Sample size measures the number of individual samples measured or observations used in a survey or experiment. For example, if you test 100 samples of soil for evidence of acid rain, your sample size is 100. If an online survey returned 30,500 completed questionnaires, your sample size is 30,500. | |
C6709 | Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking). | |
C6710 | Hopfield in 1982. It consists of a single layer which contains one or more fully connected recurrent neurons. The Hopfield network is commonly used for auto-association and optimization tasks. | |
C6711 | Analysis of Variance (ANOVA) consists of calculations that provide information about levels of variability within a regression model and form a basis for tests of significance. | |
C6712 | In mathematics, a divergent series is an infinite series that is not convergent, meaning that the infinite sequence of the partial sums of the series does not have a finite limit. The divergence of the harmonic series was proven by the medieval mathematician Nicole Oresme. | |
C6713 | It is technically defined as "the nth root product of n numbers." The geometric mean must be used when working with percentages, which are derived from values, while the standard arithmetic mean works with the values themselves. The harmonic mean is best used for fractions such as rates or multiples. | |
C6714 | The law of averages is a false belief, sometimes known as the 'gambler's fallacy,' that is derived from the law of large numbers. The law of averages is a misconception that probability occurs with a small number of consecutive experiments so they will certainly have to 'average out' sooner rather than later. | |
C6715 | Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. | |
C6716 | Discriminant or discriminant function analysis is a. parametric technique to determine which weightings of. quantitative variables or predictors best discriminate. between 2 or more than 2 groups of cases and do so. | |
C6717 | TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines. When you're ready to move your models from research to production, use TFX to create and manage a production pipeline. | |
C6718 | Precision is determined by a statistical method called a standard deviation. Standard deviation is how much, on average, measurements differ from each other. High standard deviations indicate low precision, low standard deviations indicate high precision. | |
C6719 | In statistics, Bessel's correction is the use of n − 1 instead of n in the formula for the sample variance and sample standard deviation, where n is the number of observations in a sample. This method corrects the bias in the estimation of the population variance. | |
C6720 | Yes, in fact many processors provide two TLBs for this very reason. As an example, the code being accessed by a process may retain the same working set for a long period of time. However, the data the code accesses may change, thus reflecting a change in the working set for data accesses. | |
C6721 | Quartiles are the values that divide a list of numbers into quarters: Put the list of numbers in order. Then cut the list into four equal parts.In this case all the quartiles are between numbers:Quartile 1 (Q1) = (4+4)/2 = 4.Quartile 2 (Q2) = (10+11)/2 = 10.5.Quartile 3 (Q3) = (14+16)/2 = 15. | |
C6722 | LMBP algorithm | |
C6723 | In reality, for deep learning and big data tasks standard gradient descent is not often used. Rather, a variant of gradient descent called stochastic gradient descent and in particular its cousin mini-batch gradient descent is used. | |
C6724 | Using Logarithmic Functions Much of the power of logarithms is their usefulness in solving exponential equations. Some examples of this include sound (decibel measures), earthquakes (Richter scale), the brightness of stars, and chemistry (pH balance, a measure of acidity and alkalinity). | |
C6725 | The Markov condition, sometimes called the Markov assumption, is an assumption made in Bayesian probability theory, that every node in a Bayesian network is conditionally independent of its nondescendents, given its parents. Stated loosely, it is assumed that a node has no bearing on nodes which do not descend from it. | |
C6726 | 8:3111:15Suggested clip · 94 secondsIntroduction to Tensors - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C6727 | It is usually defined as the ratio of the variance to the mean. As a formula, that's: D = σ2 / μ. | |
C6728 | Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish). | |
C6729 | Reliability refers to how dependably or consistently a test measures a characteristic. If a person takes the test again, will he or she get a similar test score, or a much different score? A test that yields similar scores for a person who repeats the test is said to measure a characteristic reliably. | |
C6730 | : a proposition or theorem formed by contradicting both the subject and predicate or both the hypothesis and conclusion of a given proposition or theorem and interchanging them "if not-B then not-A " is the contrapositive of "if A then B " | |
C6731 | A null hypothesis is a type of conjecture used in statistics that proposes that there is no difference between certain characteristics of a population or data-generating process. The alternative hypothesis proposes that there is a difference. | |
C6732 | Random errors are statistical fluctuations (in either direction) in the measured data due to the precision limitations of the measurement device. Random errors usually result from the experimenter's inability to take the same measurement in exactly the same way to get exact the same number. | |
C6733 | MSE is used to check how close estimates or forecasts are to actual values. Lower the MSE, the closer is forecast to actual. This is used as a model evaluation measure for regression models and the lower value indicates a better fit. | |
C6734 | n = norm( v ) returns the Euclidean norm of vector v . This norm is also called the 2-norm, vector magnitude, or Euclidean length. n = norm( v , p ) returns the generalized vector p-norm. n = norm( X ) returns the 2-norm or maximum singular value of matrix X , which is approximately max(svd(X)) . | |
C6735 | A dendrogram is a diagram that shows the hierarchical relationship between objects. It is most commonly created as an output from hierarchical clustering. The main use of a dendrogram is to work out the best way to allocate objects to clusters. (Dendrogram is often miswritten as dendogram.) | |
C6736 | Multi-view learning is an emerging direction in machine learning which considers learning with multiple views to improve the generalization performance. Multi-view learning is also known as data fusion or data integration from multiple feature sets. | |
C6737 | Use loss-based decoding to classify examples — instead of taking the sign of the output of each classifier, com- pute the actual loss, using the training loss function (hinge loss for SVM, square loss for RLSC). | |
C6738 | Examples of Factor Analysis Studies Factor analysis provides simplicity after reducing variables. For long studies with large blocks of Matrix Likert scale questions, the number of variables can become unwieldy. Simplifying the data using factor analysis helps analysts focus and clarify the results. | |
C6739 | The standard deviation of X is σ=√(b−a)212. The probability density function of X is f(x)=1b−a for a≤x≤b. The cumulative distribution function of X is P(X≤x)=x−ab−a. | |
C6740 | To find the relative frequency, divide the frequency by the total number of data values. To find the cumulative relative frequency, add all of the previous relative frequencies to the relative frequency for the current row. | |
C6741 | A normal distribution is determined by two parameters the mean and the variance. Now the standard normal distribution is a specific distribution with mean 0 and variance 1. This is the distribution that is used to construct tables of the normal distribution. | |
C6742 | A negative binomial random variable is the number X of repeated trials to produce r successes in a negative binomial experiment. The probability distribution of a negative binomial random variable is called a negative binomial distribution. Suppose we flip a coin repeatedly and count the number of heads (successes). | |
C6743 | Correlation Coefficient = 0.8: A fairly strong positive relationship. Correlation Coefficient = 0.6: A moderate positive relationship. Correlation Coefficient = -0.8: A fairly strong negative relationship. Correlation Coefficient = -0.6: A moderate negative relationship. | |
C6744 | The Machine Learning algorithms that require the feature scaling are mostly KNN (K-Nearest Neighbours), Neural Networks, Linear Regression, and Logistic Regression. | |
C6745 | Spectral analysis is the process of breaking down a signal into its components at various frequencies, and in the context of acoustics there are two very different ways of doing this, depending on whether the result is desired on a linear frequency scale with constant resolution (in Hz) or on a logarithmic frequency | |
C6746 | 11 websites to find free, interesting datasetsFiveThirtyEight. BuzzFeed News. Kaggle. Socrata. Awesome-Public-Datasets on Github. Google Public Datasets. UCI Machine Learning Repository. Data.gov.More items | |
C6747 | Conditional entropy. In information theory, the conditional entropy quantifies the amount of information needed to describe the outcome of a random variable given that the value of another random variable is known. | |
C6748 | This is why it is important to distinguish between the statistical significance of a result and the practical significance of that result. Null hypothesis testing is a formal approach to deciding whether a statistical relationship in a sample reflects a real relationship in the population or is just due to chance. | |
C6749 | Other ways of avoiding experimenter's bias include standardizing methods and procedures to minimize differences in experimenter-subject interactions; using blinded observers or confederates as assistants, further distancing the experimenter from the subjects; and separating the roles of investigator and experimenter. | |
C6750 | Attention models, or attention mechanisms, are input processing techniques for neural networks that allows the network to focus on specific aspects of a complex input, one at a time until the entire dataset is categorized. Attention models require continuous reinforcement or backpopagation training to be effective. | |
C6751 | Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves. | |
C6752 | The outcome variable is also called the response or dependent variable, and the risk factors and confounders are called the predictors, or explanatory or independent variables. In regression analysis, the dependent variable is denoted "Y" and the independent variables are denoted by "X". | |
C6753 | A decision boundary is the region of a problem space in which the output label of a classifier is ambiguous. If the decision surface is a hyperplane, then the classification problem is linear, and the classes are linearly separable. Decision boundaries are not always clear cut. | |
C6754 | The main difference between CNN and RNN is the ability to process temporal information or data that comes in sequences, such as a sentence for example. Whereas, RNNs reuse activation functions from other data points in the sequence to generate the next output in a series. | |
C6755 | A condition variable indicates an event and has no value. More precisely, one cannot store a value into nor retrieve a value from a condition variable. If a thread must wait for an event to occur, that thread waits on the corresponding condition variable. | |
C6756 | The filter is a device that allows passing the dc component of the load and blocks the ac component of the rectifier output. Thus the output of the filter circuit will be a steady dc voltage. Capacitor is used so as to block the dc and allows ac to pass. | |
C6757 | A random forest is simply a collection of decision trees whose results are aggregated into one final result. Their ability to limit overfitting without substantially increasing error due to bias is why they are such powerful models. One way Random Forests reduce variance is by training on different samples of the data. | |
C6758 | If you don't have enough time to read through the entire post, the following hits on the key components: Bag-of-words: How to break up long text into individual words. Filtering: Different approaches to remove uninformative words. Bag of n-grams: Retain some context by breaking long text into sequences of words. | |
C6759 | The time complexity of minimax is O(b^m) and the space complexity is O(bm), where b is the number of legal moves at each point and m is the maximum depth of the tree. N-move look ahead is a variation of minimax that is applied when there is no time to search all the way to the leaves of the tree. | |
C6760 | Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. | |
C6761 | At the foundation of quantum mechanics is the Heisenberg uncertainty principle. Simply put, the principle states that there is a fundamental limit to what one can know about a quantum system. Heisenberg sometimes explained the uncertainty principle as a problem of making measurements. | |
C6762 | Pixel binning is a clocking scheme used to combine the charge collected by several adjacent CCD pixels, and is designed to reduce noise and improve the signal-to-noise ratio and frame rate of digital cameras. | |
C6763 | Binary Search: Search a sorted array by repeatedly dividing the search interval in half. Begin with an interval covering the whole array. If the value of the search key is less than the item in the middle of the interval, narrow the interval to the lower half. | |
C6764 | In information theory, the entropy of a random variable is the average level of "information", "surprise", or "uncertainty" inherent in the variable's possible outcomes. The concept of information entropy was introduced by Claude Shannon in his 1948 paper "A Mathematical Theory of Communication". | |
C6765 | TipsUnderstand the concepts. Make sure you understand the concepts first before you memorize them.Start with the hard stuff. Use the stoplight approach if you are having problems applying or understanding key concepts.Create colour-coded flashcards. | |
C6766 | In a CNN, each layer has two kinds of parameters : weights and biases. The total number of parameters is just the sum of all weights and biases. Let's define, = Number of weights of the Conv Layer. = Number of biases of the Conv Layer. | |
C6767 | Dynamic Partition takes more time in loading data compared to static partition. When you have large data stored in a table then the Dynamic partition is suitable. If you want to partition a number of columns but you don't know how many columns then also dynamic partition is suitable. | |
C6768 | Convolution is a mathematical way of combining two signals to form a third signal. It is the single most important technique in Digital Signal Processing. Using the strategy of impulse decomposition, systems are described by a signal called the impulse response. | |
C6769 | To recap: The test statistic in a paired Wilcoxon signed-rank test (the V value) is the sum of the ranks of the pairwise differences x - y > 0 . Let's create some sample data to understand how V can be zero. We draw samples from two normal distributions with different means. | |
C6770 | To use the normal approximation method a minimum of 10 successes and 10 failures in each group are necessary (i.e., np≥10 n p ≥ 10 and n(1−p)≥10 n ( 1 − p ) ≥ 10 ). The null hypothesis is that there is not a difference between the two proportions (i.e., p1=p2 p 1 = p 2 ). | |
C6771 | Even if a model-fitting procedure has been used, R2 may still be negative, for example when linear regression is conducted without including an intercept, or when a non-linear function is used to fit the data. | |
C6772 | Skewness is a measure of symmetry, or more precisely, the lack of symmetry. Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. That is, data sets with high kurtosis tend to have heavy tails, or outliers. | |
C6773 | Thresholding is a technique in OpenCV, which is the assignment of pixel values in relation to the threshold value provided. In thresholding, each pixel value is compared with the threshold value. If the pixel value is smaller than the threshold, it is set to 0, otherwise, it is set to a maximum value (generally 255). | |
C6774 | This is when your model fits the training data well, but it isn't able to generalize and make accurate predictions for data it hasn't seen before. The training set is used to train the model, while the validation set is only used to evaluate the model's performance. | |
C6775 | The learning rate hyperparameter controls the rate or speed at which the model learns. A learning rate that is too small may never converge or may get stuck on a suboptimal solution. When the learning rate is too large, gradient descent can inadvertently increase rather than decrease the training error. | |
C6776 | This is the idea behind the use of pooling in convolutional neural networks. The pooling layer serves to progressively reduce the spatial size of the representation, to reduce the number of parameters, memory footprint and amount of computation in the network, and hence to also control overfitting. | |
C6777 | Examples of Artificial Intelligence: Work & School1 – Google's AI-Powered Predictions. 2 – Ridesharing Apps Like Uber and Lyft. 3 — Commercial Flights Use an AI Autopilot.1 – Spam Filters.2 – Smart Email Categorization.1 –Plagiarism Checkers. 2 –Robo-readers. 1 – Mobile Check Deposits.More items• | |
C6778 | A binomial distribution can be thought of as simply the probability of a SUCCESS or FAILURE outcome in an experiment or survey that is repeated multiple times. The binomial is a type of distribution that has two possible outcomes (the prefix “bi” means two, or twice). | |
C6779 | A moving average is a technique to get an overall idea of the trends in a data set; it is an average of any subset of numbers. The moving average is extremely useful for forecasting long-term trends. You can calculate it for any period of time. | |
C6780 | Conditional probability is the probability of one event occurring with some relationship to one or more other events. For example: Event A is that it is raining outside, and it has a 0.3 (30%) chance of raining today. Event B is that you will need to go outside, and that has a probability of 0.5 (50%). | |
C6781 | Univariate and multivariate represent two approaches to statistical analysis. Univariate involves the analysis of a single variable while multivariate analysis examines two or more variables. Most multivariate analysis involves a dependent variable and multiple independent variables. | |
C6782 | 0:008:06Suggested clip · 106 secondsSPSS - Correspondence Analysis - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C6783 | 0:0010:07Suggested clip · 109 secondsProbability Exponential Distribution Problems - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C6784 | Control Strategy in Artificial Intelligence scenario is a technique or strategy, tells us about which rule has to be applied next while searching for the solution of a problem within problem space. It helps us to decide which rule has to apply next without getting stuck at any point. | |
C6785 | Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. | |
C6786 | Dimensional analysis, or more specifically the factor-label method, also known as the unit-factor method, is a widely used technique for such conversions using the rules of algebra. The concept of physical dimension was introduced by Joseph Fourier in 1822. | |
C6787 | Each party in a dispute recognises that its own use of the concept is contested by those of other parties. To use an essentially contested concept means to use it against other users. To use such a concept means to use it aggresssively and defensively. | |
C6788 | Logistic regression models are a great tool for analysing binary and categorical data, allowing you to perform a contextual analysis to understand the relationships between the variables, test for differences, estimate effects, make predictions, and plan for future scenarios. | |
C6789 | Artificial intelligence can dramatically improve the efficiencies of our workplaces and can augment the work humans can do. When AI takes over repetitive or dangerous tasks, it frees up the human workforce to do work they are better equipped for—tasks that involve creativity and empathy among others. | |
C6790 | What is the F-distribution. A probability distribution, like the normal distribution, is means of determining the probability of a set of events occurring. This is true for the F-distribution as well. The F-distribution is a skewed distribution of probabilities similar to a chi-squared distribution. | |
C6791 | The values that divide each part are called the first, second, and third quartiles; and they are denoted by Q1, Q2, and Q3, respectively. Q1 is the "middle" value in the first half of the rank-ordered data set. Q2 is the median value in the set. Q3 is the "middle" value in the second half of the rank-ordered data set. | |
C6792 | The "Fast Fourier Transform" (FFT) is an important measurement method in the science of audio and acoustics measurement. It converts a signal into individual spectral components and thereby provides frequency information about the signal. | |
C6793 | Note that it is possible to get a negative R-square for equations that do not contain a constant term. Because R-square is defined as the proportion of variance explained by the fit, if the fit is actually worse than just fitting a horizontal line then R-square is negative. | |
C6794 | In this case, convergence in distribution implies convergence in probability. We can state the following theorem: Theorem If Xn d→ c, where c is a constant, then Xn p→ c. Since Xn d→ c, we conclude that for any ϵ>0, we have limn→∞FXn(c−ϵ)=0,limn→∞FXn(c+ϵ2)=1. | |
C6795 | Predictive analytics is the process of using data analytics to make predictions based on data. This process uses data along with analysis, statistics, and machine learning techniques to create a predictive model for forecasting future events. | |
C6796 | The Kruskal-Wallis H test (sometimes also called the "one-way ANOVA on ranks") is a rank-based nonparametric test that can be used to determine if there are statistically significant differences between two or more groups of an independent variable on a continuous or ordinal dependent variable. | |
C6797 | One of the newest and most effective ways to resolve the vanishing gradient problem is with residual neural networks, or ResNets (not to be confused with recurrent neural networks). ResNets refer to neural networks where skip connections or residual connections are part of the network architecture. | |
C6798 | The chain rule, or general product rule, calculates any component of the joint distribution of a set of random variables using only conditional probabilities. This probability theory is used as a foundation for backpropagation and in creating Bayesian networks. | |
C6799 | The n-1 equation is used in the common situation where you are analyzing a sample of data and wish to make more general conclusions. The SD computed this way (with n-1 in the denominator) is your best guess for the value of the SD in the overall population. |
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