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C2600 | The collaborative filtering algorithm uses “User Behavior” for recommending items. This is one of the most commonly used algorithms in the industry as it is not dependent on any additional information. | |
C2601 | StepsStep 1: For each (x,y) point calculate x2 and xy.Step 2: Sum all x, y, x2 and xy, which gives us Σx, Σy, Σx2 and Σxy (Σ means "sum up")Step 3: Calculate Slope m:m = N Σ(xy) − Σx Σy N Σ(x2) − (Σx)2Step 4: Calculate Intercept b:b = Σy − m Σx N.Step 5: Assemble the equation of a line. | |
C2602 | 5 industries that are using Artificial Intelligence the mostHealthcare. This is one area that tops the list when it comes to the extent of AI application. Education. Those days are long gone when parent-teacher meetings used to happen in haste without many insights. Marketing. Retail and E-commerce. Financial markets and services. | |
C2603 | Backtracking is a technique based on algorithm to solve problem. It uses recursive calling to find the solution by building a solution step by step increasing values with time. It removes the solutions that doesn't give rise to the solution of the problem based on the constraints given to solve the problem. | |
C2604 | Dimensionality reduction refers to techniques for reducing the number of input variables in training data. When dealing with high dimensional data, it is often useful to reduce the dimensionality by projecting the data to a lower dimensional subspace which captures the “essence” of the data. | |
C2605 | Markov model is a state machine with the state changes being probabilities. In a hidden Markov model, you don't know the probabilities, but you know the outcomes. | |
C2606 | jobs. deep learning performs better when sequential processing is used. | |
C2607 | K-nearest neighbor (KNN) decision boundary K-nearest neighbor is an algorithm based on the local geometry of the distribution of the data on the feature hyperplane (and their relative distance measures). The decision boundary, therefore, comes up as nonlinear and non-smooth. | |
C2608 | Receptive fields are defined portion of space or spatial construct containing units that provide input to a set of units within a corresponding layer. The receptive field is defined by the filter size of a layer within a convolution neural network. | |
C2609 | Ambiguity. The main challenge of NLP is the understanding and modeling of elements within a variable context. In a natural language, words are unique but can have different meanings depending on the context resulting in ambiguity on the lexical, syntactic, and semantic levels. | |
C2610 | In linear regression, coefficients are the values that multiply the predictor values. The sign of each coefficient indicates the direction of the relationship between a predictor variable and the response variable. A positive sign indicates that as the predictor variable increases, the response variable also increases. | |
C2611 | Implementing Deep Q-Learning using TensorflowPrerequisites: Deep Q-Learning.Step 1: Importing the required libraries.Step 2: Building the Environment.Step 3: Building the learning agent.Step 4: Finding the Optimal Strategy.The agent tries different methods to reach the top and thus gaining knowledge from each episode.Step 5: Testing the Learning Agent.More items• | |
C2612 | A decision tree is built on an entire dataset, using all the features/variables of interest, whereas a random forest randomly selects observations/rows and specific features/variables to build multiple decision trees from and then averages the results. | |
C2613 | Hidden Layers, which are neuron nodes stacked in between inputs and outputs, allowing neural networks to learn more complicated features (such as XOR logic) | |
C2614 | Metric learning aims to measure the similarity among samples while using an optimal distance metric for learning tasks. In recent years, deep metric learning, which provides a better solution for nonlinear data through activation functions, has attracted researchers' attention in many different areas. | |
C2615 | From the network operations perspective, streaming telemetry can improve efficiency in many use cases, including: Detecting problems by setting up network monitors and alerts based on pre-configured thresholds or network performance baselines. Troubleshooting connectivity and performance issues. | |
C2616 | The bag-of-words model is a simplifying representation used in natural language processing and information retrieval (IR). In this model, a text (such as a sentence or a document) is represented as the bag (multiset) of its words, disregarding grammar and even word order but keeping multiplicity. | |
C2617 | The skip-gram model. Both the input vector x and the output y are one-hot encoded word representations. The hidden layer is the word embedding of size N. | |
C2618 | 1. Agglomerative approach: This method is also called a bottom-up approach shown in Figure 6.7. In this method, each node represents a single cluster at the beginning; eventually, nodes start merging based on their similarities and all nodes belong to the same cluster. | |
C2619 | One way to par- allelize neural network training is to use a technique called Network Parallel Training (NPT). In this approach the neu- rons of the ANN are divided across machines in the cluster, so that each machine holds a portion of the neural network. | |
C2620 | Process: In the process of Artificial Intelligence (AI), Future events are forecasted using the predictive model. But Data Science involves the process of prediction, visualization, analysis, and pre-processing of data. But the primary goal of Data Science is to find the patterns that are hidden in the data. | |
C2621 | With two-way ANOVA, you have one continuous dependent variable and two categorical grouping variables for the independent variables. MANOVA models several dependent variables simultaneously and you can include a variety of independent variables. | |
C2622 | A ratio scale is a quantitative scale where there is a true zero and equal intervals between neighboring points. Unlike on an interval scale, a zero on a ratio scale means there is a total absence of the variable you are measuring. Length, area, and population are examples of ratio scales. | |
C2623 | The basic assumption of factor analysis is that for a collection of observed variables there are a set of underlying variables called factors (smaller than the observed variables), that can explain the interrelationships among those variables. | |
C2624 | For a good regression model, you want to include the variables that you are specifically testing along with other variables that affect the response in order to avoid biased results. Cross-validation determines how well your model generalizes to other data sets by partitioning your data. | |
C2625 | A latent variable is a variable that is inferred using models from observed data. Approaches to inferring latent variables from data include: using a single observed variable, multi-item scales, predictive models, dimension reduction techniques such as factor analysis, structural equation models, and mixture models. | |
C2626 | To help you get started in the field, we've assembled a list of the best Big Data courses available.Simplilearn. Simplilearn's Big Data Course catalogue is known for their large number of courses, in subjects as varied as Hadoop, SAS, Apache Spark, and R. Cloudera. Big Data University. Hortonworks. Coursera. | |
C2627 | Training deep learning neural networks is very challenging. The best general algorithm known for solving this problem is stochastic gradient descent, where model weights are updated each iteration using the backpropagation of error algorithm. Optimization in general is an extremely difficult task. | |
C2628 | The lognormal distribution is a probability distribution whose logarithm has a normal distribution. The mean m and variance v of a lognormal random variable are functions of the lognormal distribution parameters µ and σ: m = exp ( μ + σ 2 / 2 ) v = exp ( 2 μ + σ 2 ) ( exp ( σ 2 ) − 1 ) | |
C2629 | Informally, a neural attention mechanism equips a neural network with the ability to focus on a subset of its inputs (or features): it selects specific inputs. | |
C2630 | Joint prediction Crucially, Bayesian networks can also be used to predict the joint probability over multiple outputs (discrete and or continuous). This is useful when it is not enough to predict two variables separately, whether using separate models or even when they are in the same model. | |
C2631 | So, we have listed some of the ways where you can achieve trade-off between the two. Both bias and variance are related to each other, if you increase one the other decreases and vice versa. By a trade-off, there is an optimal balance in the bias and variance which gives us a model that is neither underfit nor overfit. | |
C2632 | There are two big reasons why you want homoscedasticity: While heteroscedasticity does not cause bias in the coefficient estimates, it does make them less precise. Lower precision increases the likelihood that the coefficient estimates are further from the correct population value. | |
C2633 | The false discovery rate (FDR) is a method of conceptualizing the rate of type I errors in null hypothesis testing when conducting multiple comparisons. Thus, FDR-controlling procedures have greater power, at the cost of increased numbers of Type I errors. | |
C2634 | Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data. The test provides evidence concerning the plausibility of the hypothesis, given the data. Statistical analysts test a hypothesis by measuring and examining a random sample of the population being analyzed. | |
C2635 | Machine learning can be automated when it involves the same activity again and again. However, the fundamental nature of machine learning deals with the opposite: variable conditions. In this regard, machine learning needs to be able to function independently and with different solutions to match different demands. | |
C2636 | Gradient Descent with Momentum considers the past gradients to smooth out the update. It computes an exponentially weighted average of your gradients, and then use that gradient to update your weights instead. It works faster than the standard gradient descent algorithm. | |
C2637 | An autonomous agent is an intelligent agent operating on an owner's behalf but without any interference of that ownership entity. Non-biological examples include intelligent agents, autonomous robots, and various software agents, including artificial life agents, and many computer viruses. | |
C2638 | Bootstrap Aggregating is an ensemble method. First, we create random samples of the training data set with replacment (sub sets of training data set). Then, we build a model (classifier or Decision tree) for each sample. Finally, results of these multiple models are combined using average or majority voting. | |
C2639 | Based on these definitions, EBPH and EBM differ in the following three ways: First, EBM focuses on individual patients, whereas EBPH focuses on community and residents [13]. Second, the EBM intervention is disease treatment, whereas the EBPH intervention is disease prevention and health promotion [14]. | |
C2640 | In general, you should probably use the divergence theorem whenever you wish to evaluate a vector surface integral over a closed surface. The divergence theorem can also be used to evaluate triple integrals by turning them into surface integrals. | |
C2641 | Cluster analysis is the task of grouping a set of data points in such a way that they can be characterized by their relevancy to one another. These types are Centroid Clustering, Density Clustering Distribution Clustering, and Connectivity Clustering. | |
C2642 | Using P values and Significance Levels Together If your P value is less than or equal to your alpha level, reject the null hypothesis. The P value results are consistent with our graphical representation. The P value of 0.03112 is significant at the alpha level of 0.05 but not 0.01. | |
C2643 | We input the data in the learning algorithm as a set of inputs, which is called as Features, denoted by X along with the corresponding outputs, which is indicated by Y, and the algorithm learns by comparing its actual production with correct outputs to find errors. It then modifies the model accordingly. | |
C2644 | Evolution is not a random process. The genetic variation on which natural selection acts may occur randomly, but natural selection itself is not random at all. The survival and reproductive success of an individual is directly related to the ways its inherited traits function in the context of its local environment. | |
C2645 | The various metrics used to evaluate the results of the prediction are :Mean Squared Error(MSE)Root-Mean-Squared-Error(RMSE).Mean-Absolute-Error(MAE).R² or Coefficient of Determination.Adjusted R² | |
C2646 | To plot the probability density function for a log normal distribution in R, we can use the following functions: dlnorm(x, meanlog = 0, sdlog = 1) to create the probability density function. curve(function, from = NULL, to = NULL) to plot the probability density function. | |
C2647 | The shape of the t distribution depends on the degrees of freedom (df) that went into the estimate of the standard deviation. With very few degrees of freedom, the t distribution is very leptokurtic. With 100 or more degrees of freedom, the t distribution is almost indistinguishable from the normal distribution. | |
C2648 | As the formula shows, the standard score is simply the score, minus the mean score, divided by the standard deviation. | |
C2649 | In statistics, the mode is the most commonly observed value in a set of data. For the normal distribution, the mode is also the same value as the mean and median. In many cases, the modal value will differ from the average value in the data. | |
C2650 | Both tests relate the mean difference to the variance (variability of measurements) (and to the sample size). The z-test assumes that the variance is known, whereas the t-test does not make this assumption. Usually one does not know the variance, so one needs to estimate it from the available data. | |
C2651 | There are two straightforward ways to solve the optimal control problem: (1) the method of Lagrange multipliers and (2) dynamic programming. We have already outlined the idea behind the Lagrange multipliers approach. The second way, dynamic programming, solves the constrained problem directly. | |
C2652 | Assumptions. The assumptions of discriminant analysis are the same as those for MANOVA. The analysis is quite sensitive to outliers and the size of the smallest group must be larger than the number of predictor variables. Multivariate normality: Independent variables are normal for each level of the grouping variable. | |
C2653 | Whereas AI is preprogrammed to carry out a task that a human can but more efficiently, artificial general intelligence (AGI) expects the machine to be just as smart as a human. This is the kind of AI we're used to seeing in blockbuster movies. | |
C2654 | A positive test result indicates that a person has inherited a known harmful mutation in BRCA1 or BRCA2 and, therefore, has an increased risk of developing certain cancers. However, a positive test result cannot tell whether or when an individual will actually develop cancer. | |
C2655 | "The Gini coefficient provides an index to measure inequality," says Antonio Cabrales, a professor of economics at University College London. It is a way of comparing how distribution of income in a society compares with a similar society in which everyone earned exactly the same amount. | |
C2656 | Another way to look at the difference is that a p-value of 0.05 implies that 5% of all tests will result in false positives. An FDR adjusted p-value (or q-value) of 0.05 implies that 5% of significant tests will result in false positives. The latter will result in fewer false positives. | |
C2657 | When Longitudinal data looks like a time series is when we measure the same thing over time. The big difference is that in a time series we can measure the overall change in the measurement over time (or by group) while in a longitudinal analysis you actually have the measurement of change at the individual level. | |
C2658 | Negentropy is reverse entropy. It means things becoming more in order. By 'order' is meant organisation, structure and function: the opposite of randomness or chaos. One example of negentropy is a star system such as the Solar System. The opposite of entropy is negentropy. | |
C2659 | 2:1812:46Suggested clip 110 secondsStructural Equation Modeling with SPSS AMOS PART1: by G N YouTubeStart of suggested clipEnd of suggested clip | |
C2660 | Canonical discriminant analysis is a dimension-reduction technique related to principal component analysis and canonical correlation. This maximal multiple correlation is called the first canonical correlation. The coefficients of the linear combination are the canonical coefficients or canonical weights. | |
C2661 | Adaptive learning systems are designed to dynamically adjust to the level or type of course content based on an individual student's abilities or skill attainment, in ways that accelerate a learner's performance with both automated and instructor interventions. | |
C2662 | A support vector machine is a machine learning model that is able to generalise between two different classes if the set of labelled data is provided in the training set to the algorithm. The main function of the SVM is to check for that hyperplane that is able to distinguish between the two classes. | |
C2663 | Given a character sequence and a defined document unit, tokenization is the task of chopping it up into pieces, called tokens , perhaps at the same time throwing away certain characters, such as punctuation. | |
C2664 | This term is used in two different senses; one related to multi-stage sampling and the other to multi-phase sampling. In multi-stage sampling the process of selecting sample units, say, at the second stage from any selected first stage unit is called subsampling of the first stage unit. | |
C2665 | Preparing Your Dataset for Machine Learning: 8 Basic Techniques That Make Your Data BetterArticulate the problem early.Establish data collection mechanisms.Format data to make it consistent.Reduce data.Complete data cleaning.Decompose data.Rescale data.Discretize data. | |
C2666 | The Area Under the Curve (AUC) is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve. The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes. | |
C2667 | A machine learning pipeline is used to help automate machine learning workflows. They operate by enabling a sequence of data to be transformed and correlated together in a model that can be tested and evaluated to achieve an outcome, whether positive or negative. | |
C2668 | In probability theory and statistics, a collection of random variables is independent and identically distributed if each random variable has the same probability distribution as the others and all are mutually independent. This property is usually abbreviated as i.i.d. or iid or IID. | |
C2669 | Moderation distinguishes between the roles of the two variables involved in the interaction. They are both considered predictor variables. The interaction tells us that the effect of X on Y is different at different values of Z. It also tells us that the effect of Z on Y is different at different values of X. | |
C2670 | Multinomial logistic regression does have assumptions, such as the assumption of independence among the dependent variable choices. This assumption states that the choice of or membership in one category is not related to the choice or membership of another category (i.e., the dependent variable). | |
C2671 | Text classification also known as text tagging or text categorization is the process of categorizing text into organized groups. By using Natural Language Processing (NLP), text classifiers can automatically analyze text and then assign a set of pre-defined tags or categories based on its content. | |
C2672 | Categorical variables require special attention in regression analysis because, unlike dichotomous or continuous variables, they cannot by entered into the regression equation just as they are. Instead, they need to be recoded into a series of variables which can then be entered into the regression model. | |
C2673 | In statistics and regression analysis, moderation occurs when the relationship between two variables depends on a third variable. The third variable is referred to as the moderator variable or simply the moderator. | |
C2674 | The most common functional form is parametric linear model, as a type of parametric regression, is frequently used to describe the relationship between a dependent variable and explanatory variables. Parametric linear models require the estimation of a finite number of parameters, β. | |
C2675 | The relative frequencies add up to 1. | |
C2676 | For our objective function, which measures the quality of a clustering, we use the sum of the squared error (SSE), which is also known as scatter. In other words, we calculate the error of each data point, i.e., its Euclidean distance to the closest centroid, and then compute the total sum of the squared errors. | |
C2677 | A probability distribution is a statistical function that describes all the possible values and likelihoods that a random variable can take within a given range. | |
C2678 | Any object, function, or statistic that doesn't change when scales are multiplied by a common factor is scale invariant. In statistics, it can also mean a statistic that tends not to change (i.e. 99% of the time, it will stay the same). Some specific statistics are scale invariant. | |
C2679 | Statistical inference is the process through which inferences about a population are made based on certain statistics calculated from a sample of data drawn from that population. | |
C2680 | Definition: Union of Events. The union of events A and B, denoted A∪B, is the collection of all outcomes that are elements of one or the other of the sets A and B, or of both of them. | |
C2681 | 7.2. Radial basis function (RBF) networks are a commonly used type of artificial neural network for function approximation problems. Radial basis function networks are distinguished from other neural networks due to their universal approximation and faster learning speed. | |
C2682 | Statistically significant means a result is unlikely due to chance. The p-value is the probability of obtaining the difference we saw from a sample (or a larger one) if there really isn't a difference for all users. Statistical significance doesn't mean practical significance. | |
C2683 | Because neural networks work internally with numeric data, binary data (such as sex, which can be male or female) and categorical data (such as a community, which can be suburban, city or rural) must be encoded in numeric form. | |
C2684 | The difference between these two statistical measurements is that correlation measures the degree of a relationship between two variables (x and y), whereas regression is how one variable affects another. | |
C2685 | A mixture of two normal distributions with equal standard deviations is bimodal only if their means differ by at least twice the common standard deviation. Estimates of the parameters is simplified if the variances can be assumed to be equal (the homoscedastic case). | |
C2686 | His later short story "The Last Question", however, expands the AC suffix to be "analog computer". In possibly the most famous Multivac story, "The Last Question", two slightly drunken technicians ask Multivac if humanity can reverse the increase of entropy. | |
C2687 | Partial least squares regression (PLS regression) is a statistical method that bears some relation to principal components regression; instead of finding hyperplanes of maximum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the | |
C2688 | Gradient clipping is a technique to prevent exploding gradients in very deep networks, usually in recurrent neural networks. This prevents any gradient to have norm greater than the threshold and thus the gradients are clipped. | |
C2689 | No. If the learning rate is too high, then the model can diverge. If the validation error consistently goes up, that means the model could be diverging because of high learning rate. | |
C2690 | It is based on a mixture of deep autoencoders where each cluster is represented by an autoencoder. A clustering network transforms the data into another space and then selects one of the clusters. Next, the autoencoder associated with this cluster is used to reconstruct the data-point. | |
C2691 | Deep learning is getting a lot of hype right now, but neural networks aren't the answer to everything.Disadvantages of Neural NetworksBlack Box. Duration of Development. Amount of Data. Computationally Expensive. | |
C2692 | Nonparametric tests are sometimes called distribution-free tests because they are based on fewer assumptions (e.g., they do not assume that the outcome is approximately normally distributed). There are several statistical tests that can be used to assess whether data are likely from a normal distribution. | |
C2693 | The hypergeometric distribution has the following properties: The mean of the distribution is equal to n * k / N . The variance is n * k * ( N - k ) * ( N - n ) / [ N2 * ( N - 1 ) ] . | |
C2694 | 1 Answer. Transfer learning is when a model developed for one task is reused to work on a second task. Fine tuning is one approach to transfer learning. | |
C2695 | The reason for preferring L2 norm is that it corresponds to Hilbert space . | |
C2696 | Can it solve any problem that a person would solve by thinking? Are human intelligence and machine intelligence the same? | |
C2697 | the condition or quality of being true, correct, or exact; freedom from error or defect; precision or exactness; correctness. Chemistry, Physics. the extent to which a given measurement agrees with the standard value for that measurement. Compare precision (def. 6). | |
C2698 | Image SegmentationNon-contextual thresholding. Simple thresholding. Adaptive thresholding. Colour thresholding.Contextual segmentation: Region growing. Pixel connectivity. Region similarity. Region growing. Split-and-merge segmentation.Texture segmentation: Spectral features.References. | |
C2699 | 3:237:22Suggested clip · 117 secondsCategorical Regression Model - YouTubeYouTubeStart of suggested clipEnd of suggested clip |
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