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C1600 | Understanding the differences Detection refers to mining insights or information in a data pool when it is being processed. Prediction or predictive analysis employs probability based on the data analyses and processing. | |
C1601 | The trace is sometimes called the spur, from the German word Spur, which means track or trace. For example, the trace of the n by n identity matrix is equal to n. A matrix in which all the elements below the diagonal elements vanish is called an upper triangular matrix. | |
C1602 | An iterative algorithm is said to converge when as the iterations proceed the output gets closer and closer to a specific value. In some circumstances, an algorithm will diverge; its output will undergo larger and larger oscillations, never approaching a useful result. | |
C1603 | Two random variables X and Y are said to be bivariate normal, or jointly normal, if aX+bY has a normal distribution for all a,b∈R. In the above definition, if we let a=b=0, then aX+bY=0. We agree that the constant zero is a normal random variable with mean and variance 0. | |
C1604 | A major difference is in its shape: the normal distribution is symmetrical, whereas the lognormal distribution is not. Because the values in a lognormal distribution are positive, they create a right-skewed curve. A further distinction is that the values used to derive a lognormal distribution are normally distributed. | |
C1605 | Deep learning is an artificial intelligence (AI) function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Also known as deep neural learning or deep neural network. | |
C1606 | The “reinforcement” in reinforcement learning refers to how certain behaviors are encouraged, and others discouraged. Behaviors are reinforced through rewards which are gained through experiences with the environment. Reinforcement learning borrowed his name from the first thread of studies. | |
C1607 | So far we've looked at GBMs that use two different direction vectors, the residual vector (Gradient boosting: Distance to target) and the sign vector (Gradient boosting: Heading in the right direction). | |
C1608 | AI can signal posts of people who might be in need and/or perhaps driven by suicidal tendencies. The AI uses machine learning to flag key phrases in posts and concerned comments from friends or family members to help identify users who may be at risk. | |
C1609 | The methods of signal processing include time domain, frequency domain, and complex frequency domain. | |
C1610 | Feature extraction involves reducing the number of resources required to describe a large set of data.GeneralIndependent component analysis.Isomap.Kernel PCA.Latent semantic analysis.Partial least squares.Principal component analysis.Multifactor dimensionality reduction.Nonlinear dimensionality reduction.More items | |
C1611 | Stratified sampling offers several advantages over simple random sampling. A stratified sample can provide greater precision than a simple random sample of the same size. Because it provides greater precision, a stratified sample often requires a smaller sample, which saves money. | |
C1612 | 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. | |
C1613 | To get α subtract your confidence level from 1. For example, if you want to be 95 percent confident that your analysis is correct, the alpha level would be 1 – . 95 = 5 percent, assuming you had a one tailed test. For two-tailed tests, divide the alpha level by 2. | |
C1614 | MinMax scaling will not affect the values of dummy variables but Standardised scaling will. | |
C1615 | A generative model includes the distribution of the data itself, and tells you how likely a given example is. For example, models that predict the next word in a sequence are typically generative models (usually much simpler than GANs) because they can assign a probability to a sequence of words. | |
C1616 | The normalisation ensures that the inputs have a mean of 0 and a standard deviation of 1, meaning that the input distribution to every neuron will be the same, thereby fixing the problem of internal covariate shift and providing regularisation. | |
C1617 | A data stream is a set of extracted information from a data provider. It contains raw data that was gathered out of users' browser behavior from websites, where a dedicated pixel is placed. | |
C1618 | A Convolutional Neural Networks Introduction so to speak.Step 1: Convolution Operation. Step 1(b): ReLU Layer. Step 2: Pooling. Step 3: Flattening. Step 4: Full Connection. Step 1 - Convolution Operation. Step 1(b): The Rectified Linear Unit (ReLU) Step 2 - Max Pooling.More items• | |
C1619 | 1 Natural Language Processing. Computational linguistics (CL), natural language processing (NLP) and machine translation (MT) are domains whose perspective on natural language is different from that of linguistic fields such as semantics, pragmatics and syntax. | |
C1620 | From Example 20.2, the posterior distribution of P is Beta(s+α, n−s+α). The posterior mean is then (s+α)/(n+2α), and the posterior mode is (s+α−1)/(n+2α−2). Both of these may be taken as a point estimate p for p. | |
C1621 | The trace of a matrix is the sum of its (complex) eigenvalues, and it is invariant with respect to a change of basis. This characterization can be used to define the trace of a linear operator in general. The trace is only defined for a square matrix (n × n). | |
C1622 | If each member actively seeks out knowledge and information, and feels empowered to share it, they can open up a collaborative discussion. This cognitive mechanism enables individuals to share views, ideas and attitudes when focusing on issues together, something that cannot be replicated by individual attention. | |
C1623 | Technically, all interpreters do the same thing and follow the same basic principles. But since sign languages are visual-manual while spoken languages are based on speaking, hearing and writing/reading, the difference entails several special requirements for interpreting. | |
C1624 | The P-value is the probability that a chi-square statistic having 2 degrees of freedom is more extreme than 19.58. We use the Chi-Square Distribution Calculator to find P(Χ2 > 19.58) = 0.0001. | |
C1625 | Whereas R-squared is a relative measure of fit, RMSE is an absolute measure of fit. As the square root of a variance, RMSE can be interpreted as the standard deviation of the unexplained variance, and has the useful property of being in the same units as the response variable. Lower values of RMSE indicate better fit. | |
C1626 | The data used in calculating a chi-square statistic must be random, raw, mutually exclusive, drawn from independent variables, and drawn from a large enough sample. Chi-square tests are often used in hypothesis testing. | |
C1627 | A lazy learner delays abstracting from the data until it is asked to make a prediction while an eager learner abstracts away from the data during training and uses this abstraction to make predictions rather than directly compare queries with instances in the dataset. | |
C1628 | Explanation of Collaborative Filtering vs Content Based Filtering. Recommender systems help users select similar items when something is being chosen online. The method is based on content and collaborative filtering approach that captures correlation between user preferences and item features. | |
C1629 | Abstract: The Local Binary Pattern Histogram(LBPH) algorithm is a simple solution on face recognition problem, which can recognize both front face and side face. To solve this problem, a modified LBPH algorithm based on pixel neighborhood gray median(MLBPH) is proposed. | |
C1630 | Box plots divide the data into sections that each contain approximately 25% of the data in that set. Box plots are useful as they provide a visual summary of the data enabling researchers to quickly identify mean values, the dispersion of the data set, and signs of skewness. | |
C1631 | Ordinary least squares assumes things like equal variance of the noise at every x location. Generalized least squares does not assume a diagonal co-variance matrix. | |
C1632 | In statistics and machine learning, lasso (least absolute shrinkage and selection operator; also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces. | |
C1633 | Linear regression is the next step up after correlation. It is used when we want to predict the value of a variable based on the value of another variable. The variable we want to predict is called the dependent variable (or sometimes, the outcome variable). | |
C1634 | Hebbian learning is one of the oldest learning algorithms, and is based in large part on the dynamics of biological systems. A synapse between two neurons is strengthened when the neurons on either side of the synapse (input and output) have highly correlated outputs. | |
C1635 | Random error can be caused by numerous things, such as inconsistencies or imprecision in equipment used to measure data, in experimenter measurements, in individual differences between participants who are being measured, or in experimental procedures. | |
C1636 | Random error can be reduced by: Using an average measurement from a set of measurements, or. Increasing sample size. | |
C1637 | Yes, it is possible but not in the near future. We are nowhere close to building an AI like JARVIS. It would take decades of research. | |
C1638 | Random Forests / Ensemble Trees. One approach to dimensionality reduction is to generate a large and carefully constructed set of trees against a target attribute and then use each attribute's usage statistics to find the most informative subset of features. | |
C1639 | The F ratio is the ratio of two mean square values. If the null hypothesis is true, you expect F to have a value close to 1.0 most of the time. The P value is determined from the F ratio and the two values for degrees of freedom shown in the ANOVA table. | |
C1640 | Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date. In some cases the data used to predict the variable of interest is itself forecast. | |
C1641 | Ordinary least squares (OLS) regression is a statistical method of analysis that estimates the relationship between one or more independent variables and a dependent variable; the method estimates the relationship by minimizing the sum of the squares in the difference between the observed and predicted values of the | |
C1642 | If you want to ingest DynamoDB data into Redshift you have a few options.The Redshift Copy command.Build a Data Pipeline that copies the data using an EMR job to S3.Export the DynamoDB data to a file using the AWS CLI and load the flat file into Redshift.More items | |
C1643 | A sample standard deviation is a statistic. This means that it is calculated from only some of the individuals in a population. Since the sample standard deviation depends upon the sample, it has greater variability. Thus the standard deviation of the sample is greater than that of the population. | |
C1644 | Artificial neural networks are forecasting methods that are based on simple mathematical models of the brain. They allow complex nonlinear relationships between the response variable and its predictors. | |
C1645 | Key Terms. normal approximation: The process of using the normal curve to estimate the shape of the distribution of a data set. central limit theorem: The theorem that states: If the sum of independent identically distributed random variables has a finite variance, then it will be (approximately) normally distributed. | |
C1646 | Batch normalisation is a technique for improving the performance and stability of neural networks, and also makes more sophisticated deep learning architectures work in practice (like DCGANs). That means we can think of any layer in a neural network as the first layer of a smaller subsequent network. | |
C1647 | 1 Answer. In word2vec, you train to find word vectors and then run similarity queries between words. In doc2vec, you tag your text and you also get tag vectors. If two authors generally use the same words then their vector will be closer. | |
C1648 | Others tried to use deep learning to solve problems that were beyond its scope. But according to famous data scientist and deep learning researcher Jeremy Howard, the “deep learning is overhyped” argument is a bit— well—overhyped. | |
C1649 | An F-test is any statistical test in which the test statistic has an F-distribution under the null hypothesis. It is most often used when comparing statistical models that have been fitted to a data set, in order to identify the model that best fits the population from which the data were sampled. | |
C1650 | Bayesian Model Averaging (BMA) is an application of Bayesian inference to the problems of model selection, combined estimation and prediction that produces a straightforward model choice criteria and less risky predictions. | |
C1651 | Time series analysis can be useful to see how a given asset, security, or economic variable changes over time. It can also be used to examine how the changes associated with the chosen data point compare to shifts in other variables over the same time period. | |
C1652 | Like random forests, gradient boosting is a set of decision trees. The two main differences are: Combining results: random forests combine results at the end of the process (by averaging or "majority rules") while gradient boosting combines results along the way. | |
C1653 | Inverse reinforcement learning is the problem of inferring the reward function of an observed agent, given its policy or behavior. Researchers perceive IRL both as a problem and as a class of methods. | |
C1654 | As the area of a bar represents the frequency of its interval, the height of the bar represents the density. If you label the scare it is either frequency per unit or, if you divide by the total frequency, relative frequency per unit. | |
C1655 | It is common to allocate 50 percent or more of the data to the training set, 25 percent to the test set, and the remainder to the validation set. Some training sets may contain only a few hundred observations; others may include millions. | |
C1656 | The Absolute min, is the smallest function value of the domain of the function, whereas, the Local min at point c, is the smallest function value where x is near c. A function is a local minimum at x=c, if f(c) > or = to f(x), for all x values near c ) some interval containing c). | |
C1657 | How k-means cluster analysis worksStep 1: Specify the number of clusters (k). Step 2: Allocate objects to clusters. Step 3: Compute cluster means. Step 4: Allocate each observation to the closest cluster center. Step 5: Repeat steps 3 and 4 until the solution converges. | |
C1658 | One-shot learning is a classification task where one, or a few, examples are used to classify many new examples in the future. One-shot learning are classification tasks where many predictions are required given one (or a few) examples of each class, and face recognition is an example of one-shot learning. | |
C1659 | PVQ is an acronym for Pressure Vessel Quality. That means any steel plate using this designation is designed for use in pressure vessels. These pressure vessels are normally some type of closed container meant to hold any gas or liquid that is held at a pressure much different than its surrounding ambient pressure. | |
C1660 | Linear algebra is usually taken by sophomore math majors after they finish their calculus classes, but you don't need a lot of calculus in order to do it. | |
C1661 | 18:2725:32Suggested clip · 115 secondsStructural Equation Modeling: what is it and what can we use it for YouTubeStart of suggested clipEnd of suggested clip | |
C1662 | The major difference between a traditional Artificial Neural Network (ANN) and CNN is that only the last layer of a CNN is fully connected whereas in ANN, each neuron is connected to every other neurons as shown in Fig. | |
C1663 | Translational Invariance makes the CNN invariant to translation. Invariance to translation means that if we translate the inputs the CNN will still be able to detect the class to which the input belongs. Translational Invariance is a result of the pooling operation. | |
C1664 | Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. | |
C1665 | Now we'll check out the proven way to improve the performance(Speed and Accuracy both) of neural network models:Increase hidden Layers. Change Activation function. Change Activation function in Output layer. Increase number of neurons. Weight initialization. More data. Normalizing/Scaling data.More items• | |
C1666 | This term is used in statistics in its ordinary sense, but most frequently occurs in connection with samples from different populations which may or may not be identical. If the populations are identical they are said to be homogeneous, and by extension, the sample data are also said to be homogeneous. | |
C1667 | Coding theory is one of the most important and direct applications of information theory. Using a statistical description for data, information theory quantifies the number of bits needed to describe the data, which is the information entropy of the source. | |
C1668 | The basic steps to build an image classification model using a neural network are:Flatten the input image dimensions to 1D (width pixels x height pixels)Normalize the image pixel values (divide by 255)One-Hot Encode the categorical column.Build a model architecture (Sequential) with Dense layers.More items• | |
C1669 | In general, in deep learning NNs are quite large and so the number of (local) minima is much larger than in simple cases of NNs. TL;DR: In general they are non convex. | |
C1670 | They are different types of clustering methods, including:Partitioning methods.Hierarchical clustering.Fuzzy clustering.Density-based clustering.Model-based clustering. | |
C1671 | If we use a generalized linear model (GLM) to model the relationship, deviance is a measure of goodness of fit: the smaller the deviance, the better the fit. The exact definition of deviance is as follows: for a particular GLM (denoted ), let denote the maximum achievable likelihood under this model. | |
C1672 | Association rules are "if-then" statements, that help to show the probability of relationships between data items, within large data sets in various types of databases. | |
C1673 | Synapses are the couplings between neurons, allowing signals to pass from one neuron to another. However, synapses are much more than mere relays: they play an important role in neural computation. | |
C1674 | A recurrent neural network, however, is able to remember those characters because of its internal memory. It produces output, copies that output and loops it back into the network. Simply put: recurrent neural networks add the immediate past to the present. | |
C1675 | A number used to multiply a variable. Example: 6z means 6 times z, and "z" is a variable, so 6 is a coefficient. Variables with no number have a coefficient of 1. Example: x is really 1x. Sometimes a letter stands in for the number. | |
C1676 | Definition: To give a helpful lift up to someone, either physically or emotionally. This phrasal verb means to lift someone up to reach a higher point. This can be physically, if someone cannot reach something, or emotionally, if someone needs a boost, or increase, in confidence or morale. | |
C1677 | From Wikipedia, the free encyclopedia. In statistics and, in particular, in the fitting of linear or logistic regression models, the elastic net is a regularized regression method that linearly combines the L1 and L2 penalties of the lasso and ridge methods. | |
C1678 | Probability of Two Events Occurring Together: Independent Just multiply the probability of the first event by the second. For example, if the probability of event A is 2/9 and the probability of event B is 3/9 then the probability of both events happening at the same time is (2/9)*(3/9) = 6/81 = 2/27. | |
C1679 | A non-stationary process with a deterministic trend becomes stationary after removing the trend, or detrending. For example, Yt = α + βt + εt is transformed into a stationary process by subtracting the trend βt: Yt - βt = α + εt, as shown in the figure below. | |
C1680 | Prerequisite for Machine LearningStatistics, Calculus, Linear Algebra and Probability. A) Statistics contain tools that are used to get an outcome from data. Programming Knowledge. Being able to write code is one of the most important things when it comes to Machine Learning. Data Modeling. | |
C1681 | The AUC value lies between 0.5 to 1 where 0.5 denotes a bad classifer and 1 denotes an excellent classifier. | |
C1682 | It can be seen that the function of the loss of quality is a U-shaped curve, which is determined by the following simple quadratic function: L(x)= Quality loss function. x = Value of the quality characteristic (observed). N = Nominal value of the quality characteristic (Target value – target). | |
C1683 | Negative Log-Likelihood (NLL) Recall that when training a model, we aspire to find the minima of a loss function given a set of parameters (in a neural network, these are the weights and biases). We can interpret the loss as the “unhappiness” of the network with respect to its parameters. | |
C1684 | Backpropagation (backward propagation) is an important mathematical tool for improving the accuracy of predictions in data mining and machine learning. Artificial neural networks use backpropagation as a learning algorithm to compute a gradient descent with respect to weights. | |
C1685 | 1. Evaluate ARIMA ModelSplit the dataset into training and test sets.Walk the time steps in the test dataset. Train an ARIMA model. Make a one-step prediction. Store prediction; get and store actual observation.Calculate error score for predictions compared to expected values. | |
C1686 | ReLU provides just enough non-linearity so that it is nearly as simple as a linear activation, but this non-linearity opens the door for extremely complex representations. Because unlike in the linear case, the more you stack non-linear ReLUs, the more it becomes non-linear. | |
C1687 | Bootstrap aggregating, also called bagging (from bootstrap aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. | |
C1688 | parameter-list is the list of parameters that the function takes separated by commas. If no parameters are given, then the function does not take any and should be defined with an empty set of parenthesis or with the keyword void. If no variable type is in front of a variable in the paramater list, then int is assumed. | |
C1689 | Weights are the co-efficients of the equation which you are trying to resolve. Negative weights reduce the value of an output. When a neural network is trained on the training set, it is initialised with a set of weights. A neuron first computes the weighted sum of the inputs. | |
C1690 | Random variables are denoted by capital letters If you see a lowercase x or y, that's the kind of variable you're used to in algebra. It refers to an unknown quantity or quantities. If you see an uppercase X or Y, that's a random variable and it usually refers to the probability of getting a certain outcome. | |
C1691 | Error -- subtract the theoretical value (usually the number the professor has as the target value) from your experimental data point. Percent error -- take the absolute value of the error divided by the theoretical value, then multiply by 100. | |
C1692 | How to Find a Sample Size Given a Confidence Interval and Width (unknown population standard deviation)za/2: Divide the confidence interval by two, and look that area up in the z-table: .95 / 2 = 0.475. E (margin of error): Divide the given width by 2. 6% / 2. : use the given percentage. 41% = 0.41. : subtract. from 1. | |
C1693 | Unsupervised feature learning is learning features from unlabeled data. The goal of unsupervised feature learning is often to discover low-dimensional features that captures some structure underlying the high-dimensional input data. | |
C1694 | As the area of a bar represents the frequency of its interval, the height of the bar represents the density. If you label the scare it is either frequency per unit or, if you divide by the total frequency, relative frequency per unit. | |
C1695 | Definition. In probability theory, a normalizing constant is a constant by which an everywhere non-negative function must be multiplied so the area under its graph is 1, e.g., to make it a probability density function or a probability mass function. | |
C1696 | Statistical analysis is used in order to gain an understanding of a larger population by analysing the information of a sample. Data analysis is the process of inspecting, presenting and reporting data in a way that is useful to non-technical people. | |
C1697 | LDA is a probabilistic generative model that extracts the thematic structure in a big document collection. The model assumes that every topic is a distribution of words in the vocabulary, and every document (described over the same vocabulary) is a distribution of a small subset of these topics. | |
C1698 | Additional terms will always improve the model whether the new term adds significant value to the model or not. As a matter of fact, adding new variables can actually make the model worse. Adding more and more variables makes it more and more likely that you will overfit your model to the training data. | |
C1699 | Covariance indicates the relationship of two variables whenever one variable changes. If an increase in one variable results in an increase in the other variable, both variables are said to have a positive covariance. Both variables move together in the same direction when they change. |
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