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C600 | Solutions to this are to decrease your network size, or to increase dropout. For example you could try dropout of 0.5 and so on. If your training/validation loss are about equal then your model is underfitting. Increase the size of your model (either number of layers or the raw number of neurons per layer) | |
C601 | Under the batch processing model, a set of data is collected over time, then fed into an analytics system. In other words, you collect a batch of information, then send it in for processing. Under the streaming model, data is fed into analytics tools piece-by-piece. The processing is usually done in real time. | |
C602 | 4. An implementation of Reinforcement LearningInitialize the Values table 'Q(s, a)'.Observe the current state 's'.Choose an action 'a' for that state based on one of the action selection policies (eg. Take the action, and observe the reward 'r' as well as the new state 's'.More items• | |
C603 | 7 Techniques to Handle Imbalanced DataUse the right evaluation metrics. Resample the training set. Use K-fold Cross-Validation in the right way. Ensemble different resampled datasets. Resample with different ratios. Cluster the abundant class. Design your own models. | |
C604 | Pooled data occur when we have a “time series of cross sections,” but the observations in each cross section do not necessarily refer to the same unit. Panel data refers to samples of the same cross-sectional units observed at multiple points in time. | |
C605 | The hazard function (also called the force of mortality, instantaneous failure rate, instantaneous death rate, or age-specific failure rate) is a way to model data distribution in survival analysis. The function is defined as the instantaneous risk that the event of interest happens, within a very narrow time frame. | |
C606 | The most common hash functions used in digital forensics are Message Digest 5 (MD5), and Secure Hashing Algorithm (SHA) 1 and 2. | |
C607 | From Wikipedia, the free encyclopedia. In computational linguistics and computer science, edit distance is a way of quantifying how dissimilar two strings (e.g., words) are to one another by counting the minimum number of operations required to transform one string into the other. | |
C608 | Positive Skewness means when the tail on the right side of the distribution is longer or fatter. The mean and median will be greater than the mode. Negative Skewness is when the tail of the left side of the distribution is longer or fatter than the tail on the right side. The mean and median will be less than the mode. | |
C609 | It's a method of evaluating how well specific algorithm models the given data. If predictions deviates too much from actual results, loss function would cough up a very large number. Gradually, with the help of some optimization function, loss function learns to reduce the error in prediction. | |
C610 | These are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. | |
C611 | Probabilities for the two diceTotalNumber of combinationsProbability6513.89%7616.67%8513.89%9411.11%8 more rows | |
C612 | As regards the normality of group data, the one-way ANOVA can tolerate data that is non-normal (skewed or kurtotic distributions) with only a small effect on the Type I error rate. However, platykurtosis can have a profound effect when your group sizes are small. | |
C613 | Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. | |
C614 | Null and alternate hypothesis are different and you can't interchange them. Alternate hypothesis is just the opposite of null which means there is a statistical difference in Mean / median of both the data sets. | |
C615 | The expected value for a random variable, X, from a Bernoulli distribution is: E[X] = p. For example, if p = . 04, then E[X] = 0.4. | |
C616 | One of the most widely used predictive analytics models, the forecast model deals in metric value prediction, estimating numeric value for new data based on learnings from historical data. This model can be applied wherever historical numerical data is available. | |
C617 | The mean, expected value, or expectation of a random variable X is writ- ten as E(X) or µX. If we observe N random values of X, then the mean of the N values will be approximately equal to E(X) for large N. The expectation is defined differently for continuous and discrete random variables. | |
C618 | Standard deviation measures the spread of a data distribution. It measures the typical distance between each data point and the mean. The formula we use for standard deviation depends on whether the data is being considered a population of its own, or the data is a sample representing a larger population. | |
C619 | Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others. It is also called ascertainment bias in medical fields. Sampling bias limits the generalizability of findings because it is a threat to external validity, specifically population validity. | |
C620 | When we have a high degree linear polynomial that is used to fit a set of points in a linear regression setup, to prevent overfitting, we use regularization, and we include a lambda parameter in the cost function. This lambda is then used to update the theta parameters in the gradient descent algorithm. | |
C621 | Definition of artificial intelligence AI is the ability of a machine to display human-like capabilities such as reasoning, learning, planning and creativity. AI enables technical systems to perceive their environment, deal with what they perceive, solve problems and act to achieve a specific goal. | |
C622 | The bootstrap method is a resampling technique used to estimate statistics on a population by sampling a dataset with replacement. It can be used to estimate summary statistics such as the mean or standard deviation. | |
C623 | The averaged height is just one number now. Sample distribution: Just the distribution of the data from the sample. Sampling distribution: The distribution of a statistic from several samples. | |
C624 | The homunculus argument is a fallacy whereby a concept is explained in terms of the concept itself, recursively, without first defining or explaining the original concept. The obvious answer is that there is another homunculus inside the first homunculus's "head" or "brain" looking at this "movie". | |
C625 | The general application of the matrix norm is the derivative form of finding proof in terms of interplay and tandem of vectorial normalized formats to whom are extended.. It can be used in tandem with Graphical processing, image processing, all kinds of algorithmics in terms of calculations and derivatives.. | |
C626 | Data is the currency of applied machine learning. Resampling is a methodology of economically using a data sample to improve the accuracy and quantify the uncertainty of a population parameter. Resampling methods, in fact, make use of a nested resampling method. | |
C627 | You do need distributional assumptions about the response variable in order to make inferences (e.g, confidence intervals), but it is not necessary that the response variable be normallhy distributed. | |
C628 | Naive Bayes works best when you have small training data set, relatively small features(dimensions). If you have huge feature list, the model may not give you accuracy, because the likelihood would be distributed and may not follow the Gaussian or other distribution. | |
C629 | You take the sum of the squares of the terms in the distribution, and divide by the number of terms in the distribution (N). From this, you subtract the square of the mean (μ2). It's a lot less work to calculate the standard deviation this way. | |
C630 | Some of the main drawbacks of association rule algorithms in e-learning are: the used algorithms have too many parameters for somebody non expert in data mining and the obtained rules are far too many, most of them non-interesting and with low comprehensibility. | |
C631 | 3:1532:58Suggested clip · 96 secondsHow To Deploy TensorFlow Models On Mobile Platforms - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C632 | The common wisdom is, Interpolation is likely to be more accurate than extrapolation. And the further you extrapolate from your data, the more inaccurate your predictions are likely to be. The closer you are to a known data point, the more accurate your estimate is likely to be. | |
C633 | 7 Best Models for Image Classification using Keras1 Xception. It translates to “Extreme Inception”. 2 VGG16 and VGG19: This is a keras model with 16 and 19 layer network that has an input size of 224X224. 3 ResNet50. The ResNet architecture is another pre-trained model highly useful in Residual Neural Networks. 4 InceptionV3. 5 DenseNet. 6 MobileNet. 7 NASNet. | |
C634 | Stepwise Selection Stepwise regression is a modification of the forward selection so that after each step in which a variable was added, all candidate variables in the model are checked to see if their significance has been reduced below the specified tolerance level. | |
C635 | “Critical" values of z are associated with interesting central areas under the standard normal curve. In other words, there is an 80% probability that any normal variable will fall within 1.28 standard deviations of its mean. So we say that 1.28 is the critical value of z that corresponds to a central area of 0.80. | |
C636 | When examining the distribution of a quantitative variable, one should describe the overall pattern of the data (shape, center, spread), and any deviations from the pattern (outliers). | |
C637 | At a higher level, the chief difference between the L1 and the L2 terms is that the L2 term is proportional to the square of the β values, while the L1 norm is proportional the absolute value of the values in β. | |
C638 | Multivariate Normality–Multiple regression assumes that the residuals are normally distributed. No Multicollinearity—Multiple regression assumes that the independent variables are not highly correlated with each other. This assumption is tested using Variance Inflation Factor (VIF) values. | |
C639 | Center: The center is not affected by sample size. The mean of the sample means is always approximately the same as the population mean µ = 3,500. Spread: The spread is smaller for larger samples, so the standard deviation of the sample means decreases as sample size increases. | |
C640 | Time series analysis is a statistical technique that deals with time series data, or trend analysis. Time series data means that data is in a series of particular time periods or intervals. Time series data: A set of observations on the values that a variable takes at different times. | |
C641 | 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. | |
C642 | A support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text. So you're working on a text classification problem. | |
C643 | The main difference between cluster sampling and stratified sampling is that in cluster sampling the cluster is treated as the sampling unit so sampling is done on a population of clusters (at least in the first stage). In stratified sampling, the sampling is done on elements within each stratum. | |
C644 | Data for two variables (usually two types of related data). Example: Ice cream sales versus the temperature on that day. The two variables are Ice Cream Sales and Temperature. | |
C645 | 1 Posterior is compromise of prior and likelihood. For a prior distribution expressed as beta(θ|a,b), the prior mean of θ is a/(a + b). Suppose we observe z heads in N flips, which is a proportion of z/N heads in the data. The posterior mean is (z + a)/[(z + a) + (N ‒ z + b)] = (z + a)/(N + a + b). | |
C646 | Distance MatrixThe proximity between object can be measured as distance matrix. For example, distance between object A = (1, 1) and B = (1.5, 1.5) is computed as.Another example of distance between object D = (3, 4) and F = (3, 3.5) is calculated as.More items | |
C647 | On a broad level, we can differentiate both AI and ML as: AI is a bigger concept to create intelligent machines that can simulate human thinking capability and behavior, whereas, machine learning is an application or subset of AI that allows machines to learn from data without being programmed explicitly. | |
C648 | The label "moving average" is technically incorrect since the MA coefficients may be negative and may not sum to unity. This label is used by convention. The name "moving average" is somewhat misleading because the weights 1,−θ1,−θ2,…,−θq, which multiply the a's, need not total unity nor need that be positive. | |
C649 | The central limit theorem applies to almost all types of probability distributions, but there are exceptions. For example, the population must have a finite variance. That restriction rules out the Cauchy distribution because it has infinite variance. | |
C650 | First of all, you don't need to normalise your inputs until one/more of the inputs start to dominate others - which is the fundamental reason behind normalization/standardization. | |
C651 | 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• | |
C652 | A frequency distribution is a representation, either in a graphical or tabular format, that displays the number of observations within a given interval. The interval size depends on the data being analyzed and the goals of the analyst. The intervals must be mutually exclusive and exhaustive. | |
C653 | Logistic regression analysis is used to examine the association of (categorical or continuous) independent variable(s) with one dichotomous dependent variable. This is in contrast to linear regression analysis in which the dependent variable is a continuous variable. | |
C654 | It is the process of transforming a categorical variable into a continuous variable and using them in the model. Lets start with basic and go to advanced methods. One Hot Encoding & Label Encoding. | |
C655 | Calculating Standard Error of the MeanFirst, take the square of the difference between each data point and the sample mean, finding the sum of those values.Then, divide that sum by the sample size minus one, which is the variance.Finally, take the square root of the variance to get the SD. | |
C656 | Bias allows you to shift the activation function by adding a constant (i.e. the given bias) to the input. Bias in Neural Networks can be thought of as analogous to the role of a constant in a linear function, whereby the line is effectively transposed by the constant value. | |
C657 | Clustering is a Machine Learning technique that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. | |
C658 | The finite frequency theory of probability defines the probability of an outcome as the frequency of the number of times the outcome occurs relative to the number of times that it could have occured. This is defined as the limiting frequency with which that outcome appears in a long series of similar events. | |
C659 | This is the reference consumption model where every infrastructure component (ML platform, algorithms, compute, and data) is deployed and managed by the user. The user builds, trains, and deploys ML models. The user is also responsible for installing and managing all components of the developer environment. | |
C660 | Events A and B are independent if the equation P(A∩B) = P(A) · P(B) holds true. You can use the equation to check if events are independent; multiply the probabilities of the two events together to see if they equal the probability of them both happening together. | |
C661 | Start by looking at the left side of your degrees of freedom and find your variance. Then, go upward to see the p-values. Compare the p-value to the significance level or rather, the alpha. Remember that a p-value less than 0.05 is considered statistically significant. | |
C662 | Principal component analysis is a dimensionality reduction method. Canonical correlation analysis, on the other hand, is a method for comparing draws from two different multivariate distributions. | |
C663 | Unlike R-squared, you can use the standard error of the regression to assess the precision of the predictions. Approximately 95% of the observations should fall within plus/minus 2*standard error of the regression from the regression line, which is also a quick approximation of a 95% prediction interval. | |
C664 | Feature extraction describes the relevant shape information contained in a pattern so that the task of classifying the pattern is made easy by a formal procedure. In pattern recognition and in image processing, feature extraction is a special form of dimensionality reduction. | |
C665 | In statistics, a generalized linear mixed model (GLMM) is an extension to the generalized linear model (GLM) in which the linear predictor contains random effects in addition to the usual fixed effects. They also inherit from GLMs the idea of extending linear mixed models to non-normal data. | |
C666 | The fundamental assumption of statistical mechanics is that, over time, an isolated system in a given macrostate is equally likely to be found in any of it's microstates. Thus, our system of 2 atoms is most likely to be in a microstate where energy is split up 50/50. | |
C667 | Another sign of overfitting may be seen in the classification accuracy on the training data, If the training accuracy is out performing our test accuracy, it means that our model is learning details and noises of training data and specifically working of training data. Overfitting is a major problem in neural networks. | |
C668 | How to conduct a multivariate testIdentify a problem. Formulate a hypothesis. Create variations. Determine your sample size. Test your tools. Start driving traffic. Analyze your results. Learn from your results. | |
C669 | For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms can perform inference and learning in Bayesian networks. | |
C670 | 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. | |
C671 | Quality Glossary Definition: Reliability. Reliability is defined as the probability that a product, system, or service will perform its intended function adequately for a specified period of time, or will operate in a defined environment without failure. | |
C672 | Andrew Ng says that batch normalization should be applied immediately before the non-linearity of the current layer. The authors of the BN paper said that as well, but now according to François Chollet on the keras thread, the BN paper authors use BN after the activation layer. | |
C673 | Using multiple features from multiple filters improve the performance of the network. Other than that, there is another fact that makes the inception architecture better than others. All the architectures prior to inception, performed convolution on the spatial and channel wise domain together. | |
C674 | Clustering or cluster analysis is an unsupervised learning problem. It is often used as a data analysis technique for discovering interesting patterns in data, such as groups of customers based on their behavior. There are many clustering algorithms to choose from and no single best clustering algorithm for all cases. | |
C675 | Vue provides higher customizability and hence is easier to learn than Angular or React. Further, Vue has an overlap with Angular and React with respect to their functionality like the use of components. Hence, the transition to Vue from either of the two is an easy option. | |
C676 | In mathematics, the inequality of arithmetic and geometric means, or more briefly the AM–GM inequality, states that the arithmetic mean of a list of non-negative real numbers is greater than or equal to the geometric mean of the same list; and further, that the two means are equal if and only if every number in the | |
C677 | is that numerical is of or pertaining to numbers while nonnumerical is not numerical; containing data other than numbers. | |
C678 | To calculate the centroid from the cluster table just get the position of all points of a single cluster, sum them up and divide by the number of points. | |
C679 | Inter-Rater Reliability MethodsCount the number of ratings in agreement. In the above table, that's 3.Count the total number of ratings. For this example, that's 5.Divide the total by the number in agreement to get a fraction: 3/5.Convert to a percentage: 3/5 = 60%. | |
C680 | Created by the Google Brain team, TensorFlow is an open source library for numerical computation and large-scale machine learning. TensorFlow bundles together a slew of machine learning and deep learning (aka neural networking) models and algorithms and makes them useful by way of a common metaphor. | |
C681 | Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x). | |
C682 | Recurrent Neural Networks (RNNs) are a form of machine learning algorithm that are ideal for sequential data such as text, time series, financial data, speech, audio, video among others. | |
C683 | Q-learning is called off-policy because the updated policy is different from the behavior policy, so Q-Learning is off-policy. In other words, it estimates the reward for future actions and appends a value to the new state without actually following any greedy policy. | |
C684 | In signal processing, a nonlinear (or non-linear) filter is a filter whose output is not a linear function of its input. Like linear filters, nonlinear filters may be shift invariant or not. Non-linear filters have many applications, especially in the removal of certain types of noise that are not additive. | |
C685 | Variance and standard deviation (Square root of variance) is useful in any control system. But it is used more often than variance because the unit in which it is measured is the same as that of mean, a measure of central tendency. Variance is measured in square of the units whereas standard deviation in just units. | |
C686 | Mean Absolute Error (MAE) The MAE is a simple way to measure error magnitude. It consists on the average of the absolute differences between the predictions and the observed values. Th measure goes from 0 to infinite, being 0 the best value you can get. | |
C687 | "A discrete variable is one that can take on finitely many, or countably infinitely many values", whereas a continuous random variable is one that is not discrete, i.e. "can take on uncountably infinitely many values", such as a spectrum of real numbers. | |
C688 | Examples of semi-structured data include JSON and XML are forms of semi-structured data. The reason that this third category exists (between structured and unstructured data) is because semi-structured data is considerably easier to analyse than unstructured data. | |
C689 | 0:008:33Suggested clip · 112 secondsHow to read a log scale. - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C690 | A "single-layer" perceptron can't implement XOR. The reason is because the classes in XOR are not linearly separable. You cannot draw a straight line to separate the points (0,0),(1,1) from the points (0,1),(1,0). Led to invention of multi-layer networks. | |
C691 | Batch normalization is a technique for training very deep neural networks that standardizes the inputs to a layer for each mini-batch. This has the effect of stabilizing the learning process and dramatically reducing the number of training epochs required to train deep networks. | |
C692 | 0:5328:17Suggested clip 66 secondsCalculus 2 - Integral Test For Convergence and Divergence of SeriesYouTubeStart of suggested clipEnd of suggested clip | |
C693 | 0:1110:28المقطع المقترح · 110 ثانيةLambda Measure of Association for Two Nominal Variables in SPSS YouTubeبداية المقطع المقترَحنهاية المقطع المقترَح | |
C694 | Word2Vec slightly customizes the process and calls it negative sampling. In Word2Vec, the words for the negative samples (used for the corrupted pairs) are drawn from a specially designed distribution, which favours less frequent words to be drawn more often. | |
C695 | Common examples of algorithms with coefficients that can be optimized using gradient descent are Linear Regression and Logistic Regression. | |
C696 | 6.5. 9 Artificial Neural Network, Supervised Learning. A supervised learning is a type of machine learning algorithm that uses a known dataset this is known as training dataset, and it is used to make predictions of other datasets. The dataset includes two types of information: input data and response values. | |
C697 | It is a classification technique based on Bayes' Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. | |
C698 | Both are data reduction techniques—they allow you to capture the variance in variables in a smaller set. Despite all these similarities, there is a fundamental difference between them: PCA is a linear combination of variables; Factor Analysis is a measurement model of a latent variable. | |
C699 | 2:268:01Suggested clip · 91 secondsIntroduction to Univariate Analysis - YouTubeYouTubeStart of suggested clipEnd of suggested clip |
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