_id stringlengths 2 6 | text stringlengths 3 395 | title stringclasses 1 value |
|---|---|---|
C5500 | Explanation: States of units be updated synchronously and asynchronously in hopfield model. Explanation: Asynchronous update ensures that the next state is at most unit hamming distance from current state. 5. If pattern is to be stored, then what does stable state should have updated value of? | |
C5501 | If they are independent and identically distributed (IID), then they must meet the first two criteria (since differing variances constitute non-identical distributions). However, IID data need not be normally distributed. Thus, whether or not a set of data is IID is unrelated to whether they are normal. | |
C5502 | For example, we can use a version of k-fold cross-validation that preserves the imbalanced class distribution in each fold. It is called stratified k-fold cross-validation and will enforce the class distribution in each split of the data to match the distribution in the complete training dataset. | |
C5503 | A low response rate can give rise to sampling bias if the nonresponse is unequal among the participants regarding exposure and/or outcome. For many years, a survey's response rate was viewed as an important indicator of survey quality. | |
C5504 | From the table we see that the probability of the observed data is maximized for θ=2. This means that the observed data is most likely to occur for θ=2. For this reason, we may choose ˆθ=2 as our estimate of θ. This is called the maximum likelihood estimate (MLE) of θ. | |
C5505 | Gradient Descent is the process of minimizing a function by following the gradients of the cost function. This involves knowing the form of the cost as well as the derivative so that from a given point you know the gradient and can move in that direction, e.g. downhill towards the minimum value. | |
C5506 | We often see patterns or relationships in scatterplots. When the y variable tends to increase as the x variable increases, we say there is a positive correlation between the variables. When the y variable tends to decrease as the x variable increases, we say there is a negative correlation between the variables. | |
C5507 | Pearson's correlation is utilized when you have two quantitative variables and you wish to see if there is a linear relationship between those variables. Your research hypothesis would represent that by stating that one score affects the other in a certain way. The correlation is affected by the size and sign of the r. | |
C5508 | ANN (Artificial Neural Networks) and SVM (Support Vector Machines) are two popular strategies for supervised machine learning and classification. SVMs don't suffer from either of these two problems. However, it's not readily apparent that SVMs are meant to be a total replacement for ANNs. | |
C5509 | The Gamma distribution is widely used in engineering, science, and business, to model continuous variables that are always positive and have skewed distributions. In SWedge, the Gamma distribution can be useful for any variable which is always positive, such as cohesion or shear strength for example. | |
C5510 | An activation function is a node that you add to the output layer or between two layers of any neural network. It is also known as the transfer function. It is used to determine the output of neural network layer in between 0 to 1 or -1 to 1 etc. | |
C5511 | Some of the most popular methods for outlier detection are: Z-Score or Extreme Value Analysis (parametric) Probabilistic and Statistical Modeling (parametric) Linear Regression Models (PCA, LMS) | |
C5512 | You can use an unsupervised learning algorithm (like clustering) to create your training data for the supervised learning algorithm but you cannot simply convert an unsupervised learning algorithm into a supervised one. | |
C5513 | An independent random variable is a random variable that doesn't have an effect on the other random variables in your experiment. In other words, it doesn't affect the probability of another event happening. | |
C5514 | Supervised learning algorithms are trained using labeled data. Unsupervised learning algorithms are trained using unlabeled data. In unsupervised learning, only input data is provided to the model. The goal of supervised learning is to train the model so that it can predict the output when it is given new data. | |
C5515 | You can diagnose the calibration of a classifier by creating a reliability diagram of the actual probabilities versus the predicted probabilities on a test set. In scikit-learn, this is called a calibration curve. The function returns the true probabilities for each bin and the predicted probabilities for each bin. | |
C5516 | There are seven significant steps in data preprocessing in Machine Learning:Acquire the dataset. Import all the crucial libraries. Import the dataset. Identifying and handling the missing values. Encoding the categorical data. Splitting the dataset. Feature scaling. | |
C5517 | No no need to standardize. Because by definition the correlation coefficient is independent of change of origin and scale. As such standardization will not alter the value of correlation. | |
C5518 | In probability theory, a probability density function (PDF), or density of a continuous random variable, is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the | |
C5519 | Sobel Filter. The Sobel filter is used for edge detection. It works by calculating the gradient of image intensity at each pixel within the image. It finds the direction of the largest increase from light to dark and the rate of change in that direction. | |
C5520 | Whereas GLS is more efficient than OLS under heteroscedasticity or autocorrelation, this is not true for FGLS. The feasible estimator is, provided the errors covariance matrix is consistently estimated, asymptotically more efficient, but for a small or medium size sample, it can be actually less efficient than OLS. | |
C5521 | Structured data is clearly defined and searchable types of data, while unstructured data is usually stored in its native format. Structured data is often stored in data warehouses, while unstructured data is stored in data lakes. | |
C5522 | The most common exponential and logarithm functions in a calculus course are the natural exponential function, ex , and the natural logarithm function, ln(x) . We will take a more general approach however and look at the general exponential and logarithm function. | |
C5523 | Word2vec is similar to an autoencoder, encoding each word in a vector, but rather than training against the input words through reconstruction, as a restricted Boltzmann machine does, word2vec trains words against other words that neighbor them in the input corpus. | |
C5524 | Conjoint analysis is a popular method of product and pricing research that uncovers consumers' preferences and uses that information to help select product features, assess sensitivity to price, forecast market shares, and predict adoption of new products or services. | |
C5525 | An ensemble of classifiers is a set of classifiers whose individual decisions are combined in some way (typically by weighted or unweighted voting) to classify new examples. One of the most active areas of research in supervised learning has been to study methods for constructing good ensembles of classifiers. | |
C5526 | The shape of any distribution can be described by its various 'moments'. The first four are: 1) The mean, which indicates the central tendency of a distribution. 2) The second moment is the variance, which indicates the width or deviation. | |
C5527 | AI means getting a computer to mimic human behavior in some way. Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems. | |
C5528 | Perceptron Learning Rule The Perceptron receives multiple input signals, and if the sum of the input signals exceeds a certain threshold, it either outputs a signal or does not return an output. In the context of supervised learning and classification, this can then be used to predict the class of a sample. | |
C5529 | In many applications including econometrics and biostatistics a fixed effects model refers to a regression model in which the group means are fixed (non-random) as opposed to a random effects model in which the group means are a random sample from a population. | |
C5530 | A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. A t-test is used as a hypothesis testing tool, which allows testing of an assumption applicable to a population. | |
C5531 | Filtering is a technique for modifying or enhancing an image. Linear filtering is filtering in which the value of an output pixel is a linear combination of the values of the pixels in the input pixel's neighborhood. This section discusses linear filtering in MATLAB and the Image Processing Toolbox. | |
C5532 | Building an NLP Pipeline, Step-by-StepStep 1: Sentence Segmentation. Step 2: Word Tokenization. Step 3: Predicting Parts of Speech for Each Token. Step 4: Text Lemmatization. Step 5: Identifying Stop Words. Step 6: Dependency Parsing. Step 6b: Finding Noun Phrases. Step 7: Named Entity Recognition (NER)More items | |
C5533 | The key to interpreting a hierarchical cluster analysis is to look at the point at which any given pair of cards “join together” in the tree diagram. Cards that join together sooner are more similar to each other than those that join together later. | |
C5534 | In statistics, bivariate data is data on each of two variables, where each value of one of the variables is paired with a value of the other variable. For example, bivariate data on a scatter plot could be used to study the relationship between stride length and length of legs. | |
C5535 | The Cauchy–Schwarz inequality gives the reason that the numerator is always less than or equal to the denominator. For other definitions of correlation (Spearman, Kendall, Kruskal & Goodman) it's because they're defined in such a manner to always fall between -1 and 1. | |
C5536 | Bayesian model comparison is a method of model selection based on Bayes factors. The models under consideration are statistical models. The aim of the Bayes factor is to quantify the support for a model over another, regardless of whether these models are correct. | |
C5537 | Code Generation for Image ProcessingWrite your MATLAB function or application as you would normally, using functions from the Image Processing Toolbox.Add the %#codegen compiler directive at the end of the function signature. Open the MATLAB Coder (MATLAB Coder) app, create a project, and add your file to the project.More items | |
C5538 | After getting to know your data through data summaries and visualizations, you might want to transform your variables further to make them more meaningful. This is known as feature processing. For example, say you have a variable that captures the date and time at which an event occurred. | |
C5539 | Regression Techniques Regression algorithms are machine learning techniques for predicting continuous numerical values. | |
C5540 | In machine learning, the vanishing gradient problem is encountered when training artificial neural networks with gradient-based learning methods and backpropagation. The problem is that in some cases, the gradient will be vanishingly small, effectively preventing the weight from changing its value. | |
C5541 | 0:168:55Suggested clip 108 secondsMode for a Continuous Random Variable | ExamSolutions - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C5542 | The geometric distribution would represent the number of people who you had to poll before you found someone who voted independent. You would need to get a certain number of failures before you got your first success. If you had to ask 3 people, then X=3; if you had to ask 4 people, then X=4 and so on. | |
C5543 | A scatterplot displays data about two variables as a set of points in the x y xy xy -plane and is a useful tool for determining if there is a correlation between the variables. Causation means that one event causes another event to occur. Causation can only be determined from an appropriately designed experiment. | |
C5544 | SummaryWeighted Mean: A mean where some values contribute more than others.When the weights add to 1: just multiply each weight by the matching value and sum it all up.Otherwise, multiply each weight w by its matching value x, sum that all up, and divide by the sum of weights: Weighted Mean = ΣwxΣw. | |
C5545 | only has one IP address. can only refer to one reusable-IP host at any given time, with one IP address, NAT can only provide general in-bound connectivity to one responder in the entire reusable-IP network at a time. | |
C5546 | A data set can also be presented by means of a data frequency table, a table in which each distinct value is listed in the first row and its frequency, which is the number of times the value appears in the data set, is listed below it in the second row. | |
C5547 | 1.2 Semantic or Associative Networks Concepts are represented as nodes with labeled links (e.g., IS-A or Part-of) as relationships among the nodes. Based on the idea that activation can spread from one node to another, semantic networks have been quite influential in the development of models of memory. | |
C5548 | Differences Between Population Variance and Sample Variance When calculating sample variance, n is the number of sample points (vs N for population size in the formula above). Unlike the population variance, the sample variance is simply a statistic of the sample. | |
C5549 | The movie and its events are happening about a hundred years after this war. The entire Human race is used for power supply. Their bodies are asleep and their minds are plugged into the Matrix. The Matrix is a virtual world that has been pulled over their minds to hide them from the truth – they are slaves now. | |
C5550 | Regression: This is a tool used to evaluate the relationship of a dependent variable in relation to multiple independent variables. A regression will analyze the mean of the dependent variable in relation to changes in the independent variables. Time Series: A time series measures data over a specific period of time. | |
C5551 | When I calculate population variance, I then divide the sum of squared deviations from the mean by the number of items in the population (in example 1 I was dividing by 12). When I calculate sample variance, I divide it by the number of items in the sample less one. In our example 2, I divide by 99 (100 less 1). | |
C5552 | In machine learning, instance-based learning (sometimes called memory-based learning) is a family of learning algorithms that, instead of performing explicit generalization, compares new problem instances with instances seen in training, which have been stored in memory. | |
C5553 | If there are c or less defective items in the sample, the lot is accepted. If there are more than c defective items in the sample, the lot is rejected. In other words, the acceptance or rejection of the lot depends on the inspection results of a single sample. | |
C5554 | A multilayer perceptron (MLP) is a class of feedforward artificial neural network (ANN). MLP utilizes a supervised learning technique called backpropagation for training. Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable. | |
C5555 | In conclusion neural nets can learn the min function easily if either constrained within the positive or negative interval, and less easily if the interval includes both. | |
C5556 | Two-sample t-test is used when the data of two samples are statistically independent, while the paired t-test is used when data is in the form of matched pairs. To use the two-sample t-test, we need to assume that the data from both samples are normally distributed and they have the same variances. | |
C5557 | Classification algorithms are supervised learning methods to split data into classes. They can work on Linear Data as well as Nonlinear Data. Logistic Regression can classify data based on weighted parameters and sigmoid conversion to calculate the probability of classes. | |
C5558 | Prewitt operator is similar to the Sobel operator and is used for detecting vertical and horizontal edges in images. However, unlike the Sobel, this operator does not place any emphasis on the pixels that are closer to the center of the mask. | |
C5559 | The Wilcoxon signed-rank test is a non-parametric statistical hypothesis test used to compare two related samples, matched samples, or repeated measurements on a single sample to assess whether their population mean ranks differ (i.e. it is a paired difference test). | |
C5560 | Predictive analytics requires a data-driven culture: 5 steps to startDefine the business result you want to achieve. Collect relevant data from all available sources. Improve the quality of data using data cleaning techniques. Choose predictive analytics solutions or build your own models to test the data.More items• | |
C5561 | The number of outcomes in non-overlapping intervals are independent. The probability of two or more outcomes in a sufficiently short interval is virtually zero. The probability of exactly one outcome in a sufficiently short interval or small region is proportional to the length of the interval or region. | |
C5562 | Purpose of a Model. Models are representations that can aid in defining, analyzing, and communicating a set of concepts. System models are specifically developed to support analysis, specification, design, verification, and validation of a system, as well as to communicate certain information. | |
C5563 | Absolute Positioning You can use two values top and left along with the position property to move an HTML element anywhere in the HTML document. Move Left - Use a negative value for left. Move Right - Use a positive value for left. Move Up - Use a negative value for top. | |
C5564 | The Fourier Transform is an important image processing tool which is used to decompose an image into its sine and cosine components. The output of the transformation represents the image in the Fourier or frequency domain, while the input image is the spatial domain equivalent. | |
C5565 | One of the most important aspects of convenience sampling is its cost effectiveness. This method allows for funds to be distributed to other aspects of the project. Oftentimes this method of sampling is used to gain funding for a larger, more thorough research project. | |
C5566 | Principal Component Analysis, or PCA, is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set. | |
C5567 | Logistic regression is one of the statistical techniques in machine learning used to form prediction models. In short, Logistic Regression is used when the dependent variable(target) is categorical. | |
C5568 | 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. | |
C5569 | How to Deal with MulticollinearityRedesign the study to avoid multicollinearity. Increase sample size. Remove one or more of the highly-correlated independent variables. Define a new variable equal to a linear combination of the highly-correlated variables. | |
C5570 | Recurrent neural networks (RNN) are the state of the art algorithm for sequential data and are used by Apple's Siri and and Google's voice search. It is the first algorithm that remembers its input, due to an internal memory, which makes it perfectly suited for machine learning problems that involve sequential data. | |
C5571 | In the development of the probability function for a discrete random variable, two conditions must be satisfied: (1) f(x) must be nonnegative for each value of the random variable, and (2) the sum of the probabilities for each value of the random variable must equal one. | |
C5572 | Fundamentally, classification is about predicting a label and regression is about predicting a quantity. That classification is the problem of predicting a discrete class label output for an example. That regression is the problem of predicting a continuous quantity output for an example. | |
C5573 | Non-probability sampling is a sampling technique where the odds of any member being selected for a sample cannot be calculated. In addition, probability sampling involves random selection, while non-probability sampling does not—it relies on the subjective judgement of the researcher. | |
C5574 | Nevertheless, the same has been delineated briefly below:Step 1: Visualize the Time Series. It is essential to analyze the trends prior to building any kind of time series model. Step 2: Stationarize the Series. Step 3: Find Optimal Parameters. Step 4: Build ARIMA Model. Step 5: Make Predictions. | |
C5575 | In general, prediction is the process of determining the magnitude of statistical variates at some future point of time. | |
C5576 | The normal distribution is a probability function that describes how the values of a variable are distributed. It is a symmetric distribution where most of the observations cluster around the central peak and the probabilities for values further away from the mean taper off equally in both directions. | |
C5577 | K-means clustering is one of the simplest and popular unsupervised machine learning algorithms. To achieve this objective, K-means looks for a fixed number (k) of clusters in a dataset.” A cluster refers to a collection of data points aggregated together because of certain similarities. | |
C5578 | Type I and type II errors are instrumental for the understanding of hypothesis testing in a clinical research scenario. A type II error can be thought of as the opposite of a type I error and is when a researcher fails to reject the null hypothesis that is actually false in reality. | |
C5579 | The distinction between probability and likelihood is fundamentally important: Probability attaches to possible results; likelihood attaches to hypotheses. Explaining this distinction is the purpose of this first column. Possible results are mutually exclusive and exhaustive. | |
C5580 | Adaptive artificial neural networks are a class of networks used in dynamic environments. They are characterized by online learning. A number of techniques are used to provide adaptability to neural networks: adaptation by weight modification, by neuronal property modification, and by network structure modification. | |
C5581 | The Kalman filter produces an estimate of the state of the system as an average of the system's predicted state and of the new measurement using a weighted average. The purpose of the weights is that values with better (i.e., smaller) estimated uncertainty are "trusted" more. | |
C5582 | The sample variance is an estimator for the population variance. When applied to sample data, the population variance formula is a biased estimator of the population variance: it tends to underestimate the amount of variability. We are using one fitted value (sample mean) in our estimate of the variance. | |
C5583 | A hypergeometric experiment is a statistical experiment with the following properties: You take samples from two groups. You are concerned with a group of interest, called the first group. You sample without replacement from the combined groups. Each pick is not independent, since sampling is without replacement. | |
C5584 | In scikit-learn we can use the CalibratedClassifierCV class to create well calibrated predicted probabilities using k-fold cross-validation. In CalibratedClassifierCV the training sets are used to train the model and the test sets is used to calibrate the predicted probabilities. | |
C5585 | The distribution defined by the density function in (1) is known as the negative binomial distribution ; it has two parameters, the stopping parameter k and the success probability p. In the negative binomial experiment, vary k and p with the scroll bars and note the shape of the density function. | |
C5586 | 0:133:01Suggested clip · 119 secondsLearn everything about probability in 3 mins! - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C5587 | Once you have calculated the decimal values of each percentage for each given sample size, you then add these decimal values together and divide the total number by the total sum of both sample sizes. You then need to multiply this value by 100 to get the average percentage.5 days ago | |
C5588 | AI or artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning and self-correction. Some of the applications of AI include expert systems, speech recognition and machine vision. | |
C5589 | The target variable of a dataset is the feature of a dataset about which you want to gain a deeper understanding. A supervised machine learning algorithm uses historical data to learn patterns and uncover relationships between other features of your dataset and the target. | |
C5590 | A couple of ways of trying to decrease zig-zagging is to use larger batches during training, as well as trying to make the train/val split more consistent. | |
C5591 | Convolution is a general purpose filter effect for images. □ Is a matrix applied to an image and a mathematical operation. comprised of integers. □ It works by determining the value of a central pixel by adding the. weighted values of all its neighbors together. | |
C5592 | Truncated Backpropagation Through Time (truncated BPTT) is a widespread method for learning recurrent computational graphs. Truncated BPTT keeps the computational benefits of Backpropagation Through Time (BPTT) while relieving the need for a complete backtrack through the whole data sequence at every step. | |
C5593 | The regression (or regressive) fallacy is an informal fallacy. It assumes that something has returned to normal because of corrective actions taken while it was abnormal. This fails to account for natural fluctuations. It is frequently a special kind of the post hoc fallacy. | |
C5594 | Again, feature selection keeps a subset of the original features while feature extraction creates new ones. As with feature selection, some algorithms already have built-in feature extraction. As a stand-alone task, feature extraction can be unsupervised (i.e. PCA) or supervised (i.e. LDA). | |
C5595 | A feature vector is just a vector that contains information describing an object's important characteristics. In image processing, features can take many forms. A simple feature representation of an image is the raw intensity value of each pixel. However, more complicated feature representations are also possible. | |
C5596 | The correlation coefficient is a statistical measure of the strength of the relationship between the relative movements of two variables. The values range between -1.0 and 1.0. A correlation of -1.0 shows a perfect negative correlation, while a correlation of 1.0 shows a perfect positive correlation. | |
C5597 | Events A and B are independent if: knowing whether A occured does not change the probability of B. Mathematically, can say in tw. Page 1. Events A and B are independent if: knowing whether A occured does not change the probability of B. | |
C5598 | When there is lack of domain understanding for feature introspection , Deep Learning techniques outshines others as you have to worry less about feature engineering . Deep Learning really shines when it comes to complex problems such as image classification, natural language processing, and speech recognition. | |
C5599 | A continuous random variable is not defined at specific values. 1: The curve has no negative values (p(x) > 0 for all x) 2: The total area under the curve is equal to 1. A curve meeting these requirements is known as a density curve. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.