_id stringlengths 2 6 | text stringlengths 3 395 | title stringclasses 1 value |
|---|---|---|
C1000 | Singularity enables users to have full control of their environment. Singularity containers can be used to package entire scientific workflows, software and libraries, and even data. The Singularity software can import your Docker images without having Docker installed or being a superuser. | |
C1001 | In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. | |
C1002 | 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. | |
C1003 | 0:082:33Suggested clip · 117 secondsHistogram Finding Frequency - Corbettmaths - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C1004 | The ability to detect and adapt to changes in the distribution of examples is paramount for data stream mining algorithms. The shift in the underlying distribution of examples arriving from a data stream is referred to as concept drift. Concept drift occurs over time and the rate at which the drifts occurs varies. | |
C1005 | 2:4411:47Suggested clip · 92 secondsHow to Design a Convolutional Neural Network | Lecture 8 - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C1006 | Agricultural scientists often use linear regression to measure the effect of fertilizer and water on crop yields. The coefficient β0 would represent the expected crop yield with no fertilizer or water. | |
C1007 | Parametric tests are those that make assumptions about the parameters of the population distribution from which the sample is drawn. This is often the assumption that the population data are normally distributed. Non-parametric tests are “distribution-free” and, as such, can be used for non-Normal variables. | |
C1008 | Quantiles are points in a distribution that relate to the rank order of values in that distribution. Centiles/percentiles are descriptions of quantiles relative to 100; so the 75th percentile (upper quartile) is 75% or three quarters of the way up an ascending list of sorted values of a sample. | |
C1009 | Dimensional AnalysisIdentify the given (see previous concept for additional information).Identify conversion factors that will help you get from your original units to your desired unit.Set up your equation so that your undesired units cancel out to give you your desired units. Multiply through to get your final answer. | |
C1010 | An unbiased estimator is an accurate statistic that's used to approximate a population parameter. “Accurate” in this sense means that it's neither an overestimate nor an underestimate. If an overestimate or underestimate does happen, the mean of the difference is called a “bias.” | |
C1011 | The scatter diagram graphs pairs of numerical data, with one variable on each axis, to look for a relationship between them. If the variables are correlated, the points will fall along a line or curve. The better the correlation, the tighter the points will hug the line. | |
C1012 | 2. What is the area under a conditional Cumulative density function? Explanation: Area under any conditional CDF is 1. | |
C1013 | Statistics is used to process complex problems in the real world so that Data Scientists and Analysts can look for meaningful trends and changes in Data. In simple words, Statistics can be used to derive meaningful insights from data by performing mathematical computations on it. | |
C1014 | Computer vision, however, is more than machine learning applied. It involves tasks as 3D scene modeling, multi-view camera geometry, structure-from-motion, stereo correspondence, point cloud processing, motion estimation and more, where machine learning is not a key element. | |
C1015 | It's a cost function that is used as loss for machine learning models, telling us how bad it's performing, the lower the better. Also it's much easier to reason about the loss this way, to be consistent with the rule of loss functions approaching 0 as the model gets better. | |
C1016 | Convergence in distribution is in some sense the weakest type of convergence. All it says is that the CDF of Xn's converges to the CDF of X as n goes to infinity. It does not require any dependence between the Xn's and X. We saw this type of convergence before when we discussed the central limit theorem. | |
C1017 | In statistics, the Kolmogorov–Smirnov test (K–S test or KS test) is a nonparametric test of the equality of continuous (or discontinuous, see Section 2.2), one-dimensional probability distributions that can be used to compare a sample with a reference probability distribution (one-sample K–S test), or to compare two | |
C1018 | The core idea is that we cannot know exactly how well an algorithm will work in practice (the true "risk") because we don't know the true distribution of data that the algorithm will work on, but we can instead measure its performance on a known set of training data (the "empirical" risk). | |
C1019 | A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population. | |
C1020 | Topic modeling is a method for unsupervised classification of such documents, similar to clustering on numeric data, which finds natural groups of items even when we're not sure what we're looking for. Latent Dirichlet allocation (LDA) is a particularly popular method for fitting a topic model. | |
C1021 | Linear regression is used for predicting the continuous dependent variable using a given set of independent features whereas Logistic Regression is used to predict the categorical. Linear regression is used to solve regression problems whereas logistic regression is used to solve classification problems.26/04/2020 | |
C1022 | Its observed value changes randomly from one random sample to a different sample. A test statistic contains information about the data that is relevant for deciding whether to reject the null hypothesis. The sampling distribution of the test statistic under the null hypothesis is called the null distribution. | |
C1023 | A left-skewed distribution has a long left tail. The normal distribution is the most common distribution you'll come across. Next, you'll see a fair amount of negatively skewed distributions. For example, household income in the U.S. is negatively skewed with a very long left tail. | |
C1024 | A vital aspect of the model construction process is the calibration phase. In fact, a model's predictive uncertainty will only be reduced by calibration if the information content of the calibration data set is able to constrain those parameters that have a significant bearing on that prediction. | |
C1025 | While data science focuses on the science of data, data mining is concerned with the process. It deals with the process of discovering newer patterns in big data sets. In machine learning algorithms are used for gaining knowledge from data sets. | |
C1026 | A kind of average sometimes used in statistics and engineering, often abbreviated as RMS. To find the root mean square of a set of numbers, square all the numbers in the set and then find the arithmetic mean of the squares. Take the square root of the result. This is the root mean square. | |
C1027 | While machine learning is based on the idea that machines should be able to learn and adapt through experience, AI refers to a broader idea where machines can execute tasks "smartly." Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. | |
C1028 | You always have to give a three-dimensional array as an input to your LSTM network (refer to the above image). And the third dimension represents the number of units in one input sequence. For example, input shape looks like (batch_size, time_steps, seq_len) . Let's look at an example in Keras. | |
C1029 | The following are key advantages of parallel programming that motivate its use for developing computing solutions: The main reason for parallel programming is to execute code efficiently, since parallel programming saves time, allowing the execution of applications in a shorter wall-clock time. | |
C1030 | Direct link to this answer A feedforward backpropagation net is a net that just happened to be trained with a backpropagation training algorithm. The backpropagation training algorithm subtracts the training output from the target (desired answer) to obtain the error signal. | |
C1031 | Let's explore 5 common techniques used for extracting information from the above text.Named Entity Recognition. The most basic and useful technique in NLP is extracting the entities in the text. Sentiment Analysis. Text Summarization. Aspect Mining. Topic Modeling. | |
C1032 | In the literal meaning of the terms, a parametric statistical test is one that makes assumptions about the parameters (defining properties) of the population distribution(s) from which one's data are drawn, while a non-parametric test is one that makes no such assumptions. | |
C1033 | Two types of Regression in machine learning: coefficient indicates the direction of the relationship between a predictor variable and the response variable. A positive sign indicates that as the predictor variable(y )increases, the response variable(X) also increases. | |
C1034 | Quota sampling means to take a very tailored sample that's in proportion to some characteristic or trait of a population. For example, you could divide a population by the state they live in, income or education level, or sex. Care is taken to maintain the correct proportions representative of the population. | |
C1035 | Median filtering A median filter is a nonlinear filter in which each output sample is computed as the median value of the input samples under the window – that is, the result is the middle value after the input values have been sorted. Ordinarily, an odd number of taps is used. | |
C1036 | Matrix decomposition methods, also called matrix factorization methods, are a foundation of linear algebra in computers, even for basic operations such as solving systems of linear equations, calculating the inverse, and calculating the determinant of a matrix. | |
C1037 | In mathematics, a tensor is an algebraic object that describes a (multilinear) relationship between sets of algebraic objects related to a vector space. Objects that tensors may map between include vectors and scalars, and even other tensors. | |
C1038 | There are two sorts of reasons for taking the log of a variable in a regression, one statistical, one substantive. When they are positively skewed (long right tail) taking logs can sometimes help. Sometimes logs are taken of the dependent variable, sometimes of one or more independent variables. | |
C1039 | How To Develop a Machine Learning Model From ScratchDefine adequately our problem (objective, desired outputs…).Gather data.Choose a measure of success.Set an evaluation protocol and the different protocols available.Prepare the data (dealing with missing values, with categorial values…).Spilit correctly the data.More items | |
C1040 | A lurking variable can falsely identify a strong relationship between variables or it can hide the true relationship. For example, a research scientist studies the effect of diet and exercise on a person's blood pressure. Lurking variables that also affect blood pressure are whether a person smokes and stress levels. | |
C1041 | Makes sense | |
C1042 | Equal width binning is probably the most popular way of doing discretization. This means that after the binning, all bins have equal width, or represent an equal range of the original variable values, no matter how many cases are in each bin. | |
C1043 | Feedfoward neural networks are primarily used for supervised learning in cases where the data to be learned is neither sequential nor time-dependent. That is, feedforward neural networks compute a function f on fixed size input x such that f ( x ) ≈ y f(x) \approx y f(x)≈y for training pairs ( x , y ) (x, y) (x,y). | |
C1044 | 4.1 Input Layer Input layer in CNN should contain image data. Image data is represented by three dimensional matrix as we saw earlier. You need to reshape it into a single column. If you have “m” training examples then dimension of input will be (784, m). | |
C1045 | Eigenface | |
C1046 | three types | |
C1047 | In statistics, a Poisson distribution is a statistical distribution that shows how many times an event is likely to occur within a specified period of time. It is used for independent events which occur at a constant rate within a given interval of time. | |
C1048 | Activation functions cannot be linear because neural networks with a linear activation function are effective only one layer deep, regardless of how complex their architecture is. Therefore, nonlinear functions must be continuous and differentiable between this range. | |
C1049 | Binomial Approximation The normal distribution can be used as an approximation to the binomial distribution, under certain circumstances, namely: If X ~ B(n, p) and if n is large and/or p is close to ½, then X is approximately N(np, npq) | |
C1050 | Since most natural phenomena are complex and have many factors, the same logic as above applies and distribution of measures of such phenomena tend to have most values near the mean (normal distibution has a desirable property of mean and mode being the same - i.e. the mean is the same as the most frequent value). | |
C1051 | The biggest advantage of linear regression models is linearity: It makes the estimation procedure simple and, most importantly, these linear equations have an easy to understand interpretation on a modular level (i.e. the weights). | |
C1052 | Every time you conduct a t-test there is a chance that you will make a Type I error. An ANOVA controls for these errors so that the Type I error remains at 5% and you can be more confident that any statistically significant result you find is not just running lots of tests. | |
C1053 | In Reinforcement Learning (RL), the problem to resolve is described as a Markov Decision Process (MDP). Theoretical results in RL rely on the MDP description being a correct match to the problem. Conversely, if you cannot map your problem onto a MDP, then the theory behind RL makes no guarantees of any useful result. | |
C1054 | The probability distribution of a continuous random variable X is an assignment of probabilities to intervals of decimal numbers using a function f(x), called a density function, in the following way: the probability that X assumes a value in the interval [a,b] is equal to the area of the region that is bounded above | |
C1055 | In a somewhat similar fashion you can estimate the standard deviation based on the box plot:the standard deviation is approximately equal to the range / 4.the standard deviation is approximately equal to 3/4 * IQR. | |
C1056 | The MNIST database, an extension of the NIST database, is a low-complexity data collection of handwritten digits used to train and test various supervised machine learning algorithms. The database contains 70,000 28x28 black and white images representing the digits zero through nine. | |
C1057 | You can reduce High variance, by reducing the number of features in the model. There are several methods available to check which features don't add much value to the model and which are of importance. Increasing the size of the training set can also help the model generalise. | |
C1058 | Latent Class Analysis (LCA) is a statistical method for identifying unmeasured class membership among subjects using categorical and/or continuous observed variables. For example, you may wish to categorize people based on their drinking behaviors (observations) into different types of drinkers (latent classes). | |
C1059 | Neural network converts data in such a form that it would be better to solve the desired problem. This is called representation learning. | |
C1060 | Root Mean Squared Error or RMSE RMSE is the standard deviation of the errors which occur when a prediction is made on a dataset. This is the same as MSE (Mean Squared Error) but the root of the value is considered while determining the accuracy of the model. from sklearn. | |
C1061 | The variance (symbolized by S2) and standard deviation (the square root of the variance, symbolized by S) are the most commonly used measures of spread. We know that variance is a measure of how spread out a data set is. It is calculated as the average squared deviation of each number from the mean of a data set. | |
C1062 | Conversion ruleTake glm output coefficient (logit)compute e-function on the logit using exp() “de-logarithimize” (you'll get odds then)convert odds to probability using this formula prob = odds / (1 + odds) . For example, say odds = 2/1 , then probability is 2 / (1+2)= 2 / 3 (~. | |
C1063 | Specificity is the proportion of truly negative cases that were classified as negative; thus, it is a measure of how well your classifier identifies negative cases. It is also known as the true negative rate. | |
C1064 | Here are some important considerations while choosing an algorithm.Size of the training data. It is usually recommended to gather a good amount of data to get reliable predictions. Accuracy and/or Interpretability of the output. Speed or Training time. Linearity. Number of features. | |
C1065 | Generalized Linear Models let you express the relation between covariates X and response y in a linear, additive manner. | |
C1066 | Machine learning models are designed to make the most accurate predictions possible. A statistical model is a model for the data that is used either to infer something about the relationships within the data or to create a model that is able to predict future values. Often, these two go hand-in-hand. | |
C1067 | The general definition of a vector space allows scalars to be elements of any fixed field F. The notion is then known as an F-vector space or a vector space over F. A field is, essentially, a set of numbers possessing addition, subtraction, multiplication and division operations. | |
C1068 | Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research. If you want to produce results that are representative of the whole population, you need to use a probability sampling technique. There are four main types of probability sample. | |
C1069 | There are different types of mean, viz. arithmetic mean, weighted mean, geometric mean (GM) and harmonic mean (HM). If mentioned without an adjective (as mean), it generally refers to the arithmetic mean. | |
C1070 | This can happen in high dimensional data with feature crosses, when there's a huge mass of rare crosses that happen only on one example each. Fortunately, using L2 or early stopping will prevent this problem. Logistic regression models generate probabilities. Log Loss is the loss function for logistic regression. | |
C1071 | In the statistical analysis of time series, autoregressive–moving-average (ARMA) models provide a parsimonious description of a (weakly) stationary stochastic process in terms of two polynomials, one for the autoregression (AR) and the second for the moving average (MA). | |
C1072 | Augmented reality uses existing reality and physical objects to trigger computer-generated enhancements over the top of reality, in real time. Essentially, AR is a technology that lays computer-generated images over a user's view of the real world. These images typically take shape as 3D models, videos and information. | |
C1073 | A decision tree is built top-down from a root node and involves partitioning the data into subsets that contain instances with similar values (homogenous). ID3 algorithm uses entropy to calculate the homogeneity of a sample. | |
C1074 | Linear models describe a continuous response variable as a function of one or more predictor variables. They can help you understand and predict the behavior of complex systems or analyze experimental, financial, and biological data. | |
C1075 | In short, they are independent because the bivariate normal density, in case they are uncorrelated, i.e. ρ=0, reduces to a product of two normal densities the support of each one ranges from (−∞,∞). If the joint distribution can be written as a product of nonnegative functions, we know that the RVs are independent. | |
C1076 | Comparison of bootstrap and jackknife Although there are huge theoretical differences in their mathematical insights, the main practical difference for statistics users is that the bootstrap gives different results when repeated on the same data, whereas the jackknife gives exactly the same result each time. | |
C1077 | To create a stratified random sample, there are seven steps: (a) defining the population; (b) choosing the relevant stratification; (c) listing the population; (d) listing the population according to the chosen stratification; (e) choosing your sample size; (f) calculating a proportionate stratification; and (g) using | |
C1078 | If the data is symmetrical - normally distributed - then the mean tell you where the line of symmetry falls. The standard deviation tells you more. It tells you if the data is closely distributed to the mean (small standard deviation) or is the data widely distributed (big standard deviation). | |
C1079 | A regression equation is used in stats to find out what relationship, if any, exists between sets of data. For example, if you measure a child's height every year you might find that they grow about 3 inches a year. That trend (growing three inches a year) can be modeled with a regression equation. | |
C1080 | Discriminant function analysis (DFA) is a statistical procedure that classifies unknown individuals and the probability of their classification into a certain group (such as sex or ancestry group). Discriminant function analysis makes the assumption that the sample is normally distributed for the trait. | |
C1081 | Advantages of Recurrent Neural Network An RNN remembers each and every information through time. It is useful in time series prediction only because of the feature to remember previous inputs as well. This is called Long Short Term Memory. | |
C1082 | Like I said before, the AUC-ROC curve is only for binary classification problems. But we can extend it to multiclass classification problems by using the One vs All technique. So, if we have three classes 0, 1, and 2, the ROC for class 0 will be generated as classifying 0 against not 0, i.e. 1 and 2. | |
C1083 | Mamdani Fuzzy Inference Systems Mamdani fuzzy inference was first introduced as a method to create a control system by synthesizing a set of linguistic control rules obtained from experienced human operators [1]. These output fuzzy sets are combined into a single fuzzy set using the aggregation method of the FIS. | |
C1084 | Computational Logic is the process of designing and analyzing logic in computer applications. In this lesson, we'll discuss creating logic based on the statements and constraints provided. Logic in relation to computers is mainly of two types: Propositional Logic and First Order Logic(FOL). | |
C1085 | Multimodal data Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it includes multiple such modalities. | |
C1086 | Mixed models add at least one random variable to a linear or generalized linear model. The random variables of a mixed model add the assumption that observations within a level, the random variable groups, are correlated. | |
C1087 | We use three main types of layers to build ConvNet architectures: Convolutional Layer, Pooling Layer, and Fully-Connected Layer (exactly as seen in regular Neural Networks). We will stack these layers to form a full ConvNet architecture. | |
C1088 | In computer science, a rule-based system is used to store and manipulate knowledge to interpret information in a useful way. It is often used in artificial intelligence applications and research. Normally, the term rule-based system is applied to systems involving human-crafted or curated rule sets. | |
C1089 | The 5 main steps to create word clouds in RStep 1: Create a text file. Step 2 : Install and load the required packages. Step 3 : Text mining. Step 4 : Build a term-document matrix. Step 5 : Generate the Word cloud. | |
C1090 | The second reason you may see validation loss lower than training loss is due to how the loss value are measured and reported: Training loss is measured during each epoch. While validation loss is measured after each epoch. | |
C1091 | Random field theory (RFT) is a recent body of mathematics defining theo- retical results for smooth statistical maps. The way that RFT solves this problem is by using results that give the expected Euler characteristic (EC) for a smooth statistical map that has been thresholded. | |
C1092 | Taking the square root of the variance gives us the units used in the original scale and this is the standard deviation. Standard deviation is the measure of spread most commonly used in statistical practice when the mean is used to calculate central tendency. Thus, it measures spread around the mean. | |
C1093 | When there are two or more independent variables, it is called multiple regression. | |
C1094 | 10:1614:33Suggested clip · 106 secondsPermutation Hypothesis Test in R with Examples | R Tutorial 4.6 YouTubeStart of suggested clipEnd of suggested clip | |
C1095 | When most dependent variables are numeric, logistic regression and SVM should be the first try for classification. These models are easy to implement, their parameters easy to tune, and the performances are also pretty good. So these models are appropriate for beginners. | |
C1096 | In machine learning, the delta rule is a gradient descent learning rule for updating the weights of the inputs to artificial neurons in a single-layer neural network. It is a special case of the more general backpropagation algorithm. | |
C1097 | One of the common methods for organizing data is to construct frequency distribution. Frequency distribution is an organized tabulation/graphical representation of the number of individuals in each category on the scale of measurement. | |
C1098 | The k-modes algorithm tries to minimize the sum of within-cluster Hamming distance from the mode of that cluster, summed over all clusters. The procedure is similar to k-means: a number of clusters (k) is chosen, and k cluster-mode vectors are chosen at random (or according to accepted heuristics). | |
C1099 | First, to find the conditional distribution of X given a value of Y, we can think of fixing a row in Table 1 and dividing the values of the joint pmf in that row by the marginal pmf of Y for the corresponding value. For example, to find pX|Y(x|1), we divide each entry in the Y=1 row by pY(1)=1/2. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.