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C9000 | among the constituent fields of anthropology. Physical anthropology has made the most use of statistics, while archeology, linguistics, and cultural anthropology have employed them much less frequently. | |
C9001 | CRF is a discriminant model. MEMM is not a generative model, but a model with finite states based on state classification. HMM and MEMM are a directed graph, while CRF is an undirected graph. HMM directly models the transition probability and the phenotype probability, and calculates the probability of co-occurrence. | |
C9002 | Network analytics, in its simplest definition, involves the analysis of network data and statistics to identify trends and patterns. Once identified, operators take the next step of 'acting' on this data—which typically involves a network operation or a set of operations. | |
C9003 | Imputation is a procedure for entering a value for a specific data item where the response is missing or unusable. Context: Imputation is the process used to determine and assign replacement values for missing, invalid or inconsistent data that have failed edits. | |
C9004 | Description. VGG-19 is a convolutional neural network that is 19 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database [1]. The pretrained network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. | |
C9005 | So there are exactly n vectors in every basis for Rn . By definition, the four column vectors of A span the column space of A. The third and fourth column vectors are dependent on the first and second, and the first two columns are independent. Therefore, the first two column vectors are the pivot columns. | |
C9006 | The main difference between these two techniques is that regression analysis deals with a continuous dependent variable, while discriminant analysis must have a discrete dependent variable. The methodology used to complete a discriminant analysis is similar to regression analysis. | |
C9007 | Machine Learning AlgorithmsLinear Regression. To understand the working functionality of this algorithm, imagine how you would arrange random logs of wood in increasing order of their weight. Logistic Regression. Decision Tree. SVM (Support Vector Machine) Naive Bayes. KNN (K- Nearest Neighbors) K-Means. Random Forest.More items• | |
C9008 | Definition: Given data the maximum likelihood estimate (MLE) for the parameter p is the value of p that maximizes the likelihood P(data |p). That is, the MLE is the value of p for which the data is most likely. 100 P(55 heads|p) = ( 55 ) p55(1 − p)45. | |
C9009 | Since this impulse response in infinitely long, recursive filters are often called infinite impulse response (IIR) filters. In effect, recursive filters convolve the input signal with a very long filter kernel, although only a few coefficients are involved. | |
C9010 | Explanation: Simple reflex agent is based on the present condition and so it is condition action rule. 5. What are the composition for agents in artificial intelligence? Explanation: An agent program will implement function mapping percepts to actions. | |
C9011 | Outlier detection is the process of detecting and subsequently excluding outliers from a given set of data. An outlier may be defined as a piece of data or observation that deviates drastically from the given norm or average of the data set. | |
C9012 | Step 1 — Deciding on the network topology (not really considered optimization but is obviously very important) Step 2 — Adjusting the learning rate. Step 3 — Choosing an optimizer and a loss function. Step 4 — Deciding on the batch size and number of epochs. Step 5 — Random restarts. | |
C9013 | "AI is a computer system able to perform tasks that ordinarily require human intelligence Many of these artificial intelligence systems are powered by machine learning, some of them are powered by deep learning and some of them are powered by very boring things like rules." | |
C9014 | It is closely related to prior probability, which is the probability an event will happen before you taken any new evidence into account. You can think of posterior probability as an adjustment on prior probability: Posterior probability = prior probability + new evidence (called likelihood). | |
C9015 | Clustering starts by computing a distance between every pair of units that you want to cluster. A distance matrix will be symmetric (because the distance between x and y is the same as the distance between y and x) and will have zeroes on the diagonal (because every item is distance zero from itself). | |
C9016 | Thus, if the random variable X is log-normally distributed, then Y = ln(X) has a normal distribution. Equivalently, if Y has a normal distribution, then the exponential function of Y, X = exp(Y), has a log-normal distribution. A random variable which is log-normally distributed takes only positive real values. | |
C9017 | Calculating Disparity Map First, squared difference or absolute difference is calcluated for each pixel and then all the values are summed over a window W. For each shift value of the right image, there is an SSD/SAD map equal to the size of the image. The disparity map is a 2D map reduced from 3D space. | |
C9018 | A deck of cards has within it uniform distributions because the likelihood of drawing a heart, a club, a diamond or a spade is equally likely. A coin also has a uniform distribution because the probability of getting either heads or tails in a coin toss is the same. | |
C9019 | The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena. For example, heights, blood pressure, measurement error, and IQ scores follow the normal distribution. | |
C9020 | The “trick” is that kernel methods represent the data only through a set of pairwise similarity comparisons between the original data observations x (with the original coordinates in the lower dimensional space), instead of explicitly applying the transformations ϕ(x) and representing the data by these transformed | |
C9021 | Thus, a double-blind, placebo-controlled clinical trial is a medical study involving human participants in which neither side knows who's getting what treatment and placebo are given to a control group. | |
C9022 | Classification is the process of classifying the data with the help of class labels whereas, in clustering, there are no predefined class labels. 2. Classification is supervised learning, while clustering is unsupervised learning. | |
C9023 | Inductive Reasoning Tips and TricksLearn the most common patterns. There are a set of extremely common patterns that the test providers will re-use. Use the elimination method. The optimal method of solving these logical problems is to use what we call the elimination method. Lock onto one sub pattern at a time and follow that through. | |
C9024 | Typical well-designed randomized controlled trials set at 0.10 or 0.20. Related to is the statistical power (), the probability of declaring the two treatments different when the true difference is exactly . | |
C9025 | Pooling layers are used to reduce the dimensions of the feature maps. Thus, it reduces the number of parameters to learn and the amount of computation performed in the network. The pooling layer summarises the features present in a region of the feature map generated by a convolution layer. | |
C9026 | So standard deviation gives you more deviation than mean deviation whem there are certain data points that are too far from its mean. | |
C9027 | 14) A deep thinker doesn't care for small talk They'd rather talk about the universe and what the meaning of life is. The good thing about a deep thinker is that they'll only speak when they have something important to say so everyone around them knows to listen. This is why they don't see silence as awkward. | |
C9028 | The intercept of the regression line is just the predicted value for y, when x is 0. Any line has an equation, in terms of its slope and intercept: y = slope x x + intercept. | |
C9029 | "Recognize that any frequentist statistical test has a random chance of indicating significance when it is not really present. Running multiple tests on the same data set at the same stage of an analysis increases the chance of obtaining at least one invalid result. | |
C9030 | Factorials (!) are products of every whole number from 1 to n. For example: If n is 3, then 3! is 3 x 2 x 1 = 6. If n is 5, then 5! is 5 x 4 x 3 x 2 x 1 = 120. | |
C9031 | The chi-square distribution has the following properties: The mean of the distribution is equal to the number of degrees of freedom: μ = v. The variance is equal to two times the number of degrees of freedom: σ2 = 2 * v. | |
C9032 | Platt scaling works well for SVMs(Support Vector Machine) as well as other types of classification models, including boosted models and even naive Bayes classifiers, which produce distorted probability distributions. | |
C9033 | Augmented reality holds the promise of creating direct, automatic, and actionable links between the physical world and electronic information. It provides a simple and immediate user interface to an electronically enhanced physical world. | |
C9034 | Unsupervised or undirected data science uncovers hidden patterns in unlabeled data. In unsupervised data science, there are no output variables to predict. The objective of this class of data science techniques, is to find patterns in data based on the relationship between data points themselves. | |
C9035 | KNN algorithm is one of the simplest classification algorithm. Even with such simplicity, it can give highly competitive results. KNN algorithm can also be used for regression problems. | |
C9036 | 0:559:25Suggested clip · 84 secondsHow To Calculate Pearson's Correlation Coefficient (r) by Hand YouTubeStart of suggested clipEnd of suggested clip | |
C9037 | Matrix theory is a branch of mathematics which is focused on study of matrices. Initially, it was a sub-branch of linear algebra, but soon it grew to cover subjects related to graph theory, algebra, combinatorics and statistics as well. | |
C9038 | “Kernel” is used due to set of mathematical functions used in Support Vector Machine provides the window to manipulate the data. So, Kernel Function generally transforms the training set of data so that a non-linear decision surface is able to transformed to a linear equation in a higher number of dimension spaces. | |
C9039 | 68% of the data is within 1 standard deviation (σ) of the mean (μ), 95% of the data is within 2 standard deviations (σ) of the mean (μ), and 99.7% of the data is within 3 standard deviations (σ) of the mean (μ). | |
C9040 | The difference between a ratio scale and an interval scale is that the zero point on an interval scale is some arbitrarily agreed value, whereas on a ratio scale it is a true zero. | |
C9041 | An expert system (ES) is a knowledge-based system that employs knowledge about its application domain and uses an inferencing (reason) procedure to solve problems that would otherwise require human competence or expertise. | |
C9042 | Positive feedback may be controlled by signals in the system being filtered, damped, or limited, or it can be cancelled or reduced by adding negative feedback. Positive feedback is used in digital electronics to force voltages away from intermediate voltages into '0' and '1' states. | |
C9043 | The correlation coefficient is a number that summarizes the direction and degree (closeness) of linear relations between two variables. The correlation coefficient is also known as the Pearson Product-Moment Correlation Coefficient. The sample value is called r, and the population value is called r (rho). | |
C9044 | Bivariate analysis investigates the relationship between two data sets, with a pair of observations taken from a single sample or individual. However, each sample is independent. You analyze the data using tools such as t-tests and chi-squared tests, to see if the two groups of data correlate with each other. | |
C9045 | In statistics, a positively skewed (or right-skewed) distribution is a type of distribution in which most values are clustered around the left tail of the distribution while the right tail of the distribution is longer. | |
C9046 | Variance (σ2) in statistics is a measurement of the spread between numbers in a data set. That is, it measures how far each number in the set is from the mean and therefore from every other number in the set. | |
C9047 | C and Gamma are the parameters for a nonlinear support vector machine (SVM) with a Gaussian radial basis function kernel. A standard SVM seeks to find a margin that separates all positive and negative examples. Gamma is the free parameter of the Gaussian radial basis function. | |
C9048 | 4:551:11:29Suggested clip · 112 secondsRodrigo Agundez: Building a live face recognition system | Pydata YouTubeStart of suggested clipEnd of suggested clip | |
C9049 | 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. | |
C9050 | An A/B test, also known as a split test, is an experiment for determining which of different variations of an online experience performs better by presenting each version to users at random and analyzing the results. A/B testing can do a lot more than prove how changes can impact your conversions in the short-term. | |
C9051 | 1 Biasedness - The bias of on estimator is defined as: Bias( ˆθ) = E( ˆ θ ) - θ, where ˆ θ is an estimator of θ, an unknown population parameter. If E( ˆ θ ) = θ, then the estimator is unbiased. | |
C9052 | If there are other predictor variables, all coefficients will be changed. All the coefficients are jointly estimated, so every new variable changes all the other coefficients already in the model. This is one reason we do multiple regression, to estimate coefficient B1 net of the effect of variable Xm. | |
C9053 | A latent variable is a variable that cannot be observed. The presence of latent variables, however, can be detected by their effects on variables that are observable. Most constructs in research are latent variables. Consider the psychological construct of anxiety, for example. | |
C9054 | Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label. As the predicted probability decreases, however, the log loss increases rapidly. | |
C9055 | Advertisements. An algorithm is designed to achieve optimum solution for a given problem. In greedy algorithm approach, decisions are made from the given solution domain. As being greedy, the closest solution that seems to provide an optimum solution is chosen. | |
C9056 | Natural language processing (NLP) is one of the most important technologies of the information age. The course provides a deep excursion into cutting-edge research in deep learning applied to NLP. The final project will involve training a complex recurrent neural network and applying it to a large scale NLP problem. | |
C9057 | Important!The Coin Flipping Example.Steps of Bayesian Inference. Step 1: Identify the Observed Data. Step 2: Construct a Probabilistic Model to Represent the Data. Step 3: Specify Prior Distributions. Step 4: Collect Data and Application of Bayes' Rule.Conclusions.R Session. | |
C9058 | For example, if the distribution of raw scores if normally distributed, so is the distribution of z-scores. The mean of any SND always = 0. The standard deviation of any SND always = 1. Therefore, one standard deviation of the raw score (whatever raw value this is) converts into 1 z-score unit. | |
C9059 | Both skew and kurtosis can be analyzed through descriptive statistics. Acceptable values of skewness fall between − 3 and + 3, and kurtosis is appropriate from a range of − 10 to + 10 when utilizing SEM (Brown, 2006). | |
C9060 | According to Investopedia, a model is considered to be robust if its output dependent variable (label) is consistently accurate even if one or more of the input independent variables (features) or assumptions are drastically changed due to unforeseen circumstances. | |
C9061 | Standard units are common units of measurement such as centimetres, grams and litres. Non-standard units of measurement might include cups, cubes or sweets. | |
C9062 | Feature detection is a low-level image processing operation. That is, it is usually performed as the first operation on an image, and examines every pixel to see if there is a feature present at that pixel. | |
C9063 | Continuous probability distribution: A probability distribution in which the random variable X can take on any value (is continuous). Because there are infinite values that X could assume, the probability of X taking on any one specific value is zero. Therefore we often speak in ranges of values (p(X>0) = . 50). | |
C9064 | Machine vision systems rely on digital sensors protected inside industrial cameras with specialized optics to acquire images, so that computer hardware and software can process, analyze, and measure various characteristics for decision making. | |
C9065 | In implementing most of the machine learning algorithms, we represent each data point with a feature vector as the input. A vector is basically an array of numerics, or in physics, an object with magnitude and direction. | |
C9066 | CNNs can be used in tons of applications from image and video recognition, image classification, and recommender systems to natural language processing and medical image analysis. This is the way that a CNN works! Image by NatWhitePhotography on Pixabay. CNNs have an input layer, and output layer, and hidden layers. | |
C9067 | The number of examples that belong to each class may be referred to as the class distribution. Imbalanced classification refers to a classification predictive modeling problem where the number of examples in the training dataset for each class label is not balanced. | |
C9068 | In statistics, a frequency distribution is a list, table or graph that displays the frequency of various outcomes in a sample. Each entry in the table contains the frequency or count of the occurrences of values within a particular group or interval. | |
C9069 | 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. | |
C9070 | Logistic Regression in R: A Classification Technique to Predict Credit Card Default. 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. | |
C9071 | The goal of observational research is to describe a variable or set of variables. The data that are collected in observational research studies are often qualitative in nature but they may also be quantitative or both (mixed-methods). | |
C9072 | 0:434:29Suggested clip · 113 secondsPROBABILITY HISTOGRAM WITH EXCEL SIMPLE - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C9073 | There are multiple ways to select a good starting point for the learning rate. A naive approach is to try a few different values and see which one gives you the best loss without sacrificing speed of training. We might start with a large value like 0.1, then try exponentially lower values: 0.01, 0.001, etc. | |
C9074 | Six quick tips to improve your regression modelingA.1. Fit many models. A.2. Do a little work to make your computations faster and more reliable. A.3. Graphing the relevant and not the irrelevant. A.4. Transformations. A.5. Consider all coefficients as potentially varying. A.6. Estimate causal inferences in a targeted way, not as a byproduct of a large regression. | |
C9075 | Deviation means change or distance. But change is always followed by the word 'from'. Hence standard deviation is a measure of change or the distance from a measure of central tendency - which is normally the mean. Hence, standard deviation is different from a measure of central tendency. | |
C9076 | Description. Probability & Statistics introduces students to the basic concepts and logic of statistical reasoning and gives the students introductory-level practical ability to choose, generate, and properly interpret appropriate descriptive and inferential methods. | |
C9077 | Statistical Validity is the extent to which the conclusions drawn from a statistical test are accurate and reliable. To achieve statistical validity, researchers must have an adequate sample size and pick the right statistical test to analyze the data. | |
C9078 | Cluster analysis is a multivariate method which aims to classify a sample of subjects (or ob- jects) on the basis of a set of measured variables into a number of different groups such that similar subjects are placed in the same group. – Agglomerative methods, in which subjects start in their own separate cluster. | |
C9079 | Bagging is a way to decrease the variance in the prediction by generating additional data for training from dataset using combinations with repetitions to produce multi-sets of the original data. Boosting is an iterative technique which adjusts the weight of an observation based on the last classification. | |
C9080 | Blind Search - searching without information. Heuristic Seach- searching with information. For example : A* Algorithm. We choose our next state based on cost and 'heuristic information' with heuristic function. | |
C9081 | This is because a two-tailed test uses both the positive and negative tails of the distribution. In other words, it tests for the possibility of positive or negative differences. A one-tailed test is appropriate if you only want to determine if there is a difference between groups in a specific direction. | |
C9082 | We use factorials when we look at permutations and combinations. Permutations tell us how many different ways we can arrange things if their order matters. Combinations tells us how many ways we can choose k item from n items if their order does not matter. | |
C9083 | Greedy is an algorithmic paradigm that builds up a solution piece by piece, always choosing the next piece that offers the most obvious and immediate benefit. So the problems where choosing locally optimal also leads to global solution are best fit for Greedy. For example consider the Fractional Knapsack Problem. | |
C9084 | This is because a two-tailed test uses both the positive and negative tails of the distribution. In other words, it tests for the possibility of positive or negative differences. A one-tailed test is appropriate if you only want to determine if there is a difference between groups in a specific direction. | |
C9085 | 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. | |
C9086 | Seq2seq is a family of machine learning approaches used for language processing. Applications include language translation, image captioning, conversational models and text summarization. | |
C9087 | First consider the case when X and Y are both discrete. Then the marginal pdf's (or pmf's = probability mass functions, if you prefer this terminology for discrete random variables) are defined by fY(y) = P(Y = y) and fX(x) = P(X = x). The joint pdf is, similarly, fX,Y(x,y) = P(X = x and Y = y). | |
C9088 | The confidence of an association rule is the support of (X U Y) divided by the support of X. Therefore, the confidence of the association rule is in this case the support of (2,5,3) divided by the support of (2,5). | |
C9089 | So to summarize, the basic principles that guide the use of the AIC are:Lower indicates a more parsimonious model, relative to a model fit. It is a relative measure of model parsimony, so it only has. We can compare non-nested models. The comparisons are only valid for models that are fit to the same response.More items• | |
C9090 | The binomial distribution model allows us to compute the probability of observing a specified number of "successes" when the process is repeated a specific number of times (e.g., in a set of patients) and the outcome for a given patient is either a success or a failure. | |
C9091 | The central limit theorem states that the CDF of Zn converges to the standard normal CDF. converges in distribution to the standard normal random variable as n goes to infinity, that is limn→∞P(Zn≤x)=Φ(x), for all x∈R, The Xi's can be discrete, continuous, or mixed random variables. | |
C9092 | As we saw above, KNN algorithm can be used for both classification and regression problems. The KNN algorithm uses 'feature similarity' to predict the values of any new data points. This means that the new point is assigned a value based on how closely it resembles the points in the training set. | |
C9093 | Convolutional neural networks (CNNs, or ConvNets) are essential tools for deep learning, and are especially suited for analyzing image data. For example, you can use CNNs to classify images. To predict continuous data, such as angles and distances, you can include a regression layer at the end of the network. | |
C9094 | A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. | |
C9095 | The 2-sample t-test takes your sample data from two groups and boils it down to the t-value. The process is very similar to the 1-sample t-test, and you can still use the analogy of the signal-to-noise ratio. Unlike the paired t-test, the 2-sample t-test requires independent groups for each sample. | |
C9096 | Input means to provide the program with some data to be used in the program and Output means to display data on screen or write the data to a printer or a file. C programming language provides many built-in functions to read any given input and to display data on screen when there is a need to output the result. | |
C9097 | Simple Random Sampling | |
C9098 | Moment generating functions are a way to find moments like the mean(μ) and the variance(σ2). They are an alternative way to represent a probability distribution with a simple one-variable function. | |
C9099 | Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. Explicit regression gradient boosting algorithms were subsequently developed by Jerome H. |
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