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C9700 | Artificial general intelligence (AGI) is the representation of generalized human cognitive abilities in software so that, faced with an unfamiliar task, the AI system could find a solution. IBM's Watson supercomputer, expert systems and the self-driving car are all examples of weak or narrow AI. | |
C9701 | Let's return to our example comparing the mean of a sample to a given value x using a t-test. Our null hypothesis is that the mean is equal to x. A one-tailed test will test either if the mean is significantly greater than x or if the mean is significantly less than x, but not both. | |
C9702 | Time efficiency - a measure of amount of time for an algorithm to execute. Space efficiency - a measure of the amount of memory needed for an algorithm to execute. Asymptotic dominance - comparison of cost functions when n is large. That is, g asymptotically dominates f if g dominates f for all "large" values of n. | |
C9703 | 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. | |
C9704 | fastText is another word embedding method that is an extension of the word2vec model. Instead of learning vectors for words directly, fastText represents each word as an n-gram of characters. This helps capture the meaning of shorter words and allows the embeddings to understand suffixes and prefixes. | |
C9705 | ReLU is linear (identity) for all positive values, and zero for all negative values. This means that: Since ReLU is zero for all negative inputs, it's likely for any given unit to not activate at all. This is often desirable (see below). | |
C9706 | Returns the inverse, or critical value, of the cumulative standard normal distribution. This function computes the critical value so that the cumulative distribution is greater than or equal to a pre-specified value. | |
C9707 | N-grams of texts are extensively used in text mining and natural language processing tasks. They are basically a set of co-occuring words within a given window and when computing the n-grams you typically move one word forward (although you can move X words forward in more advanced scenarios). | |
C9708 | Leonard Savage's decision theory, as presented in his (1954) The Foundations of Statistics, is without a doubt the best-known normative theory of choice under uncertainty, in particular within economics and the decision sciences. | |
C9709 | Last Updated on Decem. Cross-entropy is commonly used in machine learning as a loss function. Cross-entropy is a measure from the field of information theory, building upon entropy and generally calculating the difference between two probability distributions. | |
C9710 | The fundamental difference between the two correlation coefficients is that the Pearson coefficient works with a linear relationship between the two variables whereas the Spearman Coefficient works with monotonic relationships as well. | |
C9711 | There are two types of probability distribution which are used for different purposes and various types of the data generation process. Let us discuss now both the types along with its definition, formula and examples. | |
C9712 | Step 1: Learn the fundamental data structures and algorithms. First, pick a favorite language to focus on and stick with it. Step 2: Learn advanced concepts, data structures, and algorithms. Step 1+2: Practice. Step 3: Lots of reading + writing. Step 4: Contribute to open-source projects. Step 5: Take a break. | |
C9713 | Facebook Trending is a feature of the social network designed to show each user a list of topics that are spiking in popularity in updates, posts, and comments. Facebook Trending appears as a short list of keywords and phrases in a small module at the top right of the user's News Feed. | |
C9714 | Table 1Type of BiasHow to AvoidSelection bias• Select patients using rigorous criteria to avoid confounding results. Patients should originate from the same general population. Well designed, prospective studies help to avoid selection bias as outcome is unknown at time of enrollment.17 more rows | |
C9715 | It is very much like the exponential distribution, with λ corresponding to 1/p, except that the geometric distribution is discrete while the exponential distribution is continuous. | |
C9716 | In the graph, the tangent line at c (derivative at c) is equal to the slope of [a,b] where a <>. The Mean Value Theorem is an extension of the Intermediate Value Theorem, stating that between the continuous interval [a,b], there must exist a point c where the tangent at f(c) is equal to the slope of the interval. | |
C9717 | Page Content. Many texts are multimodal, where meaning is communicated through combinations of two or more modes. Modes include written language, spoken language, and patterns of meaning that are visual, audio, gestural, tactile and spatial. | |
C9718 | Sets can be used in calculated fields Sets can be used in calculated fields as if they were a field. Or you can have the calculation return a specific value, or return another field instead, the main point is that they are not very different than normal dimensions in this respect. | |
C9719 | Ordinal YouTubeStart of suggested clipEnd of suggested clip | |
C9720 | ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average. This is one of the easiest and effective machine learning algorithm to performing time series forecasting. In simple words, it performs regression in previous time step t-1 to predict t. | |
C9721 | 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. | |
C9722 | There are many practical measures of randomness for a binary sequence.Specific tests for randomnessLinear congruential generator and Linear-feedback shift register.Generalized Fibonacci generator.Cryptographic generators.Quadratic congruential generator.Cellular automaton generators.Pseudorandom binary sequence. | |
C9723 | Hidden Markov models (HMMs) have been extensively used in biological sequence analysis. We especially focus on three types of HMMs: the profile-HMMs, pair-HMMs, and context-sensitive HMMs. | |
C9724 | A one-way ANOVA uses one independent variable, while a two-way ANOVA uses two independent variables. One-way ANOVA example As a crop researcher, you want to test the effect of three different fertilizer mixtures on crop yield. | |
C9725 | Algorithms can be difficult for some people. But I think if you learn a couple of basic ones, it will gradually get easier. But you just gotta do them. For some people, they are a little easier in the beginning. | |
C9726 | Types of testing strategiesAnalytical strategy.Model based strategy.Methodical strategy.Standards compliant or Process compliant strategy.Reactive strategy.Consultative strategy.Regression averse strategy. | |
C9727 | Hyperparameters are crucial as they control the overall behaviour of a machine learning model. The ultimate goal is to find an optimal combination of hyperparameters that minimizes a predefined loss function to give better results. | |
C9728 | The critical region is the area that lies to the left of -1.645. If the z-value is less than -1.645 there we will reject the null hypothesis and accept the alternative hypothesis. If it is greater than -1.645, we will fail to reject the null hypothesis and say that the test was not statistically significant. | |
C9729 | In a nutshell, hierarchical linear modeling is used when you have nested data; hierarchical regression is used to add or remove variables from your model in multiple steps. Knowing the difference between these two seemingly similar terms can help you determine the most appropriate analysis for your study. | |
C9730 | Similarity is a numerical measure of how alike two data objects are, and dissimilarity is a numerical measure of how different two data objects are. We go into more data mining in our data science bootcamp, have a look. | |
C9731 | It is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. | |
C9732 | A series converges uniformly on if the sequence of partial sums defined by. (2) converges uniformly on . To test for uniform convergence, use Abel's uniform convergence test or the Weierstrass M-test. | |
C9733 | The rectified linear activation function or ReLU for short is a piecewise linear function that will output the input directly if it is positive, otherwise, it will output zero. | |
C9734 | In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a probability distribution by maximizing a likelihood function, so that under the assumed statistical model the observed data is most probable. | |
C9735 | Randomization as a method of experimental control has been extensively used in human clinical trials and other biological experiments. It prevents the selection bias and insures against the accidental bias. It produces the comparable groups and eliminates the source of bias in treatment assignments. | |
C9736 | The central limit theorem tells us that no matter what the distribution of the population is, the shape of the sampling distribution will approach normality as the sample size (N) increases. Thus, as the sample size (N) increases the sampling error will decrease. | |
C9737 | Mixed models explicitly account for the correlations between repeated measurements within each patient. Mixed models are called “mixed” because they generally contain both fixed and random effects. | |
C9738 | The logit model uses something called the cumulative distribution function of the logistic distribution. The probit model uses something called the cumulative distribution function of the standard normal distribution to define f(∗). Both functions will take any number and rescale it to fall between 0 and 1. | |
C9739 | Adam is a replacement optimization algorithm for stochastic gradient descent for training deep learning models. Adam combines the best properties of the AdaGrad and RMSProp algorithms to provide an optimization algorithm that can handle sparse gradients on noisy problems. | |
C9740 | 5 Successful ExamplesSentiment Analysis Examples.Reputation Management - Social Media Monitoring - Brand Monitoring.Market Research, Competitor Analysis.Product Analytics.Customer Analysis.Customer Support. | |
C9741 | For a random variable yt, the unconditional mean is simply the expected value, E ( y t ) . In contrast, the conditional mean of yt is the expected value of yt given a conditioning set of variables, Ωt. A conditional mean model specifies a functional form for E ( y t | Ω t ) . . | |
C9742 | SVM tries to finds the “best” margin (distance between the line and the support vectors) that separates the classes and this reduces the risk of error on the data, while logistic regression does not, instead it can have different decision boundaries with different weights that are near the optimal point. | |
C9743 | Bivariate analysis looks at two paired data sets, studying whether a relationship exists between them. Multivariate analysis uses two or more variables and analyzes which, if any, are correlated with a specific outcome. The goal in the latter case is to determine which variables influence or cause the outcome. | |
C9744 | Showing a transformation is linear using the definitionT(c→u+d→v)=cT(→u)+dT(→v)Overall, since our goal is to show that T(c→u+d→v)=cT(→u)+dT(→v), we will calculate one side of this equation and then the other, finally showing that they are equal.T(c→u+d→v)=cT(→u)+dT(→v)=we have shown that T(c→u+d→v)=cT(→u)+dT(→v). Thus, by definition, the transformation is linear. ◼ | |
C9745 | 2 Answers. Simply put because one level of your categorical feature (here location) become the reference group during dummy encoding for regression and is redundant. I am quoting form here "A categorical variable of K categories, or levels, usually enters a regression as a sequence of K-1 dummy variables. | |
C9746 | Random forest is a supervised learning algorithm which is used for both classification as well as regression. Similarly, random forest algorithm creates decision trees on data samples and then gets the prediction from each of them and finally selects the best solution by means of voting. | |
C9747 | A neural network is either a system software or hardware that works similar to the tasks performed by neurons of human brain. Neural networks include various technologies like deep learning, and machine learning as a part of Artificial Intelligence (AI). | |
C9748 | In cost-sensitive learning instead of each instance being either correctly or incorrectly classified, each class (or instance) is given a misclassification cost. | |
C9749 | Use. Cluster sampling is typically used in market research. It's used when a researcher can't get information about the population as a whole, but they can get information about the clusters. Cluster sampling is often more economical or more practical than stratified sampling or simple random sampling. | |
C9750 | Measures of Dispersion A measure of dispersion is a statistic that tells you how dispersed, or spread out, data values are. One simple measure of dispersion is the range, which is the difference between the greatest and least data values. | |
C9751 | The decision of which statistical test to use depends on the research design, the distribution of the data, and the type of variable. In general, if the data is normally distributed, parametric tests should be used. If the data is non-normal, non-parametric tests should be used. | |
C9752 | ROC curves are frequently used to show in a graphical way the connection/trade-off between clinical sensitivity and specificity for every possible cut-off for a test or a combination of tests. In addition, the area under the ROC curve gives an idea about the benefit of using the test(s) in question. | |
C9753 | Within an artificial neural network, a neuron is a mathematical function that model the functioning of a biological neuron. Typically, a neuron compute the weighted average of its input, and this sum is passed through a nonlinear function, often called activation function, such as the sigmoid. | |
C9754 | A sample survey can be broadly defined as an exercise that involves collecting standardised data from a sample of study units (e.g., persons, households, businesses) designed to represent a larger population of units, in order to make quantitative inferences about the population. | |
C9755 | In Convolutional Neural Networks, Filters detect spatial patterns such as edges in an image by detecting the changes in intensity values of the image. | |
C9756 | In mathematics, proof by contrapositive, or proof by contraposition, is a rule of inference used in proofs, where one infers a conditional statement from its contrapositive. In other words, the conclusion "if A, then B" is inferred by constructing a proof of the claim "if not B, then not A" instead. | |
C9757 | How to calculate percentileRank the values in the data set in order from smallest to largest.Multiply k (percent) by n (total number of values in the data set). If the index is not a round number, round it up (or down, if it's closer to the lower number) to the nearest whole number.Use your ranked data set to find your percentile. | |
C9758 | We use two well-known trained CNNs, GoogLeNet (Szegedy et al. GoogLeNet has Inception Modules, which perform different sizes of convolutions and concatenate the filters for the next layer. AlexNet, on the other hand, has layers input provided by one previous layer instead of a filter concatenation. | |
C9759 | Median smoothers are widely used in image processing to clean images corrupted by noise. Median filters are particularly effective at removing outliers. Often referred to as “salt and pepper” noise, outliers are often present due to bit errors in transmission, or introduced during the signal acquisition stage. | |
C9760 | For two numbers x and y, let x, a, y be a sequence of three numbers. If x, a, y is an arithmetic progression then 'a' is called arithmetic mean. If x, a, y is a geometric progression then 'a' is called geometric mean. If x, a, y form a harmonic progression then 'a' is called harmonic mean. | |
C9761 | Machine Learning: Reinforcement Learning — Markov Decision Processes. A mathematical representation of a complex decision making process is “Markov Decision Processes” (MDP). MDP is defined by: A state S, which represents every state that one could be in, within a defined world. | |
C9762 | 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. | |
C9763 | Model selection is the process of selecting one final machine learning model from among a collection of candidate machine learning models for a training dataset. Model selection is the process of choosing one of the models as the final model that addresses the problem. | |
C9764 | The mean of the random variable Y is also called the expected value or the expectation of Y. It is denoted E(Y). It is also called the population mean, often denoted µ. It is what we do not know in this example. A sample mean is typically denoted ȳ (read "y-bar"). | |
C9765 | The chi-square test is a hypothesis test designed to test for a statistically significant relationship between nominal and ordinal variables organized in a bivariate table. In other words, it tells us whether two variables are independent of one another. The chi-square test is sensitive to sample size. | |
C9766 | For machine learning, every dataset does not require normalization. It is required only when features have different ranges. For example, consider a data set containing two features, age, and income(x2). Where age ranges from 0–100, while income ranges from 0–100,000 and higher. | |
C9767 | Nonparametric tests have the following limitations: Nonparametric tests are usually less powerful than corresponding parametric test when the normality assumption holds. Thus, you are less likely to reject the null hypothesis when it is false if the data comes from the normal distribution. | |
C9768 | However, for a general population it is not true that the sample median is an unbiased estimator of the population median. The sample mean is a biased estimator of the population median when the population is not symmetric. It only will be unbiased if the population is symmetric. | |
C9769 | Chi-square Test. The Pearson's χ2 test (after Karl Pearson, 1900) is the most commonly used test for the difference in distribution of categorical variables between two or more independent groups. | |
C9770 | SVM can be used to optimize classification of images (or subimages, for segmentation). SVM does not provide image classification mechanisms. | |
C9771 | Ordinary Least Squares regression (OLS) is more commonly named linear regression (simple or multiple depending on the number of explanatory variables). The OLS method corresponds to minimizing the sum of square differences between the observed and predicted values. | |
C9772 | Essentially, the process goes as follows:Select k centroids. These will be the center point for each segment.Assign data points to nearest centroid.Reassign centroid value to be the calculated mean value for each cluster.Reassign data points to nearest centroid.Repeat until data points stay in the same cluster. | |
C9773 | Linear filtering is the filtering method in which the value of output pixel is linear combinations of the neighbouring input pixels. it can be done with convolution. For examples, mean/average filters or Gaussian filtering. A non-linear filtering is one that cannot be done with convolution or Fourier multiplication. | |
C9774 | In mathematics, the geometric mean is a mean or average, which indicates the central tendency or typical value of a set of numbers by using the product of their values (as opposed to the arithmetic mean which uses their sum). | |
C9775 | Pooling Layers Its function is to progressively reduce the spatial size of the representation to reduce the amount of parameters and computation in the network. Pooling layer operates on each feature map independently. The most common approach used in pooling is max pooling. | |
C9776 | Definition: A vector space is a set V on which two operations + and · are defined, called vector addition and scalar multiplication. The operation + (vector addition) must satisfy the following conditions: Closure: If u and v are any vectors in V, then the sum u + v belongs to V. | |
C9777 | In general, an AUC of 0.5 suggests no discrimination (i.e., ability to diagnose patients with and without the disease or condition based on the test), 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding. | |
C9778 | One advantage of using sparse categorical cross entropy is it saves time in memory as well as computation because it simply uses a single integer for a class, rather than a whole vector. | |
C9779 | Multimodal learning suggests that when a number of our senses - visual, auditory, kinaesthetic - are being engaged during learning, we understand and remember more. By combining these modes, learners experience learning in a variety of ways to create a diverse learning style. | |
C9780 | At a bare minimum, collect around 1000 examples. For most "average" problems, you should have 10,000 - 100,000 examples. For “hard” problems like machine translation, high dimensional data generation, or anything requiring deep learning, you should try to get 100,000 - 1,000,000 examples. | |
C9781 | An algorithm X is said to be asymptotically better than Y if X takes smaller time than y for all input sizes n larger than a value n0 where n0 > 0. | |
C9782 | In General, A Discriminative model models the decision boundary between the classes. A Generative Model explicitly models the actual distribution of each class. A Discriminative model learns the conditional probability distribution p(y|x). Both of these models were generally used in supervised learning problems. | |
C9783 | Cross Entropy is definitely a good loss function for Classification Problems, because it minimizes the distance between two probability distributions - predicted and actual. | |
C9784 | Minimax is a kind of backtracking algorithm that is used in decision making and game theory to find the optimal move for a player, assuming that your opponent also plays optimally. It is widely used in two player turn-based games such as Tic-Tac-Toe, Backgammon, Mancala, Chess, etc. | |
C9785 | Mean, variance, and standard deviation The mean of the sampling distribution of the sample mean will always be the same as the mean of the original non-normal distribution. In other words, the sample mean is equal to the population mean. where σ is population standard deviation and n is sample size. | |
C9786 | Running the ProcedureClick Transform > Recode into Different Variables.Double-click on variable CommuteTime to move it to the Input Variable -> Output Variable box. In the Output Variable area, give the new variable the name CommuteLength, then click Change.Click the Old and New Values button. Click OK. | |
C9787 | The least-squares regression line always passes through the point (x, y). 3. The square of the correlation, r2, is the fraction of the variation in the values of y that is explained by the least- squares regression of y on x. | |
C9788 | To reach the best generalization, the dataset should be split into three parts: The training set is used to train a neural net. The error of this dataset is minimized during training. The validation set is used to determine the performance of a neural network on patterns that are not trained during learning. | |
C9789 | A set is countable if: (1) it is finite, or (2) it has the same cardinality (size) as the set of natural numbers (i.e., denumerable). Equivalently, a set is countable if it has the same cardinality as some subset of the set of natural numbers. Otherwise, it is uncountable. | |
C9790 | The higher the number of features, the harder it gets to visualize the training set and then work on it. Dimensionality reduction is the process of reducing the number of random variables under consideration, by obtaining a set of principal variables. It can be divided into feature selection and feature extraction. | |
C9791 | “The major difference between machine learning and statistics is their purpose. Machine learning models are designed to make the most accurate predictions possible. Statistical models are designed for inference about the relationships between variables.” You cannot do statistics unless you have data. | |
C9792 | An additive effect refers to the role of a variable in an estimated model. A variable that has an additive effect can merely be added to the other terms in a model to determine its effect on the independent variable. | |
C9793 | chromate ions | |
C9794 | In Decision Trees, for predicting a class label for a record we start from the root of the tree. We compare the values of the root attribute with the record's attribute. On the basis of comparison, we follow the branch corresponding to that value and jump to the next node. | |
C9795 | A good maximum sample size is usually 10% as long as it does not exceed 1000. A good maximum sample size is usually around 10% of the population, as long as this does not exceed 1000. For example, in a population of 5000, 10% would be 500. In a population of 200,000, 10% would be 20,000. | |
C9796 | The parameters of a neural network are typically the weights of the connections. So, the algorithm itself (and the input data) tunes these parameters. The hyper parameters are typically the learning rate, the batch size or the number of epochs. | |
C9797 | An iteration is a term used in machine learning and indicates the number of times the algorithm's parameters are updated. A typical example of a single iteration of training of a neural network would include the following steps: processing the training dataset batch. | |
C9798 | hadoop is an open-source computer code framework used for distributed storage and process of very massive data sets. pig is a high-level platform for making programs that run on Apache Hadoop. The language for this platform is termed Pig Latin. | |
C9799 | By sampling from it randomly, the transitions that build up a batch are decorrelated. It has been shown that this greatly stabilizes and improves the DQN training procedure. A random sampling of the memory bank breaks our sequence, how does that help when you are trying to back-fill a Q (reward) matrix? |
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