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C1300 | Instead, we uncover a more fundamental impact of BatchNorm on the training process: it makes the optimization landscape significantly smoother. This smoothness induces a more predictive and stable behavior of the gradients, allowing for faster training. | |
C1301 | Hashing is the transformation of a string of characters into a usually shorter fixed-length value or key that represents the original string. Hashing is used to index and retrieve items in a database because it is faster to find the item using the shorter hashed key than to find it using the original value. | |
C1302 | Hyperplanes are decision boundaries that help classify the data points. Data points falling on either side of the hyperplane can be attributed to different classes. | |
C1303 | The effect size is the main finding of a quantitative study. While a P value can inform the reader whether an effect exists, the P value will not reveal the size of the effect. | |
C1304 | An affine function is the composition of a linear function with a translation, so while the linear part fixes the origin, the translation can map it somewhere else. While affine functions don't preserve the origin, they do preserve some of the other geometry of the space, such as the collection of straight lines. | |
C1305 | Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. Interrupted time series analysis is the analysis of interventions on a single time series. Time series data have a natural temporal ordering. | |
C1306 | In Gradient Descent or Batch Gradient Descent, we use the whole training data per epoch whereas, in Stochastic Gradient Descent, we use only single training example per epoch and Mini-batch Gradient Descent lies in between of these two extremes, in which we can use a mini-batch(small portion) of training data per epoch | |
C1307 | 3 layers | |
C1308 | 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. | |
C1309 | Well, if you break down the words, forward implies moving ahead and propagation is a term for saying spreading of anything. forward propagation means we are moving in only one direction, from input to the output, in a neural network. | |
C1310 | Linear least squares regression is by far the most widely used modeling method. It is what most people mean when they say they have used "regression", "linear regression" or "least squares" to fit a model to their data. | |
C1311 | In mathematics, a system of equations is considered overdetermined if there are more equations than unknowns. An overdetermined system is almost always inconsistent (it has no solution) when constructed with random coefficients. Such systems usually have an infinite number of solutions. | |
C1312 | approximation of the expected error called the empirical error which is the average error on the. training set. Given a function f, a loss function V , and a training set S consisting of n data points, the empirical error of f is: IS[f] = | |
C1313 | In general, an LSTM can be used for classification or regression; it is essentially just a standard neural network that takes as input, in addition to input from that time step, a hidden state from the previous time step. So, just as a NN can be used for classification or regression, so can an LSTM. | |
C1314 | Probability distributions are a fundamental concept in statistics. They are used both on a theoretical level and a practical level. Some practical uses of probability distributions are: To calculate confidence intervals for parameters and to calculate critical regions for hypothesis tests. | |
C1315 | Dimensionality reduction refers to techniques that reduce the number of input variables in a dataset. Large numbers of input features can cause poor performance for machine learning algorithms. Dimensionality reduction is a general field of study concerned with reducing the number of input features. | |
C1316 | The output of the network is a single vector (also with 10,000 components) containing, for every word in our vocabulary, the probability that a randomly selected nearby word is that vocabulary word. In word2vec, a distributed representation of a word is used. | |
C1317 | Unbiasedness implies consistency, whereas a consistent estimator can be biased. | |
C1318 | Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning. | |
C1319 | Hashing is the practice of using an algorithm to map data of any size to a fixed length. This is called a hash value (or sometimes hash code or hash sums or even a hash digest if you're feeling fancy). Whereas encryption is a two-way function, hashing is a one-way function. Every hash value is unique. | |
C1320 | For a dichotomous categorical variable and a continuous variable you can calculate a Pearson correlation if the categorical variable has a 0/1-coding for the categories. But when you have more than two categories for the categorical variable the Pearson correlation is not appropriate anymore. | |
C1321 | Observer bias and other “experimenter effects” occur when researchers' expectations influence study outcome. To minimize bias, it is good practice to work “blind,” meaning that experimenters are unaware of the identity or treatment group of their subjects while conducting research. | |
C1322 | What factors inhibit collective intelligence?In-group bias. Out-group homogeneity bias. Groupthink, bandwagon effect, herd behavior. Facilitation and loafing . Group polarization. Biased use of information and the common knowledge effect. Risky shift. Distortions in multi-level group decisions. | |
C1323 | Linear mixed models (sometimes called “multilevel models” or “hierarchical models”, depending on the context) are a type of regression model that take into account both (1) variation that is explained by the independent variables of interest (like lm() ) – fixed effects, and (2) variation that is not explained by the | |
C1324 | Uncertainty is a popular phenomenon in machine learning and a variety of methods to model uncertainty at different levels has been developed. Different types of uncertainty can be observed: (i) Input data are subject to noise, outliers, and errors. | |
C1325 | 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. | |
C1326 | In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. | |
C1327 | EXC functions both find a requested quartile of a supplied data set. The difference between these two functions is that QUARTILE. INC bases its calculation on a percentile range of 0 to 1 inclusive, whereas QUARTILE. EXC bases its calculation on a percentile range of 0 to 1 exclusive. | |
C1328 | Origin of the term. The earliest reference to the concept of a confusion matrix appears to have been made by Karl Pearson in 1904 “on the Theory of Contingency and Its Relation to Association and Normal Correlation” [3]. | |
C1329 | Model specification refers to the determination of which independent variables should be included in or excluded from a regression equation. A multiple regression model is, in fact, a theoretical statement about the causal relationship between one or more independent variables and a dependent variable. | |
C1330 | If skewness is negative, the data are negatively skewed or skewed left, meaning that the left tail is longer. If skewness = 0, the data are perfectly symmetrical. If skewness is less than −1 or greater than +1, the distribution is highly skewed. | |
C1331 | A recurrent neural network (RNN) is a type of artificial neural network commonly used in speech recognition and natural language processing (NLP). RNNs are designed to recognize a data's sequential characteristics and use patterns to predict the next likely scenario. | |
C1332 | Example 1: Draw a box-and-whisker plot for the data set {3, 7, 8, 5, 12, 14, 21, 13, 18}. The box part represents the interquartile range and represents approximately the middle 50% of all the data. The data is divided into four regions, which each represent approximately 25% of the data. | |
C1333 | Order the values of a data set of size n from smallest to largest. If n is odd, the sample median is the value in position (n + 1)/2; if n is even, it is the average of the values in positions n/2 and n/2 + 1. | |
C1334 | BFS vs DFS BFS stands for Breadth First Search. DFS stands for Depth First Search. 2. BFS(Breadth First Search) uses Queue data structure for finding the shortest path. DFS(Depth First Search) uses Stack data structure. | |
C1335 | Note the difference between parameters and arguments: Function parameters are the names listed in the function's definition. Function arguments are the real values passed to the function. Parameters are initialized to the values of the arguments supplied. | |
C1336 | The expression double standard originally referred to 18th- and 19th-century economic policies of bimetallism. Bimetallism was a monetary system that was based on two metals—a double standard, in its financial “prescribed value” sense, of gold and silver. | |
C1337 | The standardized mean difference (SMD) measure of effect is used when studies report efficacy in terms of a continuous measurement, such as a score on a pain-intensity rating scale. The SMD is also known as Cohen's d. The SMD is a point estimate of the effect of a treatment. | |
C1338 | Mentor: Well, if the line is a good fit for the data then the residual plot will be random. However, if the line is a bad fit for the data then the plot of the residuals will have a pattern. | |
C1339 | Bivariate analysis is one of the simplest forms of quantitative (statistical) analysis. It involves the analysis of two variables (often denoted as X, Y), for the purpose of determining the empirical relationship between them. Bivariate analysis can be helpful in testing simple hypotheses of association. | |
C1340 | 2 Answers. Normalization would be required if you are doing some form a similarity measurement. Dummy variables by its nature acts as a binary switch. Usually, normalization is used when the variables are measured on different scales such that a proper comparison is not possible. | |
C1341 | 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. | |
C1342 | Getting Familiar with ML Pipelines A machine learning pipeline is used to help automate machine learning workflows. They operate by enabling a sequence of data to be transformed and correlated together in a model that can be tested and evaluated to achieve an outcome, whether positive or negative. | |
C1343 | 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. | |
C1344 | Top 10 Free Resources To Learn Reinforcement Learning1| Reinforcement Learning Explained. Source: edX. 2| Reinforcement Learning. 3| Advanced Deep Learning & Reinforcement Learning. 4| Deep Reinforcement Learning. 5| An Introduction to Reinforcement Learning. 6| An Introduction to Reinforcement Learning. 8| Reinforcement Learning Specialisation. 9| Reinforcement Learning.More items• | |
C1345 | Credible intervals capture our current uncertainty in the location of the parameter values and thus can be interpreted as probabilistic statement about the parameter. In contrast, confidence intervals capture the uncertainty about the interval we have obtained (i.e., whether it contains the true value or not). | |
C1346 | Adam is an optimization algorithm that can be used instead of the classical stochastic gradient descent procedure to update network weights iterative based in training data. | |
C1347 | An almost essential property is that the estimator should be consistent: T is a consistent estimator of θ if T converges to θ in probability as n → ∞. Consistency implies that, as the sample size increases, any bias in T tends to 0 and the variance of T also tends to 0. | |
C1348 | SVM is a supervised machine learning algorithm which can be used for classification or regression problems. It uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputs. | |
C1349 | A recurrent neural network is shown one input each timestep and predicts one output. Conceptually, BPTT works by unrolling all input timesteps. Each timestep has one input timestep, one copy of the network, and one output. Errors are then calculated and accumulated for each timestep. | |
C1350 | First, let's review how to calculate the population standard deviation:Calculate the mean (simple average of the numbers).For each number: Subtract the mean. Square the result.Calculate the mean of those squared differences. Take the square root of that to obtain the population standard deviation. | |
C1351 | In statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a | |
C1352 | Markov Chain Monte Carlo (MCMC) methods are a class of algorithms for sampling from a probability distribution based on constructing a Markov chain that has the desired distribution as its stationary distribution. The state of the chain after a number of steps is then used as a sample of the desired distribution. | |
C1353 | Alternate-form reliability is the consistency of test results between two different – but equivalent – forms of a test. Alternate-form reliability is used when it is necessary to have two forms of the same tests. – Alternative-form reliability is needed whenever two test forms are being used to measure the same thing. | |
C1354 | The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high-dimensional spaces that do not occur in low-dimensional settings such as the three-dimensional physical space of everyday experience. The expression was coined by Richard E. | |
C1355 | ❖ The variable that is used to explain or predict the response variable is called the explanatory variable. It is also sometimes called the independent variable because it is independent of the other variable. ▪ In regression, the order of the variables is very important. | |
C1356 | Gaussian elimination for solving an n × n linear system of equations Ax = b is the archetypal direct method of numerical linear algebra. In this note we point out that GE has an iterative side too. It is now one of the mainstays of computational science—the archetypal iterative method. | |
C1357 | Numeric Outlier is the simplest, nonparametric outlier detection technique in a one-dimensional feature space. The outliers are calculated by means of the IQR (InterQuartile Range). Using the interquartile multiplier value k=1.5, the range limits are the typical upper and lower whiskers of a box plot. | |
C1358 | The input() function accepts an optional string argument called prompt and returns a string. Note that the input() function always returns a string even if you entered a number. To convert it to an integer you can use int() or eval() functions. | |
C1359 | A probability sampling method is any method of sampling that utilizes some form of random selection. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen. | |
C1360 | A continuous variable is one which can take on a value between any other two values, such as: indoor temperature, time spent waiting, water consumed, color wavelength, and direction of travel. A discrete variable corresponds to a digital quantity, while a continuous variable corresponds to an analog quantity. | |
C1361 | FAQ Explanation: Volumetric efficiency is the ratio of the volume of charge admitted at N.T.P. to the swept volume of the piston while mechanical efficiency is the ratio of the brake power to the indicated power and relative efficiency is the ratio of the indicated thermal efficiency to the air standard efficiency | |
C1362 | @shuvayan - Theoretically, 25 to 30% is the maximum missing values are allowed, beyond which we might want to drop the variable from analysis. Practically this varies.At times we get variables with ~50% of missing values but still the customer insist to have it for analyzing. | |
C1363 | Factor analysis is as much of a "test" as multiple regression (or statistical tests in general) in that it is used to reveal hidden or latent relationships/groupings in one's dataset. Multiple regression takes data points in some n-dimensional space and finds the best fit line. | |
C1364 | Reinforcement learning enables the learning of optimal behavior in tasks that require the selection of sequential actions. Through repeated interactions with the environment, and the receipt of rewards, the agent learns which actions are associated with the greatest cumulative reward. | |
C1365 | 5:1310:53Suggested clip · 105 secondsStochastic Gradient Descent, Clearly Explained!!! - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C1366 | Regression is a statistical method used in finance, investing, and other disciplines that attempts to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables (known as independent variables). | |
C1367 | OLS does not require that the error term follows a normal distribution to produce unbiased estimates with the minimum variance. However, satisfying this assumption allows you to perform statistical hypothesis testing and generate reliable confidence intervals and prediction intervals. | |
C1368 | Using the Interquartile Rule to Find Outliers Multiply the interquartile range (IQR) by 1.5 (a constant used to discern outliers). Add 1.5 x (IQR) to the third quartile. Any number greater than this is a suspected outlier. Subtract 1.5 x (IQR) from the first quartile. | |
C1369 | Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future. | |
C1370 | How to find the mean of the probability distribution: StepsStep 1: Convert all the percentages to decimal probabilities. For example: Step 2: Construct a probability distribution table. Step 3: Multiply the values in each column. Step 4: Add the results from step 3 together. | |
C1371 | Nonparametric statistics refers to a statistical method in which the data are not assumed to come from prescribed models that are determined by a small number of parameters; examples of such models include the normal distribution model and the linear regression model. | |
C1372 | Conditional probability is probability of a second event given a first event has already occurred. This is conditional probability with two dependent events. A dependent event is when one event influences the outcome of another event in a probability scenario. | |
C1373 | Key Takeaways. Standard deviation looks at how spread out a group of numbers is from the mean, by looking at the square root of the variance. The variance measures the average degree to which each point differs from the mean—the average of all data points. | |
C1374 | The probability of the intersection of Events A and B is denoted by P(A ∩ B). If Events A and B are mutually exclusive, P(A ∩ B) = 0. The probability that Events A or B occur is the probability of the union of A and B. | |
C1375 | Correlation Coefficient Equation The correlation coefficient is determined by dividing the covariance by the product of the two variables' standard deviations. Standard deviation is a measure of the dispersion of data from its average. | |
C1376 | Discriminant analysis is a technique that is used by the researcher to analyze the research data when the criterion or the dependent variable is categorical and the predictor or the independent variable is interval in nature. | |
C1377 | NLP is short for natural language processing while NLU is the shorthand for natural language understanding. Similarly named, the concepts both deal with the relationship between natural language (as in, what we as humans speak, not what computers understand) and artificial intelligence. | |
C1378 | Big data analytics as the name suggest is the analysis of big data by discovering hidden patterns or extracting information from it. Big data has got more to do with High-Performance Computing, while Machine Learning is a part of Data Science. Machine learning performs tasks where human interaction doesn't matter. | |
C1379 | A vector is a quantity or phenomenon that has two independent properties: magnitude and direction. The term also denotes the mathematical or geometrical representation of such a quantity. A quantity or phenomenon that exhibits magnitude only, with no specific direction, is called a scalar . | |
C1380 | Natural Language Processing (NLP) is the part of AI that studies how machines interact with human language. Combined with machine learning algorithms, NLP creates systems that learn to perform tasks on their own and get better through experience. | |
C1381 | you can use this formula [(W−K+2P)/S]+1 .W is the input volume - in your case 128.K is the Kernel size - in your case 5.P is the padding - in your case 0 i believe.S is the stride - which you have not provided. | |
C1382 | Sentiment Analysis is a procedure used to determine if a chunk of text is positive, negative or neutral. In text analytics, natural language processing (NLP) and machine learning (ML) techniques are combined to assign sentiment scores to the topics, categories or entities within a phrase. | |
C1383 | For skewed distributions, it is quite common to have one tail of the distribution considerably longer or drawn out relative to the other tail. A "skewed right" distribution is one in which the tail is on the right side. A "skewed left" distribution is one in which the tail is on the left side. | |
C1384 | Data bias in machine learning is a type of error in which certain elements of a dataset are more heavily weighted and/or represented than others. A biased dataset does not accurately represent a model's use case, resulting in skewed outcomes, low accuracy levels, and analytical errors. | |
C1385 | Probability is the study of random events. It is used in analyzing games of chance, genetics, weather prediction, and a myriad of other everyday events. Statistics is the mathematics we use to collect, organize, and interpret numerical data. | |
C1386 | 8 Methods to Boost the Accuracy of a ModelAdd more data. Having more data is always a good idea. Treat missing and Outlier values. Feature Engineering. Feature Selection. Multiple algorithms. Algorithm Tuning. Ensemble methods. | |
C1387 | Active learning: Reinforces important material, concepts, and skills. Provides more frequent and immediate feedback to students. Provides students with an opportunity to think about, talk about, and process course material. | |
C1388 | An endogenous variable is a variable in a statistical model that's changed or determined by its relationship with other variables within the model. In other words, an endogenous variable is synonymous with a dependent variable, meaning it correlates with other factors within the system being studied. | |
C1389 | Like a standard normal distribution (or z-distribution), the t-distribution has a mean of zero. The normal distribution assumes that the population standard deviation is known. The t-distribution does not make this assumption. | |
C1390 | Poisson Formula. P(x; μ) = (e-μ) (μx) / x! where x is the actual number of successes that result from the experiment, and e is approximately equal to 2.71828. The Poisson distribution has the following properties: The mean of the distribution is equal to μ . The variance is also equal to μ . | |
C1391 | Multi-label classification is a type of classification in which an object can be categorized into more than one class. For example, In the above dataset, we will classify a picture as the image of a dog or cat and also classify the same image based on the breed of the dog or cat. | |
C1392 | Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. The GRU is like a long short-term memory (LSTM) with a forget gate, but has fewer parameters than LSTM, as it lacks an output gate. | |
C1393 | The Poisson distribution is used to model the number of events occurring within a given time interval. λ is the shape parameter which indicates the average number of events in the given time interval. The following is the plot of the Poisson probability density function for four values of λ. | |
C1394 | In an economic model, an exogenous variable is one whose value is determined outside the model and is imposed on the model, and an exogenous change is a change in an exogenous variable. In contrast, an endogenous variable is a variable whose value is determined by the model. | |
C1395 | The difference between interval and ratio scales comes from their ability to dip below zero. Interval scales hold no true zero and can represent values below zero. For example, you can measure temperature below 0 degrees Celsius, such as -10 degrees. Ratio variables, on the other hand, never fall below zero. | |
C1396 | Inverted dropout is a variant of the original dropout technique developed by Hinton et al. The one difference is that, during the training of a neural network, inverted dropout scales the activations by the inverse of the keep probability q=1−p q = 1 − p . | |
C1397 | The difference is a matter of emphasis. The joint distribution depends on some unknown parameters. So the model you are using is a joint density function where is the total number of measurements and is the total number of parameters. That's the likelihood function. | |
C1398 | Among the learning algorithms, one of the most popular and easiest to understand is the decision tree induction. The popularity of this method is related to three nice characteristics: interpretability, efficiency, and flexibility. Decision tree can be used for both classification and regression kind of problem. | |
C1399 | Under simple random sampling, a sample of items is chosen randomly from a population, and each item has an equal probability of being chosen. Meanwhile, systematic sampling involves selecting items from an ordered population using a skip or sampling interval. |
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