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C3600 | Back-propagation is just a way of propagating the total loss back into the neural network to know how much of the loss every node is responsible for, and subsequently updating the weights in such a way that minimizes the loss by giving the nodes with higher error rates lower weights and vice versa. | |
C3601 | Unlike range and quartiles, the variance combines all the values in a data set to produce a measure of spread. It is calculated as the average squared deviation of each number from the mean of a data set. For example, for the numbers 1, 2, and 3 the mean is 2 and the variance is 0.667. | |
C3602 | You can start with a bimodal distribution of data and turn it into a standard normal distribution if you want. | |
C3603 | Covariance: An Overview. Variance and covariance are mathematical terms frequently used in statistics and probability theory. Variance refers to the spread of a data set around its mean value, while a covariance refers to the measure of the directional relationship between two random variables. | |
C3604 | 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. | |
C3605 | Sparse matrix is a matrix which contains very few non-zero elements. For example, consider a matrix of size 100 X 100 containing only 10 non-zero elements. In this matrix, only 10 spaces are filled with non-zero values and remaining spaces of the matrix are filled with zero. | |
C3606 | Spatiotemporal data mining refers to the process of discovering patterns and knowledge from spatiotemporal data. Other examples of moving-object data mining include mining periodic patterns for one or a set of moving objects, and mining trajectory patterns, clusters, models, and outliers. | |
C3607 | An experimental group is a test sample or the group that receives an experimental procedure. This group is exposed to changes in the independent variable being tested. A control group is a group separated from the rest of the experiment such that the independent variable being tested cannot influence the results. | |
C3608 | The difference between true random number generators(TRNGs) and pseudo-random number generators(PRNGs) is that TRNGs use an unpredictable physical means to generate numbers (like atmospheric noise), and PRNGs use mathematical algorithms (completely computer-generated). | |
C3609 | Compared to simple random sampling, stratified sampling has two main disadvantages.Advantages and DisadvantagesA stratified sample can provide greater precision than a simple random sample of the same size.Because it provides greater precision, a stratified sample often requires a smaller sample, which saves money.More items | |
C3610 | Put simply, batch processing is the process by which a computer completes batches of jobs, often simultaneously, in non-stop, sequential order. It's also a command that ensures large jobs are computed in small parts for efficiency during the debugging process. | |
C3611 | 22 thousand | |
C3612 | Binomial is defined as a math term meaning two expressions connected by a plus or minus sign. An example of a binomial is x – y. An example of a binomial is Canis familiaris, the scientific name for dog. | |
C3613 | 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. The rectified linear activation function overcomes the vanishing gradient problem, allowing models to learn faster and perform better. | |
C3614 | In machine learning, however, there's one way to tackle outliers: it's called “one-class classification” (OCC). This involves fitting a model on the “normal” data, and then predicting whether the new data collected is normal or an anomaly. | |
C3615 | The Erlang distribution was developed by A. K. Erlang to examine the number of telephone calls which might be made at the same time to the operators of the switching stations. This work on telephone traffic engineering has been expanded to consider waiting times in queueing systems in general. | |
C3616 | A sampling distribution is where you take a population (N), and find a statistic from that population. This is repeated for all possible samples from the population. Example: You hold a survey about college student's GRE scores and calculate that the standard deviation is 1. | |
C3617 | AdaBoost is one of the first boosting algorithms to be adapted in solving practices. Adaboost helps you combine multiple “weak classifiers” into a single “strong classifier”. → AdaBoost algorithms can be used for both classification and regression problem. | |
C3618 | Streaming Data is data that is generated continuously by thousands of data sources, which typically send in the data records simultaneously, and in small sizes (order of Kilobytes). | |
C3619 | Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. | |
C3620 | classifiers, the time for training classifiers may actually decrease, since the training data set for each classifier is much smaller. . This general method can be extended to give a multiclass formulation of various kinds of linear classifiers. | |
C3621 | The rank-sum test is a non-parametric hypothesis test that can be used to determine if there is a statistically significant association between categorical survey responses provided for two different survey questions. The use of this test is appropriate even when survey sample size is small. | |
C3622 | If your data are missing completely at random, you could consider listwise deletion: just remove the cases with missing values from your analysis. In addition to decision trees, logistic regression is the workhorse in the modelling in order to forecast the occurrence of an event. | |
C3623 | A statistic is biased if it is calculated in such a way that it is systematically different from the population parameter being estimated. The following lists some types of biases, which can overlap. Selection bias involves individuals being more likely to be selected for study than others, biasing the sample. | |
C3624 | For example, Q-learning is an off-policy learner. Q-learning is called off-policy because the updated policy is different from the behavior policy, so Q-Learning is off-policy. In other words, it estimates the reward for future actions and appends a value to the new state without actually following any greedy policy. | |
C3625 | In short, when a dependent variable is not distributed normally, linear regression remains a statistically sound technique in studies of large sample sizes. Figure 2 provides appropriate sample sizes (i.e., >3000) where linear regression techniques still can be used even if normality assumption is violated. | |
C3626 | You can think of an N-gram as the sequence of N words, by that notion, a 2-gram (or bigram) is a two-word sequence of words like “please turn”, “turn your”, or ”your homework”, and a 3-gram (or trigram) is a three-word sequence of words like “please turn your”, or “turn your homework” | |
C3627 | Developers can make use of NLP to perform tasks like speech recognition, sentiment analysis, translation, auto-correct of grammar while typing, and automated answer generation. NLP is a challenging field since it deals with human language, which is extremely diverse and can be spoken in a lot of ways. | |
C3628 | The term Markov chain refers to any system in which there are a certain number of states and given probabilities that the system changes from any state to another state. If it doesn't rain today (N), then there is a 20% chance it will rain tomorrow and 80% chance of no rain. | |
C3629 | The range can also be used to estimate another measure of spread, the standard deviation. Rather than go through a fairly complicated formula to find the standard deviation, we can instead use what is called the range rule. The range is fundamental in this calculation. | |
C3630 | Using too large a batch size can have a negative effect on the accuracy of your network during training since it reduces the stochasticity of the gradient descent. | |
C3631 | K nearest neighbors is a simple algorithm that stores all available cases and predict the numerical target based on a similarity measure (e.g., distance functions). KNN has been used in statistical estimation and pattern recognition already in the beginning of 1970's as a non-parametric technique. | |
C3632 | The usual logic of 2SLS doesn't work the same way for logit, since the underlying regression equations are latent (you only observe a categorical indicator instead of the underlying, interval-scaled response). | |
C3633 | The global facial recognition market size is projected to grow from USD 3.2 billion in 2019 to USD 7.0 billion by 2024, at a CAGR of 16.6% from 2019 to 2024. The major factors driving the market include increased technological advancements across verticals. | |
C3634 | Although both techniques have certain similarities, the difference lies in the fact that classification uses predefined classes in which objects are assigned, while clustering identifies similarities between objects, which it groups according to those characteristics in common and which differentiate them from other | |
C3635 | The squared hinge loss is differentiable because the term from the chain rule forces the limits to converge to the same number from both sides. | |
C3636 | Time Series Forecast in RStep 1: Reading data and calculating basic summary. Step 2: Checking the cycle of Time Series Data and Plotting the Raw Data. Step 3: Decomposing the time series data. Step 4: Test the stationarity of data. Step 5: Fitting the model. Step 6: Forecasting. | |
C3637 | The product moment correlation coefficient (pmcc) can be used to tell us how strong the correlation between two variables is. A positive value indicates a positive correlation and the higher the value, the stronger the correlation. If there is a perfect negative correlation, then r = -1. | |
C3638 | Difficulties in NLU Syntax Level ambiguity − A sentence can be parsed in different ways. For example, “He lifted the beetle with red cap.” − Did he use cap to lift the beetle or he lifted a beetle that had red cap? Referential ambiguity − Referring to something using pronouns. For example, Rima went to Gauri. | |
C3639 | Definition: The trend is the component of a time series that represents variations of low frequency in a time series, the high and medium frequency fluctuations having been filtered out. | |
C3640 | Conclusion. Human intelligence revolves around adapting to the environment using a combination of several cognitive processes. The field of Artificial intelligence focuses on designing machines that can mimic human behavior. However, AI researchers are able to go as far as implementing Weak AI, but not the Strong AI. | |
C3641 | A matrix is a linear operator acting on the vector space of column vectors. Per linear algebra and its isomorphism theorems, any vector space is isomorphic to any other vector space of the same dimension. As such, matrices can be seen as representations of linear operators subject to some basis of column vectors. | |
C3642 | Statistical inference consists in the use of statistics to draw conclusions about some unknown aspect of a population based on a random sample from that population. Point estimation is discussed in the statistics section of the encyclopedia. | |
C3643 | Conversely, according to the fundamental theorem of calculus, Eq. (1.7), p(x) = F′(x). Thus, the probability density is the derivative of the cumulative distribution function. This in turn implies that the probability density is always nonnegative, p(x) ≥ 0, because F is monotone increasing. | |
C3644 | Reduce Variance of an Estimate If we want to reduce the amount of variance in a prediction, we must add bias. Consider the case of a simple statistical estimate of a population parameter, such as estimating the mean from a small random sample of data. A single estimate of the mean will have high variance and low bias. | |
C3645 | Keras is a high-level interface and uses Theano or Tensorflow for its backend. It runs smoothly on both CPU and GPU. Keras supports almost all the models of a neural network – fully connected, convolutional, pooling, recurrent, embedding, etc. Furthermore, these models can be combined to build more complex models. | |
C3646 | 0:003:12Suggested clip · 104 secondsBeta distribution: mean - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C3647 | The False Discovery Rate approach is a more recent development. This approach also determines adjusted p-values for each test. An FDR adjusted p-value (or q-value) of 0.05 implies that 5% of significant tests will result in false positives. The latter will result in fewer false positives. | |
C3648 | A feature map is formed by different units in a CNN that share the same weights and biases. For example: Basically they are feature extractors/filters learned through training. When convolved with the input and passed through the activation function, they generate meaningful inputs for the next layer or output. | |
C3649 | For distributions that are strongly skewed or have outliers, the median is often the most appropriate measure of central tendency because in skewed distributions the mean is pulled out toward the tail. The median is more resistant to outliers compared to the mean. | |
C3650 | In other words, stream processing receives and analyses data in a continuous stream without delays. In the past, data was stored in a database and prepped for analysis. Stream processing allows users to skip storage and go straight into analysis allowing users to gain insights at a faster rate than before. | |
C3651 | Fourier Methods in Signal Processing The Fourier transform and discrete-time Fourier transform are mathematical analysis tools and cannot be evaluated exactly in a computer. The Fourier transform is used to analyze problems involving continuous-time signals or mixtures of continuous- and discrete-time signals. | |
C3652 | Summing up, a more precise statement of the universality theorem is that neural networks with a single hidden layer can be used to approximate any continuous function to any desired precision. | |
C3653 | If there are only two variables, one is continuous and another one is categorical, theoretically, it would be difficult to capture the correlation between these two variables. | |
C3654 | Average-linkage is where the distance between each pair of observations in each cluster are added up and divided by the number of pairs to get an average inter-cluster distance. Average-linkage and complete-linkage are the two most popular distance metrics in hierarchical clustering. | |
C3655 | It repetitively leverages the patterns in residuals, strengthens the model with weak predictions, and make it better. By combining the advantages from both random forest and gradient boosting, XGBoost gave the a prediction error ten times lower than boosting or random forest in my case. | |
C3656 | The amount that the weights are updated during training is referred to as the step size or the “learning rate.” Specifically, the learning rate is a configurable hyperparameter used in the training of neural networks that has a small positive value, often in the range between 0.0 and 1.0. | |
C3657 | The mean is the average of a group of numbers, and the variance measures the average degree to which each number is different from the mean. | |
C3658 | 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. | |
C3659 | Word embedding and topic modeling come from two different research communities. Word embeddings come from the neural net research tradition, while topic modelings come from Bayesian model research tradition. Word embedding can be used to improve topic models like Lda2Vec. | |
C3660 | A confusion matrix is a table that is often used to describe the performance of a classification model (or "classifier") on a set of test data for which the true values are known. The confusion matrix itself is relatively simple to understand, but the related terminology can be confusing. | |
C3661 | The sampling frequency or sampling rate, fs, is the average number of samples obtained in one second (samples per second), thus fs = 1/T. | |
C3662 | Creative Ways to Benefit From Social Media AnalyticsEngage Better With Your Audience. Many businesses have a hard time keeping up with the vast amount of social media activity that impacts their brand. Improve Customer Relations. Monitor Your Competition. Identify and Engage With Your Top Customers. Find Out Where Your Industry is Heading. | |
C3663 | In artificial intelligence, an intelligent agent (IA) refers to an autonomous entity which acts, directing its activity towards achieving goals (i.e. it is an agent), upon an environment using observation through sensors and consequent actuators (i.e. it is intelligent). | |
C3664 | The short answer is yes—because most regression models will not perfectly fit the data at hand. If you need a more complex model, applying a neural network to the problem can provide much more prediction power compared to a traditional regression. | |
C3665 | When working with a measurement variable, the Kruskal–Wallis test starts by substituting the rank in the overall data set for each measurement value. The smallest value gets a rank of 1, the second-smallest gets a rank of 2, etc. | |
C3666 | A modern approach to reducing generalization error is to use a larger model that may be required to use regularization during training that keeps the weights of the model small. These techniques not only reduce overfitting, but they can also lead to faster optimization of the model and better overall performance. | |
C3667 | While the trials are independent, their outcomes X are dependent because they must be summed to n. ; in this form, a categorical distribution is equivalent to a multinomial distribution over a single trial. | |
C3668 | The arithmetic mean is appropriate if the values have the same units, whereas the geometric mean is appropriate if the values have differing units. The harmonic mean is appropriate if the data values are ratios of two variables with different measures, called rates. | |
C3669 | Asynchronous data is data that is not synchronized when it is sent or received. This usually refers to data that is transmitted at intermittent intervals rather than in a steady stream, which means that the first parts of the complete file might not always be the first to be sent and arrive at the destination. | |
C3670 | : being or having the shape of a normal curve or a normal distribution. | |
C3671 | A Gaussian filter is a linear filter. It's usually used to blur the image or to reduce noise. If you use two of them and subtract, you can use them for "unsharp masking" (edge detection). The Gaussian filter alone will blur edges and reduce contrast. | |
C3672 | In this technique, multiple models are used to make predictions for each data point. The predictions by each model are considered as a separate vote. The prediction which we get from the majority of the models is used as the final prediction. | |
C3673 | The item response theory (IRT), also known as the latent response theory refers to a family of mathematical models that attempt to explain the relationship between latent traits (unobservable characteristic or attribute) and their manifestations (i.e. observed outcomes, responses or performance). | |
C3674 | Boosting is an ensemble modeling technique which attempts to build a strong classifier from the number of weak classifiers. It is done building a model by using weak models in series. AdaBoost was the first really successful boosting algorithm developed for the purpose of binary classification. | |
C3675 | Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching and recognition developed by David Lowe (1999, 2004). The SIFT descriptor has also been extended from grey-level to colour images and from 2-D spatial images to 2+1-D spatio-temporal video. | |
C3676 | Topic modelling refers to the task of identifying topics that best describes a set of documents. And the goal of LDA is to map all the documents to the topics in a way, such that the words in each document are mostly captured by those imaginary topics. | |
C3677 | Data Science vs Business Analytics, often used interchangeably, are very different domains. Simply put, Data science is the study of Data using statistics which provides key insights but not business changing decisions whereas Business Analytics is the analysis of data to make key business decisions for the company. | |
C3678 | The Euclidean distance corresponds to the L2-norm of a difference between vectors. The cosine similarity is proportional to the dot product of two vectors and inversely proportional to the product of their magnitudes. | |
C3679 | Cross-entropy can be calculated using the probabilities of the events from P and Q, as follows: H(P, Q) = – sum x in X P(x) * log(Q(x)) | |
C3680 | Deep learning requires large amounts of labeled data. For example, driverless car development requires millions of images and thousands of hours of video. Deep learning requires substantial computing power. High-performance GPUs have a parallel architecture that is efficient for deep learning. | |
C3681 | In qualitative research no hypotheses or relationships of variables are tested. Because variables must be defined numerically in hypothesis-testing research, they cannot reflect subjective experience. This leads to hypothesis-generating research using the grounded theory method to study subjective experience directly. | |
C3682 | In contrast to the non-stationary process that has a variable variance and a mean that does not remain near, or returns to a long-run mean over time, the stationary process reverts around a constant long-term mean and has a constant variance independent of time. | |
C3683 | An independent variable, sometimes called an experimental or predictor variable, is a variable that is being manipulated in an experiment in order to observe the effect on a dependent variable, sometimes called an outcome variable. | |
C3684 | Statistical significance is a determination by an analyst that the results in the data are not explainable by chance alone. A p-value of 5% or lower is often considered to be statistically significant. | |
C3685 | Arithmetic mean is calculated by dividing the sum of the numbers by number count. However, Geometric means takes into account the compounding effect while calculation. | |
C3686 | Distance MatrixThe proximity between object can be measured as distance matrix. For example, distance between object A = (1, 1) and B = (1.5, 1.5) is computed as.Another example of distance between object D = (3, 4) and F = (3, 3.5) is calculated as.More items | |
C3687 | Artificial Intelligence (AI) is the ability for an artificial machine to act intelligently. Logic Programming is a method that computer scientists are using to try to allow machines to reason because it is useful for knowledge representation. The diagram below shows the essence of logic programming. | |
C3688 | The only goal PCA and other dimension reduction techniques accomplish is just that; reducing the dimensions of your feature space, thus driving down computational cost and time. Whether or not you decide to normalize the data is a completely independent matter. In short, don't skip on normalization. | |
C3689 | A statistical model is a mathematical representation (or mathematical model) of observed data. When data analysts apply various statistical models to the data they are investigating, they are able to understand and interpret the information more strategically. | |
C3690 | A variable whose Output property is Yes is an output variable. When the script runs, any value assigned to the variable is saved for use outside of the script. Its value is output to external storage when the script executes. | |
C3691 | 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). | |
C3692 | spark. mllib is the first of the two Spark APIs while org.apache.spark.ml is the new API. mllib carries the original API built on top of RDDs. spark.ml contains higher-level API built on top of DataFrames for constructing ML pipelines. | |
C3693 | In general, as sample size increases, the difference between expected adjusted r-squared and expected r-squared approaches zero; in theory this is because expected r-squared becomes less biased. the standard error of adjusted r-squared would get smaller approaching zero in the limit. | |
C3694 | A decision tree is a simple representation for classifying examples. For this section, assume that all of the input features have finite discrete domains, and there is a single target feature called the "classification". Each element of the domain of the classification is called a class. | |
C3695 | As you prepare to conduct your statistics, it is important to consider testing the assumptions that go with your analysis. Assumption testing of your chosen analysis allows you to determine if you can correctly draw conclusions from the results of your analysis. | |
C3696 | A probability density plot simply means a density plot of probability density function (Y-axis) vs data points of a variable (X-axis). By showing probability density plots, we're only able to understand the distribution of data visually without knowing the exact probability for a certain range of values. | |
C3697 | Multivariate ANOVA (MANOVA) extends the capabilities of analysis of variance (ANOVA) by assessing multiple dependent variables simultaneously. ANOVA statistically tests the differences between three or more group means. This statistical procedure tests multiple dependent variables at the same time. | |
C3698 | Verify that the partial derivative Fxy is correct by calculating its equivalent, Fyx, taking the derivatives in the opposite order (d/dy first, then d/dx). In the above example, the derivative d/dy of the function f(x,y) = 3x^2*y - 2xy is 3x^2 - 2x. | |
C3699 | The significance of Matrix is - they represent Linear transformations like rotation/scaling. A Matrix is just a stack of numbers - but very special - you can add them and subtract them and multiply them [restrictions]. The significance of Matrix is - they represent Linear transformations like rotation/scaling. |
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