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C5400 | A simple linear regression plot for amount of rainfall. Regression analysis is used in stats to find trends in data. For example, you might guess that there's a connection between how much you eat and how much you weigh; regression analysis can help you quantify that. | |
C5401 | Sampling error is the difference between a population parameter and a sample statistic used to estimate it. For example, the difference between a population mean and a sample mean is sampling error. Sampling error occurs because a portion, and not the entire population, is surveyed.… | |
C5402 | In a crossover network, resistors are usually used in combination with other components to control either impedance magnitudes or the relative levels between different drivers in a system. | |
C5403 | In a normal distribution the mean is zero and the standard deviation is 1. It has zero skew and a kurtosis of 3. Normal distributions are symmetrical, but not all symmetrical distributions are normal. | |
C5404 | Definition: Two events, A and B, are independent if the fact that A occurs does not affect the probability of B occurring. Some other examples of independent events are: Landing on heads after tossing a coin AND rolling a 5 on a single 6-sided die. Choosing a marble from a jar AND landing on heads after tossing a coin. | |
C5405 | The Q-learning algorithm ProcessStep 1: Initialize Q-values. Step 2: For life (or until learning is stopped) Step 3: Choose an action. Steps 4–5: Evaluate! Step 1: We init our Q-table.Step 2: Choose an action. Steps 4–5: Update the Q-function. | |
C5406 | A learning curve is a plot of model learning performance over experience or time. Learning curves are a widely used diagnostic tool in machine learning for algorithms that learn from a training dataset incrementally. Learning curves are plots that show changes in learning performance over time in terms of experience. | |
C5407 | A permutation test5 is used to determine the statistical significance of a model by computing a test statistic on the dataset and then for many random permutations of that data. If the model is significant, the original test statistic value should lie at one of the tails of the null hypothesis distribution. | |
C5408 | 4 neurons | |
C5409 | The main advantage of using the ReLU function over other activation functions is that it does not activate all the neurons at the same time. Due to this reason, during the backpropogation process, the weights and biases for some neurons are not updated. This can create dead neurons which never get activated. | |
C5410 | Discriminant analysis is statistical technique used to classify observations into non-overlapping groups, based on scores on one or more quantitative predictor variables. For example, a doctor could perform a discriminant analysis to identify patients at high or low risk for stroke. | |
C5411 | Multiple regression formula is used in the analysis of relationship between dependent and multiple independent variables and formula is represented by the equation Y is equal to a plus bX1 plus cX2 plus dX3 plus E where Y is dependent variable, X1, X2, X3 are independent variables, a is intercept, b, c, d are slopes, | |
C5412 | The Dirichlet is the multivariate generalization of the beta distribution. The Dirichlet equals the uniform distribution when all parameters (α1… αk) are equal. The Dirichlet distribution is a conjugate prior to the categorical distribution and multinomial distributions. A compound variant is the Dirichlet-multinomial. | |
C5413 | Hyperparameter optimization is a big part of deep learning. The reason is that neural networks are notoriously difficult to configure and there are a lot of parameters that need to be set. On top of that, individual models can be very slow to train. | |
C5414 | Now, let the random variable X represent the number of Heads that result from this experiment. The random variable X can only take on the values 0, 1, or 2, so it is a discrete random variable. | |
C5415 | SummaryWeighted Mean: A mean where some values contribute more than others.When the weights add to 1: just multiply each weight by the matching value and sum it all up.Otherwise, multiply each weight w by its matching value x, sum that all up, and divide by the sum of weights: Weighted Mean = ΣwxΣw. | |
C5416 | The main difference between stratified sampling and cluster sampling is that with cluster sampling, you have natural groups separating your population. With stratified random sampling, these breaks may not exist*, so you divide your target population into groups (more formally called "strata"). | |
C5417 | Bootstrap is a potent front-end framework used to create modern websites and web apps. It's open-source and free to use, yet features numerous HTML and CSS templates for UI interface elements such as buttons and forms. Bootstrap also supports JavaScript extensions. | |
C5418 | Canonical Correspondence Analysis (CCA) has been developed to allow ecologists to relate the abundance of species to environmental variables (Ter Braak, 1986). However, this method can be used in other domains. A table Y of descriptive variables that are measured on the same sites. | |
C5419 | The random (or precision) error for this data point is defined as the reading minus the average of readings, or -1.20 - (-1.42) = 0.22oC. Thus, the maximum absolute value of random error is 0.22oC. You can verify that the magnitude of the random error for any of the other data points is less than this. | |
C5420 | The batch size limits the number of samples to be shown to the network before a weight update can be performed. This same limitation is then imposed when making predictions with the fit model. Specifically, the batch size used when fitting your model controls how many predictions you must make at a time. | |
C5421 | Due to its mathematical complexity, the theoretical foundations of neural network are not covered. However, the universal approximation theorem (and the tools used in its proof) give a very deep insight into why neural networks are so powerful, and it even lays the groundwork for engineering novel architectures. | |
C5422 | In layman terms, vectors have magnitude and direction and follow the laws of vector addition. The only difference is that tensor is the generalized form of scalars and vectors . Means scalars and vectors are the special cases of tensor quantities. Scalar is a tensor of rank 0 and vector is a tensor of rank 1. | |
C5423 | “Machine learning is essentially a form of applied statistics” “Machine learning is glorified statistics” “Machine learning is statistics scaled up to big data” “The short answer is that there is no difference” | |
C5424 | Cost function(J) of Linear Regression is the Root Mean Squared Error (RMSE) between predicted y value (pred) and true y value (y). Gradient Descent: To update θ1 and θ2 values in order to reduce Cost function (minimizing RMSE value) and achieving the best fit line the model uses Gradient Descent. | |
C5425 | Deep learning neural networks are trained using the stochastic gradient descent optimization algorithm. The learning rate is a hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated. | |
C5426 | The two types of growth curves that are most common are logarithmic growth curves and exponential growth curves. Essentially, they are the opposite of each other. I'll start by explaining and exponential growth curve as that is the one people are typically more familiar with. | |
C5427 | Nonlinear correlation can be detected by maximal local correlation (M = 0.93, p = 0.007), but not by Pearson correlation (C = –0.08, p = 0.88) between genes Pla2g7 and Pcp2 (i.e., between two columns of the distance matrix). Pla2g7 and Pcp2 are negatively correlated when their transformed levels are both less than 5. | |
C5428 | Recall that in order for a neural networks to learn, weights associated with neuron connections must be updated after forward passes of data through the network. These weights are adjusted to help reconcile the differences between the actual and predicted outcomes for subsequent forward passes. | |
C5429 | By simple definition, in classification/clustering analyze a set of data and generate a set of grouping rules which can be used to classify future data. Classification is a data mining (machine learning) technique used to predict group membership for data instances. | |
C5430 | The standard score (more commonly referred to as a z-score) is a very useful statistic because it (a) allows us to calculate the probability of a score occurring within our normal distribution and (b) enables us to compare two scores that are from different normal distributions. | |
C5431 | The main difference between the two types of models is that path analysis assumes that all variables are measured without error. SEM uses latent variables to account for measurement error. | |
C5432 | Class limits specify the span of data values that fall within a class. Class boundaries are possible data values. Class boundaries are not possible data values. | |
C5433 | Let's GO!Step 0 : Pre-requisites. It is recommended that before jumping on to Deep Learning, you should know the basics of Machine Learning. Step 1 : Setup your Machine. Step 2 : A Shallow Dive. Step 3 : Choose your own Adventure! Step 4 : Deep Dive into Deep Learning. 27 Comments. | |
C5434 | Here are some important considerations while choosing an algorithm.Size of the training data. It is usually recommended to gather a good amount of data to get reliable predictions. Accuracy and/or Interpretability of the output. Speed or Training time. Linearity. Number of features. | |
C5435 | The main reason why we use sigmoid function is because it exists between (0 to 1). Therefore, it is especially used for models where we have to predict the probability as an output. Since probability of anything exists only between the range of 0 and 1, sigmoid is the right choice. The function is differentiable. | |
C5436 | Efficiency: ReLu is faster to compute than the sigmoid function, and its derivative is faster to compute. This makes a significant difference to training and inference time for neural networks: only a constant factor, but constants can matter. Simplicity: ReLu is simple. | |
C5437 | The main advantage of supervised learning is that it allows you to collect data or produce a data output from the previous experience. The drawback of this model is that decision boundary might be overstrained if your training set doesn't have examples that you want to have in a class. | |
C5438 | The reason dividing by n-1 corrects the bias is because we are using the sample mean, instead of the population mean, to calculate the variance. Since the sample mean is based on the data, it will get drawn toward the center of mass for the data. | |
C5439 | The alpha state of mind is when you reach a very relaxed state while awake. Your brain begins to emit alpha waves instead of beta, which is what you emit when you're fully awake. | |
C5440 | Since medical tests can't be absolutely true, false positive and false negative are two problems we have to deal with. A false positive can lead to unnecessary treatment and a false negative can lead to a false diagnostic, which is very serious since a disease has been ignored. | |
C5441 | Statistical Analysis The root mean square error (RMSE), which is the sample standard deviation of the differences between predicted and observed values, with results in the same unit of measure of observed values. the correlation coefficient (r) as a measure of the degree of association among data. | |
C5442 | Neurons can only be seen using a microscope and can be split into three parts: Soma (cell body) — this portion of the neuron receives information. It contains the cell's nucleus. | |
C5443 | Z-scores are also known as standardized scores; they are scores (or data values) that have been given a common standard. This standard is a mean of zero and a standard deviation of 1. Contrary to what many people believe, z-scores are not necessarily normally distributed. | |
C5444 | Cluster sampling is a sampling plan used when mutually homogeneous yet internally heterogeneous groupings are evident in a statistical population. In this sampling plan, the total population is divided into these groups (known as clusters) and a simple random sample of the groups is selected. | |
C5445 | A Markov model is a stochastic model which describes a sequence of possible events (states) in which the probability of each event depends on a subset of previous events [1]. This report will focus on First-Order Markov Chains, in which the probability of a future state depends only on the current state [1]. | |
C5446 | In data science, association rules are used to find correlations and co-occurrences between data sets. They are ideally used to explain patterns in data from seemingly independent information repositories, such as relational databases and transactional databases. | |
C5447 | It is used to determine the extent to which there is a linear relationship between a dependent variable and one or more independent variables. In simple linear regression a single independent variable is used to predict the value of a dependent variable. | |
C5448 | Systematic sampling is popular with researchers because of its simplicity. Researchers generally assume the results are representative of most normal populations, unless a random characteristic disproportionately exists with every "nth" data sample (which is unlikely). | |
C5449 | One of the stages that SIFT uses is to create a pyramid of scales of the image. The feature detector then works by finding features that have a peak response not only in the image space, but in scale space too. This means that it finds the scale of the image which the feature will produce the highest response. | |
C5450 | A random variable is a variable whose value is unknown or a function that assigns values to each of an experiment's outcomes. Random variables are often used in econometric or regression analysis to determine statistical relationships among one another. | |
C5451 | The model is fit on the training set, and the fitted model is used to predict the responses for the observations in the validation set. The “training” data set is the general term for the samples used to create the model, while the “test” or “validation” data set is used to qualify performance. | |
C5452 | Convergence is a term mathematically most common in the study of series and sequences. A model is said to converge when the series s(n)=losswn(ˆy,y) (Where wn is the set of weights after the n'th iteration of back-propagation and s(n) is the n'th term of the series) is a converging series. | |
C5453 | Postprocessing procedures usually include various pruning routines, rule quality processing, rule filtering, rule combination, model combination, or even knowledge integration. All these procedures provide a kind of symbolic filter for noisy, imprecise, or non-user-friendly knowledge derived by an inductive algorithm. | |
C5454 | The theorem and its generalizations can be used to prove results and solve problems in combinatorics, algebra, calculus, and many other areas of mathematics. The binomial theorem also helps explore probability in an organized way: A friend says that she will flip a coin 5 times. | |
C5455 | But in reinforcement learning, we receive sequential samples from interactions with the environment. Storing all experience in a replay buffer allows us to train on more independent samples. We just draw a batch of transitions from the buffer at random and train on that. | |
C5456 | Whereas R-squared is a relative measure of fit, RMSE is an absolute measure of fit. As the square root of a variance, RMSE can be interpreted as the standard deviation of the unexplained variance, and has the useful property of being in the same units as the response variable. Lower values of RMSE indicate better fit. | |
C5457 | NMF stands for non-negative matrix factorization, a technique for obtaining low rank representation of matrices with non-negative or positive elements. In information retrieval and text mining, we rely on term-document matrices for representing document collections. | |
C5458 | You can convert measures from discrete to continuous or from continuous to discrete. Click the field and choose Discrete or Continuous. The field is green when it is continuous, and blue when it is discrete. For measures in the Data pane, right-click the field and choose Convert to Discrete or Convert to Continuous. | |
C5459 | A t-test tells you whether the difference between two sample means is "statistically significant" - not whether the two means are statistically different. A t-score with a p-value larger than 0.05 just states that the difference found is not "statistically significant". | |
C5460 | We can compare the quality of two estimators by looking at the ratio of their MSE. If the two estimators are unbiased this is equivalent to the ratio of the variances which is defined as the relative efficiency. rndr = n + 1 n · n n + 1 θ. | |
C5461 | Explanation: Asynchronous update ensures that the next state is at most unit hamming distance from current state. 5. If pattern is to be stored, then what does stable state should have updated value of? | |
C5462 | Due to AI, recommendation engines make quick and to-the-point recommendations tailored to each customer's needs and preferences. With the usage of artificial intelligence, online searching is improving as well, since it makes recommendations related to the user's visual preferences rather than product descriptions. | |
C5463 | Random errors in experimental measurements are caused by unknown and unpredictable changes in the experiment. These changes may occur in the measuring instruments or in the environmental conditions. | |
C5464 | Cluster analysis is a statistical method used to group similar objects into respective categories. It can also be referred to as segmentation analysis, taxonomy analysis, or clustering. For example, when cluster analysis is performed as part of market research, specific groups can be identified within a population. | |
C5465 | It maximizes the margin of the hyperplane. This is the best hyperplane because it reduces the generalization error the most. If we add new data, the Maximum Margin Classifier is the best hyperplane to correctly classify the new data. The Maximum Margin Classifier is our first SVM. | |
C5466 | How to Deal with MulticollinearityRemove some of the highly correlated independent variables.Linearly combine the independent variables, such as adding them together.Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression. | |
C5467 | A multi-layered perceptron consists of interconnected neurons transferring information to each other, much like the human brain. Each neuron is assigned a value. The network can be divided into three main layers. | |
C5468 | Reinforcement learning is an area of Machine Learning. In the absence of a training dataset, it is bound to learn from its experience. Example: The problem is as follows: We have an agent and a reward, with many hurdles in between. The agent is supposed to find the best possible path to reach the reward. | |
C5469 | Insufficient Data can cause a normal distribution to look completely scattered. An extreme example: if you choose three random students and plot the results on a graph, you won't get a normal distribution. You might get a uniform distribution (i.e. 62 62 63) or you might get a skewed distribution (80 92 99). | |
C5470 | “In some footage, using Optical Flow for creating smoother motion may not produce the desired results. Frame Blending repeats frames, but it also blends between them as needed to help smooth the motion.” If you aren't changing the frame rate during your export, leave this setting at “Frame Sampling.” | |
C5471 | Correct answer: Independent variables are generally graphed on the x-axis, while dependent variables are generally graphed on the y-axis. In this question, time is the independent variable and displacement is the dependent variable. | |
C5472 | Supervised Learning deals with two main tasks Regression and Classification. Unsupervised Learning deals with clustering and associative rule mining problems. Whereas Reinforcement Learning deals with exploitation or exploration, Markov's decision processes, Policy Learning, Deep Learning and value learning. | |
C5473 | The number of neurons in the input layer equals the number of input variables in the data being processed. The number of neurons in the output layer equals the number of outputs associated with each input. | |
C5474 | In probability, an outcome is in event "A and B" only when the outcome is in both event A and event B. (Intersection) In a Venn Diagram, an element is in the intersection of "A and B" only when the element is in BOTH sets. Rule (for AND): | |
C5475 | sudo rm -rf / means to remove the contents of the root folder in a recursive manner. rm = remove, -r = recursive. This basically wipes out the contents of the root folder (the directories, sub-directories and all the files in them). | |
C5476 | It has been found that multilingualism affects the structure, and essentially, the cytoarchitecture of the brain. Language learning boosts brain plasticity and the brain's ability to code new information. Early language learning plays a significant role in the formation of memory circuits for learning new information. | |
C5477 | As binning methods consult the neighborhood of values, they perform local smoothing.Approach:Sort the array of given data set.Divides the range into N intervals, each containing the approximately same number of samples(Equal-depth partitioning).Store mean/ median/ boundaries in each row. | |
C5478 | How do I run a Z Test?State the null hypothesis and alternate hypothesis.Choose an alpha level.Find the critical value of z in a z table.Calculate the z test statistic (see below).Compare the test statistic to the critical z value and decide if you should support or reject the null hypothesis. | |
C5479 | GANs have plenty of real-world use cases like image generation, artwork generation, music generation, and video generation. Also, they can enhance the quality of your images, stylize or colorize your images, generate faces and can perform many more interesting tasks. | |
C5480 | Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers. Improving operations. Many companies use predictive models to forecast inventory and manage resources. | |
C5481 | Linear regression is used for predicting the continuous dependent variable using a given set of independent features whereas Logistic Regression is used to predict the categorical. Linear regression is used to solve regression problems whereas logistic regression is used to solve classification problems. | |
C5482 | The One-Sample z-test is used when we want to know whether the difference between the mean of a sample mean and the mean of a population is large enough to be statistically significant, that is, if it is unlikely to have occurred by chance. | |
C5483 | Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network. | |
C5484 | The Kruskal–Wallis test by ranks, Kruskal–Wallis H test (named after William Kruskal and W. Allen Wallis), or one-way ANOVA on ranks is a non-parametric method for testing whether samples originate from the same distribution. It is used for comparing two or more independent samples of equal or different sample sizes. | |
C5485 | In-group bias is notoriously difficult to avoid completely, but research shows it can be reduced through interaction with other groups, and by giving people an incentive to act in an unbiased manner. | |
C5486 | An artificial neuron (also referred to as a perceptron) is a mathematical function. It takes one or more inputs that are multiplied by values called “weights” and added together. This value is then passed to a non-linear function, known as an activation function, to become the neuron's output. | |
C5487 | While many people use the terms interchangeably, data science and big data analytics are unique fields, with the major difference being the scope. Data science produces broader insights that concentrate on which questions should be asked, while big data analytics emphasizes discovering answers to questions being asked. | |
C5488 | There are two main ways to access subsets of the elements in a tensor, either of which should work for your example.Use the indexing operator (based on tf. slice() ) to extract a contiguous slice from the tensor. input = tf. Use the tf. gather() op to select a non-contiguous slice from the tensor. input = tf. | |
C5489 | Time series decomposition involves thinking of a series as a combination of level, trend, seasonality, and noise components. Decomposition provides a useful abstract model for thinking about time series generally and for better understanding problems during time series analysis and forecasting. | |
C5490 | Both LDA and PCA are linear transformation techniques: LDA is a supervised whereas PCA is unsupervised – PCA ignores class labels. Remember that LDA makes assumptions about normally distributed classes and equal class covariances. | |
C5491 | If your data contains both numeric and categorical variables, the best way to carry out clustering on the dataset is to create principal components of the dataset and use the principal component scores as input into the clustering. | |
C5492 | From Wikipedia, the free encyclopedia. Quantization is the process of constraining an input from a continuous or otherwise large set of values (such as the real numbers) to a discrete set (such as the integers). | |
C5493 | Principal components analysis (PCA) is a statistical technique that allows identifying underlying linear patterns in a data set so it can be expressed in terms of other data set of a significatively lower dimension without much loss of information. | |
C5494 | The prior distribution is a distribution for the parameters whereas the prior predictive distribution is a distribution for the observations. The last line is based on the assumption that the upcoming observation is independent of X given θ. | |
C5495 | In mathematics, low-rank approximation is a minimization problem, in which the cost function measures the fit between a given matrix (the data) and an approximating matrix (the optimization variable), subject to a constraint that the approximating matrix has reduced rank. | |
C5496 | law of large numbers. A principle stating that the larger the number of similar exposure units considered, the more closely the losses reported will equal the underlying probability of loss. | |
C5497 | The essential difference between these two is that Logistic regression is used when the dependent variable is binary in nature. In contrast, Linear regression is used when the dependent variable is continuous and nature of the regression line is linear. | |
C5498 | matplotlib. pyplot is a collection of functions that make matplotlib work like MATLAB. Each pyplot function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. | |
C5499 | Here are some ideas to help you learn:Read. Books by authors from other countries can expand your cultural understanding. Watch movies. World cinema has a lot to offer. Listen to radio shows and podcasts. Talk with individuals from different cultures. Travelling to other countries. |
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