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C9500 | Pre-Interview PreparationDevelop a deep knowledge of data structures. You should understand and be able to talk about different data structures and their strengths, weaknesses, and how they compare to each other. Understand Big O notation. Know the major sorting algorithms. | |
C9501 | Additive smoothing plays an important role in Naive Bayes classification, as long as not all events were observed at least ones. In this case of having at least one event with no observation, the probability for this event is absolut zero. To prevent this problem, addative smoothing is used. | |
C9502 | 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. | |
C9503 | The steps are:Clean the data by removing outliers and treating missing data.Identify a parametric or nonparametric predictive modeling approach to use.Preprocess the data into a form suitable for the chosen modeling algorithm.Specify a subset of the data to be used for training the model.More items | |
C9504 | Non negative matrix factorization only takes positive values as input while SVD can take both positive and negative values. SVD and NMF are both matrix decomposition techniques but they are very different and are generally used for different purposes. SVD helps in giving Eigen vectors of the input matrix. | |
C9505 | It's more of an approach than a process. Predictive analytics and machine learning go hand-in-hand, as predictive models typically include a machine learning algorithm. These models can be trained over time to respond to new data or values, delivering the results the business needs. | |
C9506 | Both phrases are grammatically correct and meaningful, so the difference is a matter of style. “In” is more specific than “and”. A glove with a hand in it functions in close conformance with the hand, while a glove on a shelf does not. | |
C9507 | There are ways, however, to try to maintain objectivity and avoid bias with qualitative data analysis:Use multiple people to code the data. Have participants review your results. Verify with more data sources. Check for alternative explanations. Review findings with peers. | |
C9508 | The Subfields of Artificial IntelligenceMachine Learning. Machine learning refers to the ability of a computer system to use data to learn automatically, predict, act, and explain the decisions it makes. Deduction and Reasoning Systems. Robotics and Motion. Knowledge Representation. Image and Voice Recognition. Other Fields. | |
C9509 | Standard deviation is an important measure of spread or dispersion. It tells us how far, on average the results are from the mean. Therefore if the standard deviation is small, then this tells us that the results are close to the mean, whereas if the standard deviation is large, then the results are more spread out. | |
C9510 | The perceptron is a mathematical model of a biological neuron. While in actual neurons the dendrite receives electrical signals from the axons of other neurons, in the perceptron these electrical signals are represented as numerical values. | |
C9511 | Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Deep learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured and inter-connected. | |
C9512 | Binning method is used to smoothing data or to handle noisy data. In this method, the data is first sorted and then the sorted values are distributed into a number of buckets or bins. As binning methods consult the neighborhood of values, they perform local smoothing. | |
C9513 | In AI, the study on perception is mostly focused on the reproduction of human perception, especially on the perception of aural and visual signals. However, this is not necessarily the case since the perception mechanism of a computer system does not have to be identical to that of a human being. | |
C9514 | Naive bayes is a Generative model whereas Logistic Regression is a Discriminative model . Generative model is based on the joint probability, p( x, y), of the inputs x and the label y, and make their predictions by using Bayes rules to calculate p(y | x), and then picking the most likely label y. | |
C9515 | Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g. differentiable or subdifferentiable). | |
C9516 | Sample size measures the number of individual samples measured or observations used in a survey or experiment. For example, if you test 100 samples of soil for evidence of acid rain, your sample size is 100. If an online survey returned 30,500 completed questionnaires, your sample size is 30,500. | |
C9517 | To convert a transfer function into state equations in phase variable form, we first convert the transfer function to a differential equation by cross-multiplying and taking the inverse Laplace transform, assuming zero initial conditions. | |
C9518 | It allows researchers to determine the strength and direction of a relationship so that later studies can narrow the findings down and, if possible, determine causation experimentally. Correlation research only uncovers a relationship; it cannot provide a conclusive reason for why there's a relationship. | |
C9519 | Network representation learning has been recently proposed as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information. | |
C9520 | 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. | |
C9521 | Segmentation methods divide a digital image into (usually small) groups of connected pixels. Each group (aka segment, or image-object) has a unique numeric ID (e.g., 67897) in the resulting raster (aka partition). In contrast, classification methods assign a class to each element, be it individual pixels or segments. | |
C9522 | In Short: If your Father and Mother are working in Government as a class 2 employee or above, then you belong to OBC Creamy layer. If only one of your parent is class 2 employee and other is below class 2 or unemployed then you fall under OBC Non Creamy Layer. | |
C9523 | 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. | |
C9524 | Q-Learning is a basic form of Reinforcement Learning which uses Q-values (also called action values) to iteratively improve the behavior of the learning agent. Q-Values or Action-Values: Q-values are defined for states and actions. is an estimation of how good is it to take the action at the state . | |
C9525 | The defining characteristic of fifth generation computers (FGC) is that they are to be knowledge processing machines in contrast to being merely data processing machines as is the case with most of the present generation computers. | |
C9526 | Although many types of neural network models have been developed to solve different problems, the most widely used model by far for time series forecasting has been the feedforward neural network. | |
C9527 | An SLI (service level indicator) measures compliance with an SLO (service level objective). So, for example, if your SLA specifies that your systems will be available 99.95% of the time, your SLO is likely 99.95% uptime and your SLI is the actual measurement of your uptime. Maybe it's 99.96%. Maybe 99.99%. | |
C9528 | A cross-sectional study involves looking at data from a population at one specific point in time. Cross-sectional studies are observational in nature and are known as descriptive research, not causal or relational, meaning that you can't use them to determine the cause of something, such as a disease. | |
C9529 | Normality is not the only “usual” assumption. We also usually assume that the residuals have the same distribution for all values of the explanatory variables. A linear regression requires residuals to be normally distributed. You need assumptions about the distribution of the residuals in order to make inferences. | |
C9530 | According to SAS, predictive analytics is “the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. In short, predictive intelligence drives marketing decisions.” | |
C9531 | In a histogram, the total range of data set (i.e from minimum value to maximum value) is divided into 8 to 15 equal parts. These equal parts are known as bins or class intervals. Each and every observation (or value) in the data set is placed in the appropriate bin. | |
C9532 | Advantages of Recurrent Neural Network It is useful in time series prediction only because of the feature to remember previous inputs as well. This is called Long Short Term Memory. Recurrent neural network are even used with convolutional layers to extend the effective pixel neighborhood. | |
C9533 | Evaluation metrics are used to measure the quality of the statistical or machine learning model. Evaluating machine learning models or algorithms is essential for any project. There are many different types of evaluation metrics available to test a model. | |
C9534 | Two types of statistical methods are used in analyzing data: descriptive statistics and inferential statistics. Descriptive statistics are used to synopsize data from a sample exercising the mean or standard deviation. Inferential statistics are used when data is viewed as a subclass of a specific population. | |
C9535 | Natural-language understanding (NLU) or natural-language interpretation (NLI) is a subtopic of natural-language processing in artificial intelligence that deals with machine reading comprehension. Natural-language understanding is considered an AI-hard problem. | |
C9536 | A clinical trial is a randomized controlled trial only when participants are randomly allocated to the group receiving the treatment and a control group. What participants are allocated among groups receiving different treatments the clinical trial is simply called a randomized trial. | |
C9537 | Here are some important ones used in deep learning architectures:Multilayer Perceptron Neural Network (MLPNN) Backpropagation. Convolutional Neural Network (CNN) Recurrent Neural Network (RNN) Long Short-Term Memory (LSTM) Generative Adversarial Network (GAN) Restricted Boltzmann Machine (RBM) Deep Belief Network (DBN) | |
C9538 | The 'd' means a Δ in the limit approaching zero. Basically the slope is approximately Δy/Δx but if you let Δx approach zero, you reach the exactly slope which is then dy/dx. | |
C9539 | Naive Bayes is a linear classifier Naive Bayes leads to a linear decision boundary in many common cases. The red decision line indicates the decision boundary where P(y=1|x)=P(y=2|x). | |
C9540 | The notation for the uniform distribution is X ~ U(a, b) where a = the lowest value of x and b = the highest value of x. The probability density function is f(x)=1b−a f ( x ) = 1 b − a for a ≤ x ≤ b. For this example, X ~ U(0, 23) and f(x)=123−0 f ( x ) = 1 23 − 0 for 0 ≤ X ≤ 23. | |
C9541 | The purpose of an inverted index is to allow fast full-text searches, at a cost of increased processing when a document is added to the database. The inverted file may be the database file itself, rather than its index. | |
C9542 | In statistics, latent variables (from Latin: present participle of lateo (“lie hidden”), as opposed to observable variables) are variables that are not directly observed but are rather inferred (through a mathematical model) from other variables that are observed (directly measured). | |
C9543 | More precisely, it is a measure of the average distance between the values of the data in the set and the mean. A low standard deviation indicates that the data points tend to be very close to the mean; a high standard deviation indicates that the data points are spread out over a large range of values. | |
C9544 | Ordinal logistic regression (often just called 'ordinal regression') is used to predict an ordinal dependent variable given one or more independent variables. As with other types of regression, ordinal regression can also use interactions between independent variables to predict the dependent variable. | |
C9545 | In game theory, minimax is a decision rule used to minimize the worst-case potential loss; in other words, a player considers all of the best opponent responses to his strategies, and selects the strategy such that the opponent's best strategy gives a payoff as large as possible. | |
C9546 | The goal of lasso regression is to obtain the subset of predictors that minimizes prediction error for a quantitative response variable. The lasso does this by imposing a constraint on the model parameters that causes regression coefficients for some variables to shrink toward zero. | |
C9547 | In statistics: Numerical measures. The range, the difference between the largest value and the smallest value, is the simplest measure of variability in the data. The range is determined by only the two extreme data values. | |
C9548 | The Empirical Rule states that 99.7% of data observed following a normal distribution lies within 3 standard deviations of the mean. Under this rule, 68% of the data falls within one standard deviation, 95% percent within two standard deviations, and 99.7% within three standard deviations from the mean. | |
C9549 | Multiple regression is an extension of simple linear regression. It is used when we want to predict the value of a variable based on the value of two or more other variables. The variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable). | |
C9550 | Data that can only take certain values. For example: the number of students in a class (you can't have half a student). Discrete Data is not Continuous Data. See: Continuous Data. | |
C9551 | Data drift is the sum of data changes — think mobile interactions, sensor logs and web clickstreams — that started life as well-meaning business tweaks or system updates, as CMSWire contributor, Girish Pancha, explains in greater detail here. | |
C9552 | Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. | |
C9553 | StepsStep 1: For each (x,y) point calculate x2 and xy.Step 2: Sum all x, y, x2 and xy, which gives us Σx, Σy, Σx2 and Σxy (Σ means "sum up")Step 3: Calculate Slope m:m = N Σ(xy) − Σx Σy N Σ(x2) − (Σx)2Step 4: Calculate Intercept b:b = Σy − m Σx N.Step 5: Assemble the equation of a line. | |
C9554 | 1 plural also banditti\ ban-ˈdi-tē \ : an outlaw who lives by plunder especially : a member of a band of marauders. 2 : robber. 3 : an enemy plane. | |
C9555 | Likelihood is the chance that the reality you've hypothesized could have produced the particular data you got. Likelihood: The probability of data given a hypothesis. However Probability is the chance that the reality you're considering is true, given the data you have. | |
C9556 | 0:404:05Suggested clip · 108 secondsHow to interpret a survival plot - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C9557 | The covariance is defined as the mean value of this product, calculated using each pair of data points xi and yi. If the covariance is zero, then the cases in which the product was positive were offset by those in which it was negative, and there is no linear relationship between the two random variables. | |
C9558 | The various methods used for dimensionality reduction include: Principal Component Analysis (PCA) Linear Discriminant Analysis (LDA) Generalized Discriminant Analysis (GDA) | |
C9559 | Multiple regression models forecast a variable using a linear combination of predictors, whereas autoregressive models use a combination of past values of the variable. These concepts and techniques are used by technical analysts to forecast security prices. | |
C9560 | Analysis of variance (ANOVA) is a statistical technique that is used to check if the means of two or more groups are significantly different from each other. ANOVA checks the impact of one or more factors by comparing the means of different samples. Another measure to compare the samples is called a t-test. | |
C9561 | Predictive modeling, also called predictive analytics, is a mathematical process that seeks to predict future events or outcomes by analyzing patterns that are likely to forecast future results. | |
C9562 | The idea behind the chi-square goodness-of-fit test is to see if the sample comes from the population with the claimed distribution. Only when the sum is large is the a reason to question the distribution. Therefore, the chi-square goodness-of-fit test is always a right tail test. | |
C9563 | Gradient descent is an optimization algorithm that finds the optimal weights (a,b) that reduces prediction error. Step 2: Calculate the gradient i.e. change in SSE when the weights (a & b) are changed by a very small value from their original randomly initialized value. | |
C9564 | Sequence classification methods can be organized into three categories: (1) feature-based classification, which transforms a sequence into a feature vector and then applies conventional classification methods; (2) sequence distance–based classification, where the distance function that measures the similarity between | |
C9565 | 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. | |
C9566 | Why is the derivative of the LSTM cell state w.r.t. to the previous cell state equal to the forget gate? The chain rule would extend for longer and we'd have more derivative terms in there. | |
C9567 | Connected components labeling scans an image and groups its pixels into components based on pixel connectivity, i.e. all pixels in a connected component share similar pixel intensity values and are in some way connected with each other. | |
C9568 | Regularization is a technique used for tuning the function by adding an additional penalty term in the error function. The additional term controls the excessively fluctuating function such that the coefficients don't take extreme values. | |
C9569 | Multinomial logistic regression is used to predict categorical placement in or the probability of category membership on a dependent variable based on multiple independent variables. The independent variables can be either dichotomous (i.e., binary) or continuous (i.e., interval or ratio in scale). | |
C9570 | A sampling frame is a list of all the items in your population. It's a complete list of everyone or everything you want to study. For example, the population could be “People who live in Jacksonville, Florida.” The frame would name ALL of those people, from Adrian Abba to Felicity Zappa. | |
C9571 | Some of the popular techniques are: Density-based techniques (k-nearest neighbor, local outlier factor, isolation forests, and many more variations of this concept). Subspace-, correlation-based and tensor-based outlier detection for high-dimensional data. | |
C9572 | Loss is nothing but a prediction error of Neural Net. And the method to calculate the loss is called Loss Function. In simple words, the Loss is used to calculate the gradients. And gradients are used to update the weights of the Neural Net. This is how a Neural Net is trained. | |
C9573 | Inductive Learning is where we are given examples of a function in the form of data (x) and the output of the function (f(x)). The goal of inductive learning is to learn the function for new data (x). Classification: when the function being learned is discrete. Regression: when the function being learned is continuous. | |
C9574 | The standard normal distribution is a normal distribution with a mean of zero and standard deviation of 1. For the standard normal distribution, 68% of the observations lie within 1 standard deviation of the mean; 95% lie within two standard deviation of the mean; and 99.9% lie within 3 standard deviations of the mean. | |
C9575 | The consequent of a conditional statement is the part that usually follows "then". The part that usually follows "if" is called the "antecedent". To affirm the consequent is, of course, to claim that the consequent is true. Thus, affirming the consequent in the example would be to claim that I have logic class. | |
C9576 | After completing Calculus I and II, you may continue to Calculus III, Linear Algebra, and Differential Equations. These three may be taken in any order that fits your schedule, but the listed order is most common. | |
C9577 | Syllabus:Basic Data Structures: Arrays, Strings, Stacks, Queues.Asymptotic analysis (Big-O notation)Basic math operations (addition, subtraction, multiplication, division, exponentiation)Sqrt(n) primality testing.Euclid's GCD Algorithm.Basic Recursion.Greedy Algorithms.Basic Dynamic Programming.More items | |
C9578 | In order to assist the kernel with processing the image, padding is added to the frame of the image to allow for more space for the kernel to cover the image. Adding padding to an image processed by a CNN allows for more accurate analysis of images. | |
C9579 | A scatter plot can suggest various kinds of correlations between variables with a certain confidence interval. For example, weight and height, weight would be on y axis and height would be on the x axis. Correlations may be positive (rising), negative (falling), or null (uncorrelated). | |
C9580 | These are all examples of what are called Continuous Time Markov Chains, and they can each be modeled by representing the passage from one state to another as a Poisson Process . On it's own, a Poisson distribution describes the probability of an event occurring after a given amount of time, t. | |
C9581 | Do you know how to choose the right machine learning algorithm among 7 different types?1-Categorize the problem. 2-Understand Your Data. Analyze the Data. Process the data. Transform the data. 3-Find the available algorithms. 4-Implement machine learning algorithms. 5-Optimize hyperparameters.More items | |
C9582 | 5 Techniques to Prevent Overfitting in Neural NetworksSimplifying The Model. The first step when dealing with overfitting is to decrease the complexity of the model. Early Stopping. Early stopping is a form of regularization while training a model with an iterative method, such as gradient descent. Use Data Augmentation. Use Regularization. Use Dropouts. | |
C9583 | Time-series data is a set of observations collected at usually discrete and equally spaced time intervals. Cross-sectional data are observations that come from different individuals or groups at a single point in time. | |
C9584 | It is easier to reject the null hypothesis with a one-tailed than with a two-tailed test as long as the effect is in the specified direction. Therefore, one-tailed tests have lower Type II error rates and more power than do two-tailed tests. | |
C9585 | A posterior probability, in Bayesian statistics, is the revised or updated probability of an event occurring after taking into consideration new information. In statistical terms, the posterior probability is the probability of event A occurring given that event B has occurred. | |
C9586 | Deep Learning tries to find out the optimal set of features on your own and generate the output based on those features. So, in a nutshell, we can say that deep learning does not require feature selection. It will automatically find out the optimal set of features. | |
C9587 | Multiple linear regression requires at least two independent variables, which can be nominal, ordinal, or interval/ratio level variables. A rule of thumb for the sample size is that regression analysis requires at least 20 cases per independent variable in the analysis. | |
C9588 | This ability to access offline learning means employees can still read a how-to or watch a video explaining a task without a connection. That prevents learners from having to wait to get back to their office or home before they can find the information they need. | |
C9589 | Yes, we can apply logistic regression on 3 classification problem, We can use One Vs all method for 3 class classification in logistic regression. | |
C9590 | Statistical independence is a concept in probability theory. Two events A and B are statistical independent if and only if their joint probability can be factorized into their marginal probabilities, i.e., P(A ∩ B) = P(A)P(B). The concept can be generalized to more than two events. | |
C9591 | A Confusion matrix is an N x N matrix used for evaluating the performance of a classification model, where N is the number of target classes. The matrix compares the actual target values with those predicted by the machine learning model. The rows represent the predicted values of the target variable. | |
C9592 | Descriptive statistics describe what is going on in a population or data set. Inferential statistics, by contrast, allow scientists to take findings from a sample group and generalize them to a larger population. The two types of statistics have some important differences. | |
C9593 | In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. | |
C9594 | A Gentle Introduction to the Rectified Linear Unit (ReLU) In a neural network, the activation function is responsible for transforming the summed weighted input from the node into the activation of the node or output for that input. | |
C9595 | List of applicationsOptical character recognition.Handwriting recognition.Speech recognition.Face recognition.Artificial creativity.Computer vision.Virtual reality.Image processing.More items | |
C9596 | Inferential statistics are often used to compare the differences between the treatment groups. Inferential statistics use measurements from the sample of subjects in the experiment to compare the treatment groups and make generalizations about the larger population of subjects. | |
C9597 | SEM uses latent variables to account for measurement error. Latent Variables. A latent variable is a hypothetical construct that is invoked to explain observed covariation in behavior. Examples in psychology include intelligence (a.k.a. cognitive ability), Type A personality, and depression. | |
C9598 | A moving average term in a time series model is a past error (multiplied by a coefficient). Let w t ∼ i i d N ( 0 , σ w 2 ) , meaning that the wt are identically, independently distributed, each with a normal distribution having mean 0 and the same variance. | |
C9599 | 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. |
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