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C2500 | Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. Deep learning is a subfield of machine learning. While both fall under the broad category of artificial intelligence, deep learning is what powers the most human-like artificial intelligence. | |
C2501 | The ability to slide the signal is the what gives Engineers a more accurate representation of the signal and therefore a better resolution in time. So when you use a Wavelet Transform the signal is deconstructed using the same wavelet at different scales, rather than the same sin() wave at different frequencies. | |
C2502 | Equality of result- making certain that people achieve the same result. An example is making sure that all students get the same grade no matter the race. Equality of opportunity- giving people an equal chance to succeed. | |
C2503 | The coefficient of variation represents the ratio of the standard deviation to the mean, and it is a useful statistic for comparing the degree of variation from one data series to another, even if the means are drastically different from one another. | |
C2504 | Text classification also known as text tagging or text categorization is the process of categorizing text into organized groups. By using Natural Language Processing (NLP), text classifiers can automatically analyze text and then assign a set of pre-defined tags or categories based on its content. | |
C2505 | Truncated SVD are the singular values of the matrix A with rank r. We can find truncated SVD to A by setting all but the first k largest singular values equal to zero and using only the first k columns of U and V. | |
C2506 | Types of Clustering Methods: Overview and Quick Start R Code Hierarchical clustering. Fuzzy clustering. Density-based clustering. Model-based clustering. | |
C2507 | The decision for converting a predicted probability or scoring into a class label is governed by a parameter referred to as the “decision threshold,” “discrimination threshold,” or simply the “threshold.” The default value for the threshold is 0.5 for normalized predicted probabilities or scores in the range between 0 | |
C2508 | Deviation means change or distance. Hence standard deviation is a measure of change or the distance from a measure of central tendency - which is normally the mean. Hence, standard deviation is different from a measure of central tendency. | |
C2509 | A function whose value increases more slowly to infinity than any nonconstant polynomial is said to be a logarithmically increasing function. | |
C2510 | The second derivative may be used to determine local extrema of a function under certain conditions. If a function has a critical point for which f′(x) = 0 and the second derivative is positive at this point, then f has a local minimum here. This technique is called Second Derivative Test for Local Extrema. | |
C2511 | A 1-gram (or unigram) is a one-word sequence. A 2-gram (or bigram) is a two-word sequence of words, like “I love”, “love reading”, or “Analytics Vidhya”. And a 3-gram (or trigram) is a three-word sequence of words like “I love reading”, “about data science” or “on Analytics Vidhya”. | |
C2512 | Computational Learning Theory (CoLT) is a field of AI research studying the design of machine learning algorithms to determine what sorts of problems are “learnable.” The ultimate goals are to understand the theoretical underpinnings of deep learning programs, what makes them work or not, while improving accuracy and | |
C2513 | 6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case StudyGet a basic understanding of the algorithm.Find some different learning sources.Break the algorithm into chunks.Start with a simple example.Validate with a trusted implementation.Write up your process. | |
C2514 | Correlation is the concept of linear relationship between two variables. Whereas correlation coefficient is a measure that measures linear relationship between two variables. | |
C2515 | 6 Types of Artificial Neural Networks Currently Being Used in Machine LearningFeedforward Neural Network – Artificial Neuron: Radial basis function Neural Network: Kohonen Self Organizing Neural Network: Recurrent Neural Network(RNN) – Long Short Term Memory: Convolutional Neural Network: Modular Neural Network: | |
C2516 | 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. | |
C2517 | 68% | |
C2518 | What is a Convolutional Neural Network (CNN) A neural network consists of several different layers such as the input layer, at least one hidden layer, and an output layer. They are best used in object detection for recognizing patterns such as edges (vertical/horizontal), shapes, colours, and textures. | |
C2519 | Sampling error is one of two reasons for the difference between an estimate and the true, but unknown, value of the population parameter. The sampling error for a given sample is unknown but when the sampling is random, the maximum likely size of the sampling error is called the margin of error. | |
C2520 | 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. | |
C2521 | In spite of being linear, the Fourier transform is not shift invariant. In other words, a shift in the time domain does not correspond to a shift in the frequency domain. | |
C2522 | Descriptive statistics are limited in so much that they only allow you to make summations about the people or objects that you have actually measured. You cannot use the data you have collected to generalize to other people or objects (i.e., using data from a sample to infer the properties/parameters of a population). | |
C2523 | An error term represents the margin of error within a statistical model; it refers to the sum of the deviations within the regression line, which provides an explanation for the difference between the theoretical value of the model and the actual observed results. | |
C2524 | Batch normalization (also known as batch norm) is a method used to make artificial neural networks faster and more stable through normalization of the input layer by re-centering and re-scaling. Others sustain that batch normalization achieves length-direction decoupling, and thereby accelerates neural networks. | |
C2525 | A certain continuous random variable has a probability density function (PDF) given by: f ( x ) = C x ( 1 − x ) 2 , f(x) = C x (1-x)^2, f(x)=Cx(1−x)2, where x x x can be any number in the real interval [ 0 , 1 ] [0,1] [0,1]. | |
C2526 | For example, a random variable could be the outcome of the roll of a die or the flip of a coin. A probability distribution is a list of all of the possible outcomes of a random variable along with their corresponding probability values. | |
C2527 | Genetic algorithms are stochastic search algorithms which act on a population of possible solutions. Genetic algorithms are used in artificial intelligence like other search algorithms are used in artificial intelligence — to search a space of potential solutions to find one which solves the problem. | |
C2528 | Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. | |
C2529 | 1 : having, involving, or exhibiting symmetry. 2 : having corresponding points whose connecting lines are bisected by a given point or perpendicularly bisected by a given line or plane symmetrical curves. | |
C2530 | There are four main types of probability sample.Simple random sampling. In a simple random sample, every member of the population has an equal chance of being selected. Systematic sampling. Stratified sampling. Cluster sampling. | |
C2531 | Algorithms are often elegant and incredibly useful tools used to accomplish tasks. They are mostly invisible aids, augmenting human lives in increasingly incredible ways. However, sometimes the application of algorithms created with good intentions leads to unintended consequences. | |
C2532 | In statistics, Studentization, named after William Sealy Gosset, who wrote under the pseudonym Student, is the adjustment consisting of division of a first-degree statistic derived from a sample, by a sample-based estimate of a population standard deviation. | |
C2533 | RGB formats are usually straightforward: red, green, and blue with a given pixel size. RGB24 is the most common, allowing 8 bits and a value of 0-255 per color component. YUV color-spaces are a more efficient coding and reduce the bandwidth more than RGB capture can. | |
C2534 | Clean, augment, and preprocess the data into a convenient form, if needed. Conduct an exploratory analysis of the data to get a better sense of it. Using what you find as a guide, construct a model of some aspect of the data. Use the model to answer the question you started with, and validate your results. | |
C2535 | Applications. The discrete wavelet transform has a huge number of applications in science, engineering, mathematics and computer science. Most notably, it is used for signal coding, to represent a discrete signal in a more redundant form, often as a preconditioning for data compression. | |
C2536 | Stochastic processes appear in many different fields, including the physical sciences such as biology, chemistry, ecology, neuroscience, and physics as well as technology and engineering fields such as image processing, signal processing, information theory, computer science,, cryptography and telecommunications. | |
C2537 | You can find the decision boundary analytically. For Bayesian hypothesis testing, the decision boundary corresponds to the values of X that have equal posteriors, i.e., you need to solve: for X = (x1, x2). | |
C2538 | Weighted least squares (WLS), also known as weighted linear regression, is a generalization of ordinary least squares and linear regression in which the errors covariance matrix is allowed to be different from an identity matrix. | |
C2539 | How to create probability distribution plots in MinitabChoose Graph > Probability Distribution Plot > View Probability.Click OK.From Distribution, choose Normal.In Mean, type 100.In Standard deviation, type 15. | |
C2540 | FeaturesAssign a numerical value to each possible outcome on the tree. Label the likelihood of each outcome. Make a separate list for each decision and its possible outcomes. Review each branch on the tree for costs.More items | |
C2541 | A learning algorithm is a method used to process data to extract patterns appropriate for application in a new situation. In particular, the goal is to adapt a system to a specific input-output transformation task. | |
C2542 | Key concepts include probability distributions, statistical significance, hypothesis testing, and regression. Furthermore, machine learning requires understanding Bayesian thinking. | |
C2543 | The Least Squares Regression Line is the line that makes the vertical distance from the data points to the regression line as small as possible. It's called a “least squares” because the best line of fit is one that minimizes the variance (the sum of squares of the errors). | |
C2544 | Chaos theory describes the qualities of the point at which stability moves to instability or order moves to disorder. For example, unlike the behavior of a pendulum, which adheres to a predictable pattern a chaotic system does not settle into a predictable pattern due to its nonlinear processes. | |
C2545 | In the real world, an impulse function is a pulse that is much shorter than the time response of the system. The system's response to an impulse can be used to determine the output of a system to any input using the time-slicing technique called convolution. | |
C2546 | While a linear equation has one basic form, nonlinear equations can take many different forms. Literally, it's not linear. If the equation doesn't meet the criteria above for a linear equation, it's nonlinear. | |
C2547 | Once you find a correlation, you can test for causation by running experiments that “control the other variables and measure the difference.” Two such experiments or analyses you can use to identify causation with your product are: Hypothesis testing. A/B/n experiments. | |
C2548 | verb (used with object), quan·tized, quan·tiz·ing. Mathematics, Physics. to restrict (a variable quantity) to discrete values rather than to a continuous set of values. | |
C2549 | Even with the use of pre-pruning, they tend to overfit and provide poor generalization performance. Therefore, in most applications, by aggregating many decision trees, using methods like bagging, random forests, and boosting, the predictive performance of decision trees can be substantially improved. | |
C2550 | We know that non-significant intercept can be interpreted as result for which the result of the analysis will be zero if all other variables are equal to zero and we must consider its removal for theoretical reasons. | |
C2551 | Dimensionality Reduction and PCA. Dimensionality reduction refers to reducing the number of input variables for a dataset. If your data is represented using rows and columns, such as in a spreadsheet, then the input variables are the columns that are fed as input to a model to predict the target variable. | |
C2552 | It is a process of converting a sentence to forms – list of words, list of tuples (where each tuple is having a form (word, tag)). The tag in case of is a part-of-speech tag, and signifies whether the word is a noun, adjective, verb, and so on. | |
C2553 | In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own. | |
C2554 | Quantum computers could enable an artificial life protocol that encodes quantum behaviours belonging to living systems, including self-replication, mutation, interaction between individuals, birth and death. The researchers executed such a model on an IBM ibmqx4 cloud quantum computer. | |
C2555 | A research hypothesis is a statement of an expected or predicted relationship between two or more variables. It's what the experimenter believes will happen in her research study. | |
C2556 | Definition 1.1 A Decision rule is a formal rule that states, based on the data obtained, when to reject the null hypothesis H0. Generally, it specifies a set of values based on the data to be collected, which are contradictory to the null H0 and which favor the alternative hypothesis H1. | |
C2557 | Softmax is used for multi-classification in the Logistic Regression model, whereas Sigmoid is used for binary classification in the Logistic Regression model. This is similar to the Sigmoid function. The difference is that, in the denominator, we sum together all of the values. | |
C2558 | There is always at least one such optimal policy[8]. The so called greedy policy is following the currently best path of actions. During learning however, for the values to converge into good estimates it is required that the agent visits all available states to gain information about them. | |
C2559 | Random forests perform well for multi-class object detection and bioinformatics, which tends to have a lot of statistical noise. Gradient Boosting performs well when you have unbalanced data such as in real time risk assessment. | |
C2560 | Last Updated on Decem. Cross-entropy is commonly used in machine learning as a loss function. Cross-entropy is a measure from the field of information theory, building upon entropy and generally calculating the difference between two probability distributions. | |
C2561 | Squared hinge loss is nothing else but a square of the output of the hinge's max(…) function. It generates a loss function as illustrated above, compared to regular hinge loss. | |
C2562 | In statistics, a sequence (or a vector) of random variables is homoscedastic /ˌhoʊmoʊskəˈdæstɪk/ if all its random variables have the same finite variance. This is also known as homogeneity of variance. | |
C2563 | Correlation measures the relationship between two variables. Correlation refers to an increase/decrease in a dependent variable with an increase/decrease in an independent variable. Collinearity refers to two or more independent variables acting in concert to explain the variation in a dependent variable. | |
C2564 | all provides a way to leverage binary classification. -all solution consists of N separate binary classifiers—one binary classifier for each possible outcome. During training, the model runs through a sequence of binary classifiers, training each to answer a separate classification question. | |
C2565 | There are multiple uses of eigenvalues and eigenvectors: Eigenvalues and Eigenvectors have their importance in linear differential equations where you want to find a rate of change or when you want to maintain relationships between two variables. | |
C2566 | Connectionism is the philosophy of Edward Thorndike, which says that learning is a product between stimulus and response. Thorndike proposed three laws of connectionism: The law of effect, which says that a positive outcome strengthens an S-R bond, while a negative outcome weakens it. | |
C2567 | Just as ordinary least square regression is the method used to estimate coefficients for the best fit line in linear regression, logistic regression uses maximum likelihood estimation (MLE) to obtain the model coefficients that relate predictors to the target. | |
C2568 | Bayesian decision making is the process in which a decision is made based on the probability of a successful outcome, where this probability is informed by both prior information and new evidence that the decision maker obtains. | |
C2569 | In short, the problem with neural networks is that a number of parameter have to be set before any training can begin. However, there are no clear rules how to set these parameters. By combining genetic algorithms with neural networks (GANN), the genetic algorithm is used to find these parameters. | |
C2570 | The basic idea is to use existing IDSs as an alert source and then apply either off-line (using data mining) or on-line (using machine learning) alert processing to reduce the number of false positives. Moreover, owing to their complementary nature, both approaches can also be used together. | |
C2571 | The Least Squares AssumptionsUseful Books for This Topic: ASSUMPTION #1: The conditional distribution of a given error term given a level of an independent variable x has a mean of zero. ASSUMPTION #2: (X,Y) for all n are independently and identically distributed. ASSUMPTION #3: Large outliers are unlikely.More items• | |
C2572 | LDA stands for Latent Dirichlet Allocation, and it is a type of topic modeling algorithm. The purpose of LDA is to learn the representation of a fixed number of topics, and given this number of topics learn the topic distribution that each document in a collection of documents has. | |
C2573 | Examples of texts to create. Live multimodal texts include dance, performance, oral storytelling, and presentations. Meaning is conveyed through combinations of various modes such as gestural, spatial, audio, and oral language. | |
C2574 | Gradient descent is used because it guarantees that, on a convex surface, every modification of the parameters will take you in the right direction toward optimization. Genetic algorithms have no such guarantee. | |
C2575 | The null hypothesis is generally denoted as H0. It states the exact opposite of what an investigator or an experimenter predicts or expects. It basically defines the statement which states that there is no exact or actual relationship between the variables. The alternative hypothesis is generally denoted as H1. | |
C2576 | A Lorenz curve is a graphical representation of income inequality or wealth inequality developed by American economist Max Lorenz in 1905. The graph plots percentiles of the population on the horizontal axis according to income or wealth. | |
C2577 | The exponential distribution is a continuous probability distribution used to model the time we need to wait before a given event occurs. It is the continuous counterpart of the geometric distribution, which is instead discrete. | |
C2578 | Neural networks are widely used in unsupervised learning in order to learn better representations of the input data. This process doesn't give you clusters, but it creates meaningful representations that can be used for clustering. You could, for instance, run a clustering algorithm on the hidden layer's activations. | |
C2579 | So here are some signs you're highly intelligent, even if you don't feel like it.You're Empathetic And Compassionate. Andrew Zaeh for Bustle. You're Curious About The World. You're Observant. You Have Self-Control. You Have A Good Working Memory. You Like To Go With The Flow.More items• | |
C2580 | You do not need to learn linear algebra before you get started in machine learning, but at some time you may wish to dive deeper. It will give you the tools to help you with the other areas of mathematics required to understand and build better intuitions for machine learning algorithms. | |
C2581 | Backpropagation and computing gradients. In other words, backpropagation aims to minimize the cost function by adjusting network's weights and biases. The level of adjustment is determined by the gradients of the cost function with respect to those parameters. | |
C2582 | For course 2018 - Take a look at @hiromi post on that: The rule of thumb for determining the embedding size is the cardinality size divided by 2, but no bigger than 50. | |
C2583 | Theoretically, convolution are linear operations on the signal or signal modifiers, whereas correlation is a measure of similarity between two signals. Also, correlation or auto-correlation is the measure of similarity of signal with itself which has a different time lag between them. | |
C2584 | Examples of Unbiased Sample Kathy wants to know how many students in her city use the internet for learning purposes. She used an email poll. Based on the replies to her poll, she found that 83% of those surveyed used the internet. Kathy's sample is biased as she surveyed only the students those who use the internet. | |
C2585 | PCA is the simplest of the true eigenvector-based multivariate analyses and is closely related to factor analysis. Factor analysis typically incorporates more domain specific assumptions about the underlying structure and solves eigenvectors of a slightly different matrix. | |
C2586 | Word2Vec, Doc2Vec and Glove are semi-supervised learning algorithms and they are Neural Word Embeddings for the sole purpose of Natural Language Processing. Specifically Word2vec is a two-layer neural net that processes text. | |
C2587 | The top-1 error:- The percentage of time that the classifier did not give the correct class the highest probability score. The top-5 error:- The percentage of time that the classifier did not involve the correct class among the top 5 probabilities or guesses. | |
C2588 | As part of the GAN series, this article looks into ways on how to improve GAN.In particular,Change the cost function for a better optimization goal.Add additional penalties to the cost function to enforce constraints.Avoid overconfidence and overfitting.Better ways of optimizing the model.Add labels. | |
C2589 | The difference is in the method of removing the negative values, while variance squares it, the mean deviation takes the absolute values (mod). Mean deviation is basically average of the absolute distance of all data points from the mean. | |
C2590 | Caffe2 is was intended as a framework for production edge deployment whereas TensorFlow is more suited towards server production and research. TensorFlow is aimed for researchers and servers while Caffe2 is aimed towards mobile phones and other (relatively) computationally constrained platforms. | |
C2591 | Machine Learning will revolutionize Psychometrics. IRT psychometrics are usually based upon logistic regression techniques. Machine Learning can be utilized to reveal candidate's strengths in the in the social components of collaborative problem solving, such as perspective taking, participation, and social regulation. | |
C2592 | The Chi square test is used to compare a group with a value, or to compare two or more groups, always using categorical data. | |
C2593 | Offline Education – Also referred to as traditional training. Offline Education means a student needs to go in a school, in a classroom, and attend a class face to face with a teacher. So you got it, the main difference between online education vs offline education is the location of the the learning process.26/08/2020 | |
C2594 | More precisely, if we consider repeated sampling from our population, for large sample sizes, the distribution (across repeated samples) of the ordinary least squares estimates of the regression coefficients follow a normal distribution. | |
C2595 | Gradient boosting is a technique for building an ensemble of weak models such that the predictions of the ensemble minimize a loss function. Gradient descent "descends" the gradient by introducing changes to parameters, whereas gradient boosting descends the gradient by introducing new models. | |
C2596 | The outcome variable and dependent variable are used synonymously. However, they are not exactly the same: the outcome variable is defined as the presumed effect in a non-experimental study, where the dependent variable is the presumed effect in an experimental study1. | |
C2597 | Bayesian probability is an interpretation of probability whereby we treat facts about the world, or hypotheses, as inherently random. They might be true or they might not be true; it depends on the fixed evidence we have available. | |
C2598 | Decision Tree - Classification. Decision tree builds classification or regression models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes | |
C2599 | How to Conduct Hypothesis TestsState the hypotheses. Every hypothesis test requires the analyst to state a null hypothesis and an alternative hypothesis. Formulate an analysis plan. The analysis plan describes how to use sample data to accept or reject the null hypothesis. Analyze sample data. Interpret the results. |
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