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C10800 | 1)It enhances the learner's motivation and leads to more effective learning. 2)It provides learners with more opportunities for English communication in a non-native environment. 3) It caters to the individual needs of learners at all levels. | |
C10801 | 7 Techniques to Handle Imbalanced DataUse the right evaluation metrics. Resample the training set. Use K-fold Cross-Validation in the right way. Ensemble different resampled datasets. Resample with different ratios. Cluster the abundant class. Design your own models. | |
C10802 | Decision trees use multiple algorithms to decide to split a node in two or more sub-nodes. Decision tree splits the nodes on all available variables and then selects the split which results in most homogeneous sub-nodes. The algorithm selection is also based on type of target variables. | |
C10803 | Causation explicitly applies to cases where action A {quote:right}Causation explicitly applies to cases where action A causes outcome B. {/quote} causes outcome B. On the other hand, correlation is simply a relationship. Action A relates to Action B—but one event doesn't necessarily cause the other event to happen. | |
C10804 | To find the mean, add up the values in the data set and then divide by the number of values that you added. To find the median, list the values of the data set in numerical order and identify which value appears in the middle of the list. | |
C10805 | Constraint satisfaction problems (CSPs) are mathematical questions defined as a set of objects whose state must satisfy a number of constraints or limitations. CSPs represent the entities in a problem as a homogeneous collection of finite constraints over variables, which is solved by constraint satisfaction methods. | |
C10806 | TL;DR: Sparsity means most of the weights are 0. This can lead to an increase in space and time efficiency. Detailed version: In general, neural networks are represented as tensors. Each layer of neurons is represented by a matrix. A matrix in which most entries are 0 is called a sparse matrix. | |
C10807 | 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. | |
C10808 | The difference between Dense and Sparse. When used as adjectives, dense means having relatively high density, whereas sparse means having widely spaced intervals. Dense is also noun with the meaning: a thicket. | |
C10809 | The random variable in the chi-square distribution is the sum of squares of df standard normal variables, which must be independent. The chi-square distribution curve is skewed to the right, and its shape depends on the degrees of freedom df. For df > 90, the curve approximates the normal distribution. | |
C10810 | A boxplot is a standardized way of displaying the distribution of data based on a five number summary (“minimum”, first quartile (Q1), median, third quartile (Q3), and “maximum”). It can also tell you if your data is symmetrical, how tightly your data is grouped, and if and how your data is skewed. | |
C10811 | There are four types of artificial intelligence: reactive machines, limited memory, theory of mind and self-awareness. | |
C10812 | Properties of a normal distribution The mean, mode and median are all equal. The curve is symmetric at the center (i.e. around the mean, μ). Exactly half of the values are to the left of center and exactly half the values are to the right. The total area under the curve is 1. | |
C10813 | In statistics, bivariate data is data on each of two variables, where each value of one of the variables is paired with a value of the other variable. For example, bivariate data on a scatter plot could be used to study the relationship between stride length and length of legs. | |
C10814 | The empirical (or experimental) probability of an event is an "estimate" that an event will occur based upon how often the event occurred after collecting data from an experiment in a large number of trials. With theoretical probability, you do not actually conduct an experiment. | |
C10815 | In general, data structures are used to implement the physical forms of abstract data types. This can be translated into a variety of applications, such as displaying a relational database as a binary tree. In programming languages, data structures are used to organize code and information in a digital space. | |
C10816 | Counterintuitive as it may be, supervised algorithms (particularly logistic regression and random forest) tend to outperform unsupervised ones on discrete classification and categorization tasks, where data is relatively structured and well-labeled. | |
C10817 | From Wikipedia, the free encyclopedia. Error-driven learning is a sub-area of machine learning concerned with how an agent ought to take actions in an environment so as to minimize some error feedback. It is a type of reinforcement learning. | |
C10818 | Cross-Validation is a very powerful tool. It helps us better use our data, and it gives us much more information about our algorithm performance. In complex machine learning models, it's sometimes easy not pay enough attention and use the same data in different steps of the pipeline. | |
C10819 | The loss given default (LGD) is an important calculation for financial institutions projecting out their expected losses due to borrowers defaulting on loans. The expected loss of a given loan is calculated as the LGD multiplied by both the probability of default and the exposure at default. | |
C10820 | A target function, in machine learning, is a method for solving a problem that an AI algorithm parses its training data to find. Once an algorithm finds its target function, that function can be used to predict results (predictive analysis). | |
C10821 | Main limitation of Linear Regression is the assumption of linearity between the dependent variable and the independent variables. In the real world, the data is rarely linearly separable. It assumes that there is a straight-line relationship between the dependent and independent variables which is incorrect many times. | |
C10822 | For years, people have been forecasting weather patterns, economic and political events, sports outcomes, and more. Because we try to predict so many different events, there are a wide variety of ways in which forecasts can be developed. | |
C10823 | It's a Gaussian distribution in more than one dimension at a time. Nothing tricky in the combining itself, just a straightforward Cartesian-style combination. | |
C10824 | In Gradient Descent or Batch Gradient Descent, we use the whole training data per epoch whereas, in Stochastic Gradient Descent, we use only single training example per epoch and Mini-batch Gradient Descent lies in between of these two extremes, in which we can use a mini-batch(small portion) of training data per epoch | |
C10825 | In Bayesian statistics, a maximum a posteriori probability (MAP) estimate is an estimate of an unknown quantity, that equals the mode of the posterior distribution. The MAP can be used to obtain a point estimate of an unobserved quantity on the basis of empirical data. | |
C10826 | The work efficiency formula is efficiency = output / input, and you can multiply the result by 100 to get work efficiency as a percentage. This is used across different methods of measuring energy and work, whether it's energy production or machine efficiency. | |
C10827 | Frequency distribution in statistics is a representation that displays the number of observations within a given interval. Frequency distributions are particularly useful for normal distributions, which show the observations of probabilities divided among standard deviations. | |
C10828 | Both PLS and PCA are used for dimension reduction. Partial Least Squares, use the annotated label to maximize inter-class variance. Principal components are focus on maximize correlation. The main difference is that the PCA is unsupervised method and PLS is supervised method. | |
C10829 | Randomization in an experiment means random assignment of treatments. This way we can eliminate any possible biases that may arise in the experiment. Good. Randomization in an experiment is important because it minimizes bias responses. | |
C10830 | A low R-squared value indicates that your independent variable is not explaining much in the variation of your dependent variable - regardless of the variable significance, this is letting you know that the identified independent variable, even though significant, is not accounting for much of the mean of your | |
C10831 | Observer bias (also called experimenter bias or research bias) is the tendency to see what we expect to see, or what we want to see. When a researcher studies a certain group, they usually come to an experiment with prior knowledge and subjective feelings about the group being studied. | |
C10832 | If X and Y are normed vector spaces (a special type of TVS), then L is bounded if and only if there exists some M ≥ 0 such that for all x in X, ||Lx||Y ≤ M ||x||X. The smallest such M, denoted by ||L||, is called the operator norm of L. | |
C10833 | The weights of artificial neural networks must be initialized to small random numbers. This is because this is an expectation of the stochastic optimization algorithm used to train the model, called stochastic gradient descent. About the need for nondeterministic and randomized algorithms for challenging problems. | |
C10834 | Topic modeling is a type of statistical modeling for discovering the abstract “topics” that occur in a collection of documents. Latent Dirichlet Allocation (LDA) is an example of topic model and is used to classify text in a document to a particular topic. | |
C10835 | Linear Regression, intuitively is a regression algorithm with a Linear approach. We try to predict a continuous value of a given data point by generalizing on the data that we have in hand. The linear part indicates that we are using a linear approach in generalizing over the data. | |
C10836 | Two different learning models were introduced that can be used as part of the word2vec approach to learn the word embedding; they are: Continuous Bag-of-Words, or CBOW model. Continuous Skip-Gram Model. | |
C10837 | The multinomial distribution models the outcome of n experiments, where the outcome of each trial has a categorical distribution, such as rolling a k-sided dice n times. While the trials are independent, their outcomes X are dependent because they must be summed to n. | |
C10838 | Factor analysis is a statistical data reduction and analysis technique that strives to explain correlations among multiple outcomes as the result of one or more underlying explanations, or factors. The technique involves data reduction, as it attempts to represent a set of variables by a smaller number. | |
C10839 | The C parameter trades off misclassification of training examples against simplicity of the decision surface. A low C makes the decision surface smooth, while a high C aims at classifying all training examples correctly by giving the model freedom to select more samples as support vectors. | |
C10840 | Java, Python, Lisp, Prolog, and C++ are major AI programming language used for artificial intelligence capable of satisfying different needs in the development and designing of different software. It answers the question, 'what is the language used for artificial intelligence? | |
C10841 | When instead of one, there are two independent samples then K-S two sample test can be used to test the agreement between two cumulative distributions. The null hypothesis states that there is no difference between the two distributions. The D-statistic is calculated in the same manner as the K-S One Sample Test. | |
C10842 | Define Population Distribution; and sketch a graph: The population distribution gives the values of the variable for all the individuals in the population. The sampling distribution shows the statistic values from all the possible samples of the same size from the population. It is a distribution of the statistic. | |
C10843 | Each tree is created from a different sample of rows and at each node, a different sample of features is selected for splitting. Each of the trees makes its own individual prediction. These predictions are then averaged to produce a single result. | |
C10844 | Since the observed values for y vary about their means y, the multiple regression model includes a term for this variation. In words, the model is expressed as DATA = FIT + RESIDUAL, where the "FIT" term represents the expression 0 + 1x1 + 2x2 + xp. | |
C10845 | The binomial distribution is a common discrete distribution used in statistics, as opposed to a continuous distribution, such as the normal distribution. | |
C10846 | As a rule of thumb, I'd say that SVMs are great for relatively small data sets with fewer outliers. Also, deep learning algorithms require much more experience: Setting up a neural network using deep learning algorithms is much more tedious than using an off-the-shelf classifiers such as random forests and SVMs. | |
C10847 | It does this by using a means of representing knowledge called, semantic networks. These use graphical methods to describe relationships between concepts and events to describe common sense activities. | |
C10848 | Explanation: Randomized quick sort chooses a random element as a pivot. It is done so as to avoid the worst case of quick sort in which the input array is already sorted. | |
C10849 | Tokenization breaks the raw text into words, sentences called tokens. These tokens help in understanding the context or developing the model for the NLP. The tokenization helps in interpreting the meaning of the text by analyzing the sequence of the words. Tokenization can be done to either separate words or sentences. | |
C10850 | 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. | |
C10851 | A batch is the complete dataset. Iterations is the number of batches of data the algorithm has seen (or simply the number of passes the algorithm has done on the dataset). Epochs is the number of times a learning algorithm sees the complete dataset. | |
C10852 | In very rare cases, you can have a false-positive result. This means you're not pregnant but the test says you are. You could have a false-positive result if you have blood or protein in your pee. Certain drugs, such as tranquilizers, anticonvulsants, hypnotics, and fertility drugs, could cause false-positive results. | |
C10853 | Hierarchical clustering is an instance of the agglomerative or bottom-up approach, where we start with each data point as its own cluster and then combine clusters based on some similarity measure. | |
C10854 | Specifically, we can compute the probability that a discrete random variable equals a specific value (probability mass function) and the probability that a random variable is less than or equal to a specific value (cumulative distribution function). | |
C10855 | - Population Based Training - It is open-source. The library connected with DeepMind's paper ( [1711.09846] Population Based Training of Neural Networks ) should be enough to start with something. | |
C10856 | The geometric distribution represents the number of failures before you get a success in a series of Bernoulli trials. This discrete probability distribution is represented by the probability density function: f(x) = (1 − p)x − 1p. | |
C10857 | SVD, or Singular Value Decomposition, is one of several techniques that can be used to reduce the dimensionality, i.e., the number of columns, of a data set. SVD is an algorithm that factors an m x n matrix, M, of real or complex values into three component matrices, where the factorization has the form USV*. | |
C10858 | Fourier analysis is used in electronics, acoustics, and communications. Many waveforms consist of energy at a fundamental frequency and also at harmonic frequencies (multiples of the fundamental). The relative proportions of energy in the fundamental and the harmonics determines the shape of the wave. | |
C10859 | One is that larger learning rates increase the noise on the stochastic gradient, which acts as an implicit regularizer. If you find your model overfitting with a low learning rate, the minima you're falling into might actually be too sharp and cause the model to generalize poorly. | |
C10860 | Emotional artificial intelligence, also called Emotion AI or affective computing, is being used to develop machines that are capable of reading, interpreting, responding to, and imitating human affect—the way we, as humans, experience and express emotions. | |
C10861 | �(�) = x �−1 e−xdx. then f(x �, �) will be a probability density function since it is nonnegative and it integrates | to one. Definition. The distribution with p.d.f. f(x �, �) is called Gamma distribution with | parameters � and � and it is denoted as �(�, �). | |
C10862 | In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned. | |
C10863 | Unsupervised learning is the Holy Grail of Deep Learning. The goal of unsupervised learning is to create general systems that can be trained with little data. Today Deep Learning models are trained on large supervised datasets. Meaning that for each data, there is a corresponding label. | |
C10864 | Growth curve analysis, or trajectory analysis, is a specialized set of techniques for modeling change over time. Growth curve analysis is a data reduction technique: it is used to summarize longitudinal data into a smooth curve defined by relatively few parameters for descriptive purposes or further inquiry. | |
C10865 | 9:3122:36Suggested clip · 72 seconds3.5: Mathematics of Gradient Descent - Intelligence and Learning YouTubeStart of suggested clipEnd of suggested clip | |
C10866 | In simplest manner, svm without kernel is a single neural network neuron but with different cost function. If you add a kernel function, then it is comparable with 2 layer neural nets. In simplest manner, svm without kernel is a single neural network neuron but with different cost function. | |
C10867 | Process Mining Process mining uses event commits and application logs to decipher a business process. Process DiscoveryAI-powered process discovery uses computer vision and machine intelligence to observe users and uncover deep process variants from digital traces of human work. | |
C10868 | R-squared measures the proportion of the variation in your dependent variable (Y) explained by your independent variables (X) for a linear regression model. Adjusted R-squared adjusts the statistic based on the number of independent variables in the model. | |
C10869 | Businesses use data mining techniques to identify potentially useful information in their data, in order to aid business decision making processes. Machine learning is utilized in order to improve these decision making models. | |
C10870 | Though the name is a mouthful, the concept behind this is very simple. To tell briefly, LDA imagines a fixed set of topics. Each topic represents a set of words. 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. | |
C10871 | It has become the default activation function for many types of neural networks because a model that uses it is easier to train and often achieves better performance. In this tutorial, you will discover the rectified linear activation function for deep learning neural networks. | |
C10872 | An interval scale is one where there is order and the difference between two values is meaningful. Examples of interval variables include: temperature (Farenheit), temperature (Celcius), pH, SAT score (200-800), credit score (300-850). | |
C10873 | Variability refers to how spread out a group of data is. The common measures of variability are the range, IQR, variance, and standard deviation. Data sets with similar values are said to have little variability while data sets that have values that are spread out have high variability. | |
C10874 | Apply the sigmoid function as the final activation function of CNN network which is as below. The train and validation data set is little bit different, it has additional images that has multiple classes in a given images. | |
C10875 | Target is the "correct" or desidered value for the respose associate to one input. Usually, this value will be compared with the output (the response of the neural network) to guide the learning process involving the weight changes. | |
C10876 | The Kalman filter uses a system's dynamic model (e.g., physical laws of motion), known control inputs to that system, and multiple sequential measurements (such as from sensors) to form an estimate of the system's varying quantities (its state) that is better than the estimate obtained by using only one measurement | |
C10877 | To measure test-retest reliability, you conduct the same test on the same group of people at two different points in time. Then you calculate the correlation between the two sets of results. | |
C10878 | ELIZA is an early natural language processing computer program created from 1964 to 1966 at the MIT Artificial Intelligence Laboratory by Joseph Weizenbaum. As such, ELIZA was one of the first chatterbots and one of the first programs capable of attempting the Turing test. | |
C10879 | In machine learning, kernel methods are a class of algorithms for pattern analysis, whose best known member is the support vector machine (SVM). This approach is called the "kernel trick". Kernel functions have been introduced for sequence data, graphs, text, images, as well as vectors. | |
C10880 | A person who engages in banditry is known as a bandit and primarily commits crimes such as extortion, robbery, and murder, either as an individual or in groups. Banditry is a vague concept of criminality and in modern usage can be synonymous for gangsterism, brigandage, marauding, and thievery. | |
C10881 | Consider that:You choose door 1. Monty shows you a goat behind door 2.If the car is behind door 1, Monty will not choose it. If the car is behind door 2, Monty will always open door 3, as he never reveals the car.If the car is behind door 3, Monty will open door 2 100% of the time. | |
C10882 | The tool of normal approximation allows us to approximate the probabilities of random variables for which we don't know all of the values, or for a very large range of potential values that would be very difficult and time consuming to calculate. | |
C10883 | Stochastic control or stochastic optimal control is a sub field of control theory that deals with the existence of uncertainty either in observations or in the noise that drives the evolution of the system. | |
C10884 | Bayesian inference refers to statistical inference where uncertainty in inferences is quantified using probability. Statistical models specify a set of statistical assumptions and processes that represent how the sample data is generated. Statistical models have a number of parameters that can be modified. | |
C10885 | Now, every textbook on linear algebra gives the following definition of a linear operator: an operator T: V—> W between two vector spaces V and W over the same field ! F is said to be linear if it satisfies the conditions of additivity, viz. T(u + v)=T(u)+T(v) | |
C10886 | 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. | |
C10887 | Yes. The Sobel operator approximates a horizontal gradient and a vertical gradient on an image by convolving it with two kernels, and . The kernel itself can be decomposed into the product of a averaging operator and a differentiating operator. | |
C10888 | Key Differences between AI, ML, and NLP Machine Learning and Artificial Intelligence are the terms often used together but aren't the same. ML is an application of AI. The main technology used in NLP (Natural Language Processing) which mainly focuses on teaching natural/human language to computers. | |
C10889 | Log-loss measures the accuracy of a classifier. It is used when the model outputs a probability for each class, rather than just the most likely class. Log-loss measures the accuracy of a classifier. It is used when the model outputs a probability for each class, rather than just the most likely class. | |
C10890 | Logistic regression can be binomial, ordinal or multinomial. Binomial or binary logistic regression deals with situations in which the observed outcome for a dependent variable can have only two possible types, "0" and "1" (which may represent, for example, "dead" vs. "alive" or "win" vs. "loss"). | |
C10891 | Standard interpretation of the ordered logit coefficient is that for a one unit increase in the predictor, the response variable level is expected to change by its respective regression coefficient in the ordered log-odds scale while the other variables in the model are held constant. | |
C10892 | The essential difference between the set and the multiset is that in a set the keys must be unique, while a multiset permits duplicate keys. In both sets and multisets, the sort order of components is the sort order of the keys, so the components in a multiset that have duplicate keys may appear in any order. | |
C10893 | The disadvantages: Convenience samples do not produce representative results. If you need to extrapolate to the target population, convenience samples aren't going to get you there. | |
C10894 | ReLu refers to the Rectifier Unit, the most commonly deployed activation function for the outputs of the CNN neurons. Mathematically, it's described as: Unfortunately, the ReLu function is not differentiable at the origin, which makes it hard to use with backpropagation training. | |
C10895 | Every neuron has input connections and output connections. These connections simulate the behavior of the synapses in the brain. The same way that synapses in the brain transfer the signal from one neuron to another, connections pass information between artificial neurons. | |
C10896 | The main benefit claimed for feature selection, which is the main focus in this manuscript, is that it increases classification accuracy. It is believed that removing non-informative signal can reduce noise, and can increase the contrast between labelled groups. | |
C10897 | Bivariate analysis is one of the simplest forms of quantitative (statistical) analysis. It involves the analysis of two variables (often denoted as X, Y), for the purpose of determining the empirical relationship between them. Bivariate analysis can be helpful in testing simple hypotheses of association. | |
C10898 | In single-link (or single linkage) hierarchical clustering, we merge in each step the two clusters whose two closest members have the smallest distance (or: the two clusters with the smallest minimum pairwise distance). A single-link clustering also closely corresponds to a weighted graph's minimum spanning tree. | |
C10899 | K-means clustering is one of the simplest and popular unsupervised machine learning algorithms. In other words, the K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible. |
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