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C10000 | Based on recent research, we hypothesize that there is a neural network of consciousness in which the paraventricular nucleus formally serves as the control nucleus of arousal, which is closely related to the maintenance of consciousness, and the neurons in the posterior cerebral cortex. | |
C10001 | Five Advantages of Running Repeated Measures ANOVA as a Mixed Model. There are two ways to run a repeated measures analysis. The traditional way is to treat it as a multivariate test–each response is considered a separate variable. The other way is to it as a mixed model. | |
C10002 | Some researchers say that it is a good idea to mean center variables prior to computing a product term (to serve as a moderator term) because doing so will help reduce multicollinearity in a regression model. Other researchers say that mean centering has no effect on multicollinearity. | |
C10003 | If you increase your sample size you increase the precision of your estimates, which means that, for any given estimate / size of effect, the greater the sample size the more “statistically significant” the result will be. | |
C10004 | The Frobenius Norm of a matrix is defined as the square root of the sum of the squares of the elements of the matrix. Approach: Find the sum of squares of the elements of the matrix and then print the square root of the calculated value. | |
C10005 | Image annotation is the process of manually defining regions in an image and creating text-based descriptions of those regions. You can use the following image annotation tools to quickly and accurately build the ground truth for your computer vision models. | |
C10006 | Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance. | |
C10007 | The Neural Network is constructed from 3 type of layers: Input layer — initial data for the neural network. Hidden layers — intermediate layer between input and output layer and place where all the computation is done. Output layer — produce the result for given inputs. | |
C10008 | In information theory, the graph entropy is a measure of the information rate achievable by communicating symbols over a channel in which certain pairs of values may be confused. This measure, first introduced by Körner in the 1970s, has since also proven itself useful in other settings, including combinatorics. | |
C10009 | A true positive is an outcome where the model correctly predicts the positive class. Similarly, a true negative is an outcome where the model correctly predicts the negative class. A false positive is an outcome where the model incorrectly predicts the positive class. | |
C10010 | Solution: Double Q learning The solution involves using two separate Q-value estimators, each of which is used to update the other. Using these independent estimators, we can unbiased Q-value estimates of the actions selected using the opposite estimator [3]. | |
C10011 | The standard temporal/spatial Gaussian is a low-pass filter. It replaces every element of the input signal with a weighted average of its neighborhood. This causes blurring in time/space, which is the same as attenuating high-frequency components in the frequency domain. | |
C10012 | Overview. This Master's course aims to respond to the demand for data scientists with the skills to develop innovative computational intelligence applications, capable of analysing large amounts of complex data to inform businesses decisions and market strategies. | |
C10013 | The straight line is a trend line, designed to come as close as possible to all the data points. The trend line has a positive slope, which shows a positive relationship between X and Y. The points in the graph are tightly clustered about the trend line due to the strength of the relationship between X and Y. | |
C10014 | Cost Function It is a function that measures the performance of a Machine Learning model for given data. Cost Function quantifies the error between predicted values and expected values and presents it in the form of a single real number. Depending on the problem Cost Function can be formed in many different ways. | |
C10015 | In statistics and machine learning, lasso (least absolute shrinkage and selection operator; also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces. | |
C10016 | With an appropriate kernel function, we can solve any complex problem. Unlike in neural networks, SVM is not solved for local optima. It scales relatively well to high dimensional data. SVM models have generalization in practice, the risk of over-fitting is less in SVM. | |
C10017 | Andrew Ng says that batch normalization should be applied immediately before the non-linearity of the current layer. The authors of the BN paper said that as well, but now according to François Chollet on the keras thread, the BN paper authors use BN after the activation layer. | |
C10018 | Normalization usually means to scale a variable to have a values between 0 and 1, while standardization transforms data to have a mean of zero and a standard deviation of 1. This standardization is called a z-score, and data points can be standardized with the following formula: A z-score standardizes variables. | |
C10019 | A correlation between two variables does not imply causation. On the other hand, if there is a causal relationship between two variables, they must be correlated. Example: A study shows that there is a negative correlation between a student's anxiety before a test and the student's score on the test. | |
C10020 | MNIST Handwritten Digit Classification Dataset The MNIST dataset is an acronym that stands for the Modified National Institute of Standards and Technology dataset. It is a dataset of 60,000 small square 28×28 pixel grayscale images of handwritten single digits between 0 and 9. | |
C10021 | Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology (AI) such that the results of the solution can be understood by humans. | |
C10022 | Bayes Theorem for Modeling Hypotheses. Bayes Theorem is a useful tool in applied machine learning. It provides a way of thinking about the relationship between data and a model. A machine learning algorithm or model is a specific way of thinking about the structured relationships in the data. | |
C10023 | Voting and Averaging Based Ensemble Methods Voting and averaging are two of the easiest ensemble methods. Voting is used for classification and averaging is used for regression. In both methods, the first step is to create multiple classification/regression models using some training dataset. | |
C10024 | The three main metrics used to evaluate a classification model are accuracy, precision, and recall. Accuracy is defined as the percentage of correct predictions for the test data. It can be calculated easily by dividing the number of correct predictions by the number of total predictions. | |
C10025 | Artificial General Intelligence | |
C10026 | Why use Random Forest Algorithm Random forest algorithm can be used for both classifications and regression task. It provides higher accuracy through cross validation. Random forest classifier will handle the missing values and maintain the accuracy of a large proportion of data. | |
C10027 | In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. In supervised feature learning, features are learned using labeled input data. | |
C10028 | 15:3248:19Suggested clip · 37 secondsMotion 5 | How to Use Motion Tracking, Analyze Motion, and Match YouTubeStart of suggested clipEnd of suggested clip | |
C10029 | If the student does have multiple learning styles (multimodal), the advantages gained through multiple learning strategies include the ability to learn more quickly and at a deeper level so that recall at a later date will be more successful. Using various modes of learning also improves attention span. | |
C10030 | TensorFlow is Google's open source AI framework for machine learning and high performance numerical computation. TensorFlow is a Python library that invokes C++ to construct and execute dataflow graphs. It supports many classification and regression algorithms, and more generally, deep learning and neural networks. | |
C10031 | Cluster analysis is the task of grouping a set of data points in such a way that they can be characterized by their relevancy to one another. These types are Centroid Clustering, Density Clustering Distribution Clustering, and Connectivity Clustering. | |
C10032 | The LASSO method puts a constraint on the sum of the absolute values of the model parameters, the sum has to be less than a fixed value (upper bound). In order to do so the method apply a shrinking (regularization) process where it penalizes the coefficients of the regression variables shrinking some of them to zero. | |
C10033 | Whereas multiple regression predicts a single dependent variable from a set of multiple independent variables, canonical correlation simultaneously predicts multiple dependent variables from multiple independent variables. | |
C10034 | One disadvantage to this method is that outliers can cause less-than-optimal merging. Average Linkage, or group linkage: similarity is calculated between groups of objects, rather than individual objects. Centroid Method: each iteration merges the clusters with the most similar centroid. | |
C10035 | Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks. Tim Salimans, Diederik P. Kingma. Download PDF. We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction. | |
C10036 | Definition : A random experiment is an experiment or a process for which the outcome cannot be predicted with certainty. Definition : The sample space (denoted S) of a random experiment is the set of all possible outcomes. | |
C10037 | A frequency table is a chart that shows the popularity or mode of a certain type of data. When we look at frequency, we are looking at the number of times an event occurs within a given scenario. You can find the relative frequency by simply dividing the frequency number by the total number of values in the data set. | |
C10038 | Risk tolerance | |
C10039 | But when we say multiple regression, we mean only one dependent variable with a single distribution or variance. The predictor variables are more than one. To summarise multiple refers to more than one predictor variables but multivariate refers to more than one dependent variables. | |
C10040 | It is not rare that the results from a study that uses a convenience sample differ significantly with the results from the entire population. Since the sample is not representative of the population, the results of the study cannot speak for the entire population. This results to a low external validity of the study. | |
C10041 | If you use import numpy , all sub-modules and functions in the numpy module can only be accesses in the numpy. If you use from numpy import * , all functions will be loaded into the local namespace. For example array([1,2,3]) can then be used. | |
C10042 | Partitions: A collection of sets B1,B2,,Bn is said to partition the sample space if the sets (i) are mutually disjoint and (ii) have as union the entire sample space. A simple example of a partition is given by a set B, together with its complement B . 2. | |
C10043 | Fei-Fei Li, computer vision is defined as “a subset of mainstream artificial intelligence that deals with the science of making computers or machines visually enabled, i.e., they can analyze and understand an image.” Human vision starts at the biological camera's “eyes,” which takes one picture about every 200 | |
C10044 | In regression analysis, those factors are called variables. You have your dependent variable — the main factor that you're trying to understand or predict. | |
C10045 | Integral testIt is possible to prove that the harmonic series diverges by comparing its sum with an improper integral. Additionally, the total area under the curve y = 1x from 1 to infinity is given by a divergent improper integral:More items | |
C10046 | The law of large numbers states that the sample mean of independent and identically distributed observations converges to a certain value. The central limit theorem describes the distribution of the difference between the sample mean and that value. | |
C10047 | The test statistic is used to calculate the p-value. A test statistic measures the degree of agreement between a sample of data and the null hypothesis. This Z-value corresponds to a p-value of 0.0124. Because this p-value is less than α, you declare statistical significance and reject the null hypothesis. | |
C10048 | TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. | |
C10049 | A sample refers to a smaller, manageable version of a larger group. It is a subset containing the characteristics of a larger population. Samples are used in statistical testing when population sizes are too large for the test to include all possible members or observations. | |
C10050 | In a nutshell, hierarchical linear modeling is used when you have nested data; hierarchical regression is used to add or remove variables from your model in multiple steps. Knowing the difference between these two seemingly similar terms can help you determine the most appropriate analysis for your study. | |
C10051 | A metric is a function that is used to judge the performance of your model. Metric functions are similar to loss functions, except that the results from evaluating a metric are not used when training the model. Note that you may use any loss function as a metric. | |
C10052 | Partial least squares discriminant analysis (PLS-DA) is a variant used when the Y is categorical. PLS is used to find the fundamental relations between two matrices (X and Y), i.e. a latent variable approach to modeling the covariance structures in these two spaces. | |
C10053 | It helps you to find which situation needs an action. Helps you to discover which action yields the highest reward over the longer period. Reinforcement Learning also provides the learning agent with a reward function. It also allows it to figure out the best method for obtaining large rewards.21/09/2020 | |
C10054 | Micro-level adaptive instruction: The main feature of this approach is to utilize on-task rather than pre-task measurement to diagnose the students' learning behaviors and performance so as to adapt the instruction at the micro-level. Typical examples include one-on-one tutoring and intelligent tutoring systems. | |
C10055 | In statistics, multivariate analysis of variance (MANOVA) is a procedure for comparing multivariate sample means. As a multivariate procedure, it is used when there are two or more dependent variables, and is often followed by significance tests involving individual dependent variables separately. | |
C10056 | To sum up: to build a conditional random field, you just define a bunch of feature functions (which can depend on the entire sentence, a current position, and nearby labels), assign them weights, and add them all together, transforming at the end to a probability if necessary. | |
C10057 | In simple random sampling, each member of a population has an equal chance of being included in the sample. Also, each combination of members of the population has an equal chance of composing the sample. Those two properties are what defines simple random sampling. | |
C10058 | A dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. In a data warehouse, dimensions provide structured labeling information to otherwise unordered numeric measures. The dimension is a data set composed of individual, non-overlapping data elements. | |
C10059 | If you have outliers, the best way is to use a clustering algorithm that can handle them. For example DBSCAN clustering is robust against outliers when you choose minpts large enough. Don't use k-means: the squared error approach is sensitive to outliers. But there are variants such as k-means-- for handling outliers. | |
C10060 | Dilution (also called Dropout) is a regularization technique for reducing overfitting in artificial neural networks by preventing complex co-adaptations on training data. It is an efficient way of performing model averaging with neural networks. The term dilution refers to the thinning of the weights. | |
C10061 | ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is an early single-layer artificial neural network and the name of the physical device that implemented this network. The network uses memistors. It is based on the McCulloch–Pitts neuron. It consists of a weight, a bias and a summation function. | |
C10062 | The Logit Model, better known as Logistic Regression is a binomial regression model. Logistic Regression is used to associate with a vector of random variables to a binomial random variable. Logistic regression is a special case of a generalized linear model. It is widely used in machine learning. | |
C10063 | K-means is a least-squares optimization problem, so is PCA. k-means tries to find the least-squares partition of the data. PCA finds the least-squares cluster membership vector. | |
C10064 | The assumption of homogeneity of variance means that the level of variance for a particular variable is constant across the sample. In ANOVA, when homogeneity of variance is violated there is a greater probability of falsely rejecting the null hypothesis. | |
C10065 | Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others. It is also called ascertainment bias in medical fields. In other words, findings from biased samples can only be generalized to populations that share characteristics with the sample. | |
C10066 | An object detector that uses anchor boxes can process an entire image at once, making real-time object detection systems possible. Because a convolutional neural network (CNN) can process an input image in a convolutional manner, a spatial location in the input can be related to a spatial location in the output. | |
C10067 | The normal distribution is a continuous probability distribution that is symmetrical on both sides of the mean, so the right side of the center is a mirror image of the left side. The area under the normal distribution curve represents probability and the total area under the curve sums to one. | |
C10068 | Best Way to Analyze Likert Item Data: Two Sample T-Test versus Mann-WhitneyParametric tests, such as the 2-sample t-test, assume a normal, continuous distribution. Nonparametric tests, such as the Mann-Whitney test, do not assume a normal or a continuous distribution. | |
C10069 | The null hypothesis is the one to be tested and the alternative is everything else. In our example, The null hypothesis would be: The mean data scientist salary is 113,000 dollars. While the alternative: The mean data scientist salary is not 113,000 dollars. | |
C10070 | Electronic apparatus which generates random numbers, used as targets in a psi test. A basic form of REG is an electronic coin-tossing machine, generating a series of "heads and tails" outputs. Other REGs have more complex outputs. | |
C10071 | For many continuous random variables, we can define an extremely useful function with which to calculate probabilities of events associated to the random variable. In short, the PDF of a continuous random variable is the derivative of its CDF. | |
C10072 | The one-way analysis of variance (ANOVA) is used to determine whether there are any statistically significant differences between the means of three or more independent (unrelated) groups. | |
C10073 | How to Compare Data SetsCenter. Graphically, the center of a distribution is the point where about half of the observations are on either side.Spread. The spread of a distribution refers to the variability of the data. Shape. The shape of a distribution is described by symmetry, skewness, number of peaks, etc.Unusual features. | |
C10074 | 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. | |
C10075 | An estimator of a given parameter is said to be unbiased if its expected value is equal to the true value of the parameter. In other words, an estimator is unbiased if it produces parameter estimates that are on average correct. | |
C10076 | Here are just some of the many uses of eigenvectors and eigenvalues:Using singular value decomposition for image compression. Deriving Special Relativity is more natural in the language of linear algebra. Spectral Clustering. Dimensionality Reduction/PCA. Low rank factorization for collaborative prediction.More items | |
C10077 | How to Calculate VarianceFind the mean of the data set. Add all data values and divide by the sample size n.Find the squared difference from the mean for each data value. Subtract the mean from each data value and square the result.Find the sum of all the squared differences. Calculate the variance. | |
C10078 | Events A and B are independent if the equation P(A∩B) = P(A) · P(B) holds true. You can use the equation to check if events are independent; multiply the probabilities of the two events together to see if they equal the probability of them both happening together. | |
C10079 | In probability theory and statistics, a covariance matrix (also known as auto-covariance matrix, dispersion matrix, variance matrix, or variance–covariance matrix) is a square matrix giving the covariance between each pair of elements of a given random vector. | |
C10080 | A Classification and Regression Tree(CART) is a predictive algorithm used in machine learning. It explains how a target variable's values can be predicted based on other values. It is a decision tree where each fork is a split in a predictor variable and each node at the end has a prediction for the target variable. | |
C10081 | The mean of the log-normal distribution is m = e μ + σ 2 2 , m = e^{\mu+\frac{\sigma^2}{2}}, m=eμ+2σ2, which also means that μ \mu μ can be calculated from m m m: μ = ln m − 1 2 σ 2 . | |
C10082 | Factor analysis aims to find independent latent variables. The theory behind factor analytic methods is that the information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a dataset. | |
C10083 | A relatively new method of dimensionality reduction is the autoencoder. Autoencoders are a branch of neural network which attempt to compress the information of the input variables into a reduced dimensional space and then recreate the input data set. This is where the information from the input has been compressed. | |
C10084 | The geometric mean differs from the arithmetic average, or arithmetic mean, in how it is calculated because it takes into account the compounding that occurs from period to period. Because of this, investors usually consider the geometric mean a more accurate measure of returns than the arithmetic mean. | |
C10085 | Prior probability, in Bayesian statistical inference, is the probability of an event before new data is collected. This is the best rational assessment of the probability of an outcome based on the current knowledge before an experiment is performed. | |
C10086 | In mathematics, a Fourier series (/ˈfʊrieɪ, -iər/) is a periodic function composed of harmonically related sinusoids, combined by a weighted summation. The discrete-time Fourier transform is an example of Fourier series. The process of deriving the weights that describe a given function is a form of Fourier analysis. | |
C10087 | Level of significance (alpha error): 0.05. The test is run, and the p value obtained was 0.02 (p=0.02). What does the p value indicate? It tells us that if the null hypothesis were true, the probability of obtaining such a difference (or more extreme difference) in timing between the two fighters is 2 in 100, or 0.02. | |
C10088 | In General, A Discriminative model models the decision boundary between the classes. A Generative Model explicitly models the actual distribution of each class. A Discriminative model learns the conditional probability distribution p(y|x). Both of these models were generally used in supervised learning problems. | |
C10089 | The correlation coefficient is a measure of the degree of linear association between two continuous variables, i.e. when plotted together, how close to a straight line is the scatter of points. Both x and y must be continuous random variables (and Normally distributed if the hypothesis test is to be valid). | |
C10090 | The output unit is used to present soft and hardcopy of information. The VDU (Visual Display Unit or Monitor) and printer are common output units. There are many categories of display units available for computer. | |
C10091 | Exploratory Data Analysis tools (EDA) are a diverse mix of tools that are mainly used to explore data, to find trends, exception, rules, correlation and other statistical feedback. These tools are something fairly technical (R | SPSS) or the fairly visual (Visual Intelligence | Tableau Software) stack. | |
C10092 | In Grid Search, the data scientist sets up a grid of hyperparameter values and for each combination, trains a model and scores on the testing data. By contrast, Random Search sets up a grid of hyperparameter values and selects random combinations to train the model and score. | |
C10093 | Best Image Processing Projects CollectionLicense plate recognition.Face Emotion recognition.Face recognition.Cancer detection.Object detection.Pedestrian detection.Lane detection for ADAS.Blind assistance systems.More items | |
C10094 | Mean filtering is a simple, intuitive and easy to implement method of smoothing images, i.e. reducing the amount of intensity variation between one pixel and the next. It is often used to reduce noise in images. | |
C10095 | Population change, defined generally, is the difference in the size of a population between the end and the beginning of a given time period (usually one year). Population change has two components: natural population change (the number of live births minus the number of deaths); | |
C10096 | Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. | |
C10097 | Yes it can be the same. In fact, If you don't write a meta description, Google will take portion of your site's content, which it sees are relevant, and make it your meta description. A meta description is important because it is one of the first things that users will see in the SERPs. | |
C10098 | In a normal distribution, the mean and the median are the same number while the mean and median in a skewed distribution become different numbers: A left-skewed, negative distribution will have the mean to the left of the median. A right-skewed distribution will have the mean to the right of the median. | |
C10099 | The Minkowski distance defines a distance between two points in a normed vector space. Minkowski Distance. When p=1 , the distance is known as the Manhattan distance. When p=2 , the distance is known as the Euclidean distance. In the limit that p --> +infinity , the distance is known as the Chebyshev distance. |
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