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C2000 | In many US airports, Customs and Border Protection now uses facial recognition to screen passengers on international flights. And in cities such as Baltimore, police have used facial recognition software to identify and arrest individuals at protests. | |
C2001 | So regression performance is measured by how close it fits an expected line/curve, while machine learning is measured by how good it can solve a certain problem, with whatever means necessary. I'll argue that the distinction between machine learning and statistical inference is clear. | |
C2002 | According to Bezdek (1994), Computational Intelligence is a subset of Artificial Intelligence. There are two types of machine intelligence: the artificial one based on hard computing techniques and the computational one based on soft computing methods, which enable adaptation to many situations. | |
C2003 | Now we'll check out the proven way to improve the accuracy of a model:Add 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. | |
C2004 | It is an open source artificial intelligence library, using data flow graphs to build models. It allows developers to create large-scale neural networks with many layers. TensorFlow is mainly used for: Classification, Perception, Understanding, Discovering, Prediction and Creation. | |
C2005 | Bias is calculated as the product of two components: non-response rate and the difference between the observed and non-respondent answers. Increasing either of the two components will lead to an increase in bias. | |
C2006 | Bayesian deep learning is a field at the intersection between deep learning and Bayesian probability theory. Bayesian deep learning models typically form uncertainty estimates by either placing distributions over model weights, or by learning a direct mapping to probabilistic outputs. | |
C2007 | In mathematics, the operator norm is a means to measure the "size" of certain linear operators. Formally, it is a norm defined on the space of bounded linear operators between two given normed vector spaces. | |
C2008 | It means having a strong sense of self-worth and self-belief. You can take immediate steps to project greater self-confidence in the way you behave, and how you approach other people. | |
C2009 | A statistical hypothesis is an explanation about the relationship between data populations that is interpreted probabilistically. A machine learning hypothesis is a candidate model that approximates a target function for mapping inputs to outputs. | |
C2010 | A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. Derived from feedforward neural networks, RNNs can use their internal state (memory) to process variable length sequences of inputs. | |
C2011 | In machine learning, the vanishing gradient problem is encountered when training artificial neural networks with gradient-based learning methods and backpropagation. The problem is that in some cases, the gradient will be vanishingly small, effectively preventing the weight from changing its value. | |
C2012 | The main challenge of NLP is the understanding and modeling of elements within a variable context. In a natural language, words are unique but can have different meanings depending on the context resulting in ambiguity on the lexical, syntactic, and semantic levels. | |
C2013 | Many everyday data sets typically follow a normal distribution: for example, the heights of adult humans, the scores on a test given to a large class, errors in measurements. The normal distribution is always symmetrical about the mean. | |
C2014 | Softmax is an activation function that outputs the probability for each class and these probabilities will sum up to one. Cross Entropy loss is just the sum of the negative logarithm of the probabilities. Therefore, Softmax loss is just these two appended together. | |
C2015 | A Poisson distribution assumes a ratio of 1 (i.e., the mean and variance are equal). Therefore, we can see that before we add in any explanatory variables there is a small amount of overdispersion. However, we need to check this assumption when all the independent variables have been added to the Poisson regression. | |
C2016 | Neural network regularization is a technique used to reduce the likelihood of model overfitting. There are several forms of regularization. The most common form is called L2 regularization. L2 regularization tries to reduce the possibility of overfitting by keeping the values of the weights and biases small. | |
C2017 | In addition every algorithm must satisfy the following criteria:input: there are zero or more quantities which are externally supplied;output: at least one quantity is produced;definiteness: each instruction must be clear and unambiguous;More items | |
C2018 | The normal distribution is used when the population distribution of data is assumed normal. A sample of the population is used to estimate the mean and standard deviation. The t statistic is an estimate of the standard error of the mean of the population or how well known is the mean based on the sample size. | |
C2019 | To understand potential interaction effects, compare the lines from the interaction plot:If the lines are parallel, there is no interaction.If the lines are not parallel, there is an interaction. | |
C2020 | Downsides of Multivariate Testing The most difficult challenge in executing multivariate tests is the amount of visitor traffic required to reach meaningful results. Because of the fully factorial nature of these tests, the number of variations in a test can add up quickly. | |
C2021 | In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer. | |
C2022 | MLP usually means many layers and can be supervised with labels. RBM (Restricted Boltzmann Machine) consists of only 2 layers: input layer & hidden layer, and it is un-supervised (no labels). RBM (Restricted Boltzmann Machine) consists of only 2 layers: input layer & hidden layer, and it is un-supervised (no labels). | |
C2023 | Definition. A Binned Variable (also Grouped Variable) in the context of Quantitative Risk Management is any variable that is generated via the discretization of Numerical Variable into a defined set of bins (intervals). | |
C2024 | The Kaplan-Meier estimate is the simplest way of computing the survival over time in spite of all these difficulties associated with subjects or situations. For each time interval, survival probability is calculated as the number of subjects surviving divided by the number of patients at risk. | |
C2025 | 16 Best Resources to Learn AI & Machine Learning in 2019Introduction to Machine Learning Problem Framing from Google. Artificial Intelligence: Principles and Techniques from Stanford University. Daily email list of AI and ML coding tasks from GeekForge. CS405: Artificial Intelligence from Saylor Academy. Intro to Artificial Intelligence at Udacity.More items• | |
C2026 | Also, the rule-based analysis permits an individual's risk to be predicted on the basis of only one, or at most a few, risk factors, whereas scores derived from regression models require that all covariates be available. | |
C2027 | Photo by Gareth Thompson, some rights reserved.Allocate More Memory. Work with a Smaller Sample. Use a Computer with More Memory. Change the Data Format. Stream Data or Use Progressive Loading. Use a Relational Database. Use a Big Data Platform. Summary. | |
C2028 | Pattern recognition is the process of recognizing patterns by using machine learning algorithm. Pattern recognition can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns and/or their representation. | |
C2029 | This two-step approach actually combines two different anomaly detection techniques: univariate and multivariate. Univariate anomaly detection looks for anomalies in each individual metric, while multivariate anomaly detection learns a single model for all the metrics in the system. | |
C2030 | 1:1111:18Suggested clip · 91 secondsLimits of Functions of Two Variables - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C2031 | Optimization lies at the heart of machine learning. Then the model is typically trained by solving a core optimization problem that optimizes the variables or parameters of the model with respect to the selected loss function and possibly some regularization function. | |
C2032 | Entropy can be calculated for a random variable X with k in K discrete states as follows: H(X) = -sum(each k in K p(k) * log(p(k))) | |
C2033 | A proposition of the form “if p then q” or “p implies q”, represented “p → q” is called a conditional proposition. The proposition p is called hypothesis or antecedent, and the proposition q is the conclusion or consequent. Note that p → q is true always except when p is true and q is false. | |
C2034 | Variance: Var(X) To calculate the Variance: square each value and multiply by its probability. sum them up and we get Σx2p. then subtract the square of the Expected Value μ | |
C2035 | When we think about the English word “Attention”, we know that it means directing your focus at something and taking greater notice. The Attention mechanism in Deep Learning is based off this concept of directing your focus, and it pays greater attention to certain factors when processing the data. | |
C2036 | Response bias can be defined as the difference between the true values of variables in a study's net sample group and the values of variables obtained in the results of the same study. Nonresponse bias occurs when some respondents included in the sample do not respond. | |
C2037 | No. A universal Turing machine is a Turing machine that takes as its input a string of the form where is the representation of the transition table of Turing machine and is a string over the input alphabet of . | |
C2038 | Overall, Sentiment analysis may involve the following types of classification algorithms: Linear Regression. Naive Bayes. Support Vector Machines. | |
C2039 | This significantly reduces bias as we are using most of the data for fitting, and also significantly reduces variance as most of the data is also being used in validation set. Interchanging the training and test sets also adds to the effectiveness of this method. | |
C2040 | An endogenous variable is a variable in a statistical model that's changed or determined by its relationship with other variables within the model. Endogenous variables are the opposite of exogenous variables, which are independent variables or outside forces. | |
C2041 | In its simplest form, the sigmoid is a representation of time (on the horizontal axis) and activity (on the vertical axis). The wonder of this curve is that it really describes most phenomena, regardless of type. The phenomenon experiences sharp growth. It hits a maturity phase where growth slows, and then stops. | |
C2042 | Probability density function (PDF) is a statistical expression that defines a probability distribution (the likelihood of an outcome) for a discrete random variable (e.g., a stock or ETF) as opposed to a continuous random variable. | |
C2043 | 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. | |
C2044 | Random oversampling involves randomly selecting examples from the minority class, with replacement, and adding them to the training dataset. Random undersampling involves randomly selecting examples from the majority class and deleting them from the training dataset. | |
C2045 | Markov models are often used to model the probabilities of different states and the rates of transitions among them. The method is generally used to model systems. Markov models can also be used to recognize patterns, make predictions and to learn the statistics of sequential data. | |
C2046 | During the experiment, they found that one of the useful way to do text augmentation is replacing words or phrases with their synonyms . Leverage existing thesaurus help to generate lots of data in a short time. Zhang et al. select a word and replace it by synonyms according to geometric distribution. | |
C2047 | Correlation is a statistical measure that expresses the extent to which two variables are linearly related (meaning they change together at a constant rate). It's a common tool for describing simple relationships without making a statement about cause and effect. | |
C2048 | EM is an iterative method which alternates between two steps, expectation (E) and maximization (M). For clustering, EM makes use of the finite Gaussian mixtures model and estimates a set of parameters iteratively until a desired convergence value is achieved. | |
C2049 | A confidence level refers to the percentage of all possible samples that can be expected to include the true population parameter. For example, suppose all possible samples were selected from the same population, and a confidence interval were computed for each sample. | |
C2050 | In machine learning, multiclass or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). | |
C2051 | simple random sample | |
C2052 | As the name implies, multivariate regression is a technique that estimates a single regression model with more than one outcome variable. When there is more than one predictor variable in a multivariate regression model, the model is a multivariate multiple regression. | |
C2053 | The prerequisites for really understanding deep learning are linear algebra, calculus and statistics, as well as programming and some machine learning. The prerequisites for applying it are just learning how to deploy a model. | |
C2054 | To solve the problem using logistic regression we take two parameters w, which is n dimensional vector and b which is a real number. The logistic regression model to solve this is : Equation for Logistic Regression. We apply sigmoid function so that we contain the result of ŷ between 0 and 1 (probability value). | |
C2055 | Logistic regression measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function, which is the cumulative distribution function of logistic distribution. | |
C2056 | Cluster-Based Similarity Partitioning Algorithm For each input partition, an N × N binary similarity matrix encodes the piecewise similarity between any two objects, that is, the similarity of one indicates that two objects are grouped into the same cluster and a similarity of zero otherwise. | |
C2057 | Given an image or a video stream, an object detection model can identify which of a known set of objects might be present and provide information about their positions within the image. | |
C2058 | Binomial counts successes in a fixed number of trials, while Negative binomial counts failures until a fixed number successes. The Bernoulli and Geometric distributions are the simplest cases of the Binomial and Negative Binomial distributions. | |
C2059 | In short, security is contested because of the politically mobilising and powerful connotations associated with the term (Booth 1991: 318; Buzan 1983: 2; McDonald 2012: 24). In contrast to realism, theoretical approaches like the Welsh School tradition understand security differently. | |
C2060 | Experience replay enables reinforcement learning agents to memorize and reuse past experiences, just as humans replay memories for the situation at hand. Contemporary off-policy algorithms either replay past experiences uniformly or utilize a rule- based replay strategy, which may be sub-optimal. | |
C2061 | Time series forecasting is an important area of machine learning that is often neglected. It is important because there are so many prediction problems that involve a time component. Standard definitions of time series, time series analysis, and time series forecasting. | |
C2062 | The term that does not apply to cluster analysis is factorization. Cluster analysis is a way of grouping data, based on obvious similarities. It is also called as classification analysis or numerical taxonomy. Hierarchical cluster analysis tends to build a hierarchy within clusters. | |
C2063 | We can define a neural network that can learn to recognize objects in less than 100 lines of code. In analogy, we conjecture that rules for development and learning in brains may be far easier to understand than their resulting properties. | |
C2064 | The difference between nonprobability and probability sampling is that nonprobability sampling does not involve random selection and probability sampling does. At least with a probabilistic sample, we know the odds or probability that we have represented the population well. | |
C2065 | normal distribution | |
C2066 | Decision Tree can be used both in classification and regression problem. This article present the Decision Tree Regression Algorithm along with some advanced topics. | |
C2067 | To conclude, it can be said that residual networks have become quite popular for image recognition and classification tasks because of their ability to solve vanishing and exploding gradients when adding more layers to an already deep neural network. A ResNet with thousand layers has not much practical use as of now. | |
C2068 | In statistics, stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic procedure. In each step, a variable is considered for addition to or subtraction from the set of explanatory variables based on some prespecified criterion. | |
C2069 | For example, a p-value of 0.01 would mean there is a 1% chance of committing a Type I error. However, using a lower value for alpha means that you will be less likely to detect a true difference if one really exists (thus risking a type II error). | |
C2070 | A tensor is a vector or matrix of n-dimensions that represents all types of data. All values in a tensor hold identical data type with a known (or partially known) shape. The shape of the data is the dimensionality of the matrix or array. | |
C2071 | What problems is humanity facing currently & can AI help to solve them?Energy.Environment.Transporation.Food and water.Disease and Human Suffering.Education.Population. | |
C2072 | The geometric distribution describes the probability of "x trials are made before a success", and the negative binomial distribution describes that of "x trials are made before r successes are obtained", where r is fixed. So you see that the latter is a particular case of the former, namely, when r=1. | |
C2073 | In statistics, the phrase "correlation does not imply causation" refers to the inability to legitimately deduce a cause-and-effect relationship between two variables solely on the basis of an observed association or correlation between them. | |
C2074 | Linear discriminant analysis (LDA) is used here to reduce the number of features to a more manageable number before the process of classification. Each of the new dimensions generated is a linear combination of pixel values, which form a template. | |
C2075 | Text mining (also referred to as text analytics) is an artificial intelligence (AI) technology that uses natural language processing (NLP) to transform the free (unstructured) text in documents and databases into normalized, structured data suitable for analysis or to drive machine learning (ML) algorithms. | |
C2076 | Weights(Parameters) — A weight represent the strength of the connection between units. If the weight from node 1 to node 2 has greater magnitude, it means that neuron 1 has greater influence over neuron 2. A weight brings down the importance of the input value. | |
C2077 | While the chi-squared test relies on an approximation, Fisher's exact test is one of exact tests. Especially when more than 20% of cells have expected frequencies < 5, we need to use Fisher's exact test because applying approximation method is inadequate. | |
C2078 | Examples of ordinal variables include: socio economic status (“low income”,”middle income”,”high income”), education level (“high school”,”BS”,”MS”,”PhD”), income level (“less than 50K”, “50K-100K”, “over 100K”), satisfaction rating (“extremely dislike”, “dislike”, “neutral”, “like”, “extremely like”). | |
C2079 | Benchmarking Sentiment Analysis Algorithms (Algorithmia) – “Sentiment Analysis, also known as opinion mining, is a powerful tool you can use to build smarter products. It's a natural language processing algorithm that gives you a general idea about the positive, neutral, and negative sentiment of texts. | |
C2080 | A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0). | |
C2081 | Stratified sampling offers several advantages over simple random sampling. A stratified sample can provide greater precision than a simple random sample of the same size. Because it provides greater precision, a stratified sample often requires a smaller sample, which saves money. | |
C2082 | Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image. | |
C2083 | There are two types of probability distribution which are used for different purposes and various types of the data generation process.Normal or Cumulative Probability Distribution.Binomial or Discrete Probability Distribution. | |
C2084 | In computer science, an inverted index (also referred to as a postings file or inverted file) is a database index storing a mapping from content, such as words or numbers, to its locations in a table, or in a document or a set of documents (named in contrast to a forward index, which maps from documents to content). | |
C2085 | Probability and the Normal Curve The normal distribution is a continuous probability distribution. The total area under the normal curve is equal to 1. The probability that a normal random variable X equals any particular value is 0. | |
C2086 | Batch normalization is a technique for training very deep neural networks that standardizes the inputs to a layer for each mini-batch. This has the effect of stabilizing the learning process and dramatically reducing the number of training epochs required to train deep networks. | |
C2087 | Univariate statistics summarize only one variable at a time. Bivariate statistics compare two variables. Multivariate statistics compare more than two variables. | |
C2088 | There are two main types of criterion validity: concurrent validity and predictive validity. Concurrent validity is determined by comparing tests scores of current employees to a measure of their job performance. | |
C2089 | Text segmentation is the process of dividing written text into meaningful units, such as words, sentences, or topics. The term applies both to mental processes used by humans when reading text, and to artificial processes implemented in computers, which are the subject of natural language processing. | |
C2090 | 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. | |
C2091 | The median is a measure of center (location) of a list of numbers. This will be the median. If there are an even number on the list then average the n/2 and the (N + 2)/2 numbers. In general, the median is at position (n + 1)/2. If this position is a whole number then you have the median at that position in the list. | |
C2092 | For most common hierarchical clustering software, the default distance measure is the Euclidean distance. This is the square root of the sum of the square differences. However, for gene expression, correlation distance is often used. The distance between two vectors is 0 when they are perfectly correlated. | |
C2093 | The mean is the average of the numbers. It is easy to calculate: add up all the numbers, then divide by how many numbers there are. In other words it is the sum divided by the count. | |
C2094 | Ground truth is a term used in statistics and machine learning that means checking the results of machine learning for accuracy against the real world. The term is borrowed from meteorology, where "ground truth" refers to information obtained on site. | |
C2095 | Minimax is a kind of backtracking algorithm that is used in decision making and game theory to find the optimal move for a player, assuming that your opponent also plays optimally. In Minimax the two players are called maximizer and minimizer. | |
C2096 | Adaptive Gradient Algorithm (Adagrad) is an algorithm for gradient-based optimization. It performs smaller updates As a result, it is well-suited when dealing with sparse data (NLP or image recognition) Each parameter has its own learning rate that improves performance on problems with sparse gradients. | |
C2097 | A multinomial experiment is almost identical with one main difference: a binomial experiment can have two outcomes, while a multinomial experiment can have multiple outcomes. A binomial experiment will have a binomial distribution. | |
C2098 | Natural language processing helps computers communicate with humans in their own language and scales other language-related tasks. For example, NLP makes it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important. | |
C2099 | Stochastic Gradient Descent (SGD) is a simple yet very efficient approach to fitting linear classifiers and regressors under convex loss functions such as (linear) Support Vector Machines and Logistic Regression. The advantages of Stochastic Gradient Descent are: Efficiency. |
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