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C3900 | A local minimum is a suboptimal equilibrium point at which system error is non-zero and the hidden output matrix is singular [12]. The complex problem which has a large number of patterns needs as many hidden nodes as patterns in order not to cause a singular hidden output matrix. | |
C3901 | Heisenberg's uncertainty principle is a key principle in quantum mechanics. Very roughly, it states that if we know everything about where a particle is located (the uncertainty of position is small), we know nothing about its momentum (the uncertainty of momentum is large), and vice versa. | |
C3902 | Lift can be found by dividing the confidence by the unconditional probability of the consequent, or by dividing the support by the probability of the antecedent times the probability of the consequent, so: The lift for Rule 1 is (3/4)/(4/7) = (3*7)/(4 * 4) = 21/16 ≈ 1.31. | |
C3903 | Experimental probability is the result of an experiment. Theoretical probability is what is expected to happen. Three students tossed a coin 50 times individually. | |
C3904 | In statistics, a sampling frame is the source material or device from which a sample is drawn. It is a list of all those within a population who can be sampled, and may include individuals, households or institutions. Importance of the sampling frame is stressed by Jessen and Salant and Dillman. | |
C3905 | By adjusting the rotation of the prism, separated lines of light with different colors could be observed with the telescope on the left. These lines were the spectrum of the substance. Kirchhoff and Bunsen found that elements such as lithium, sodium, and potassium all had their unique spectra. | |
C3906 | Some popular examples of unsupervised learning algorithms are:k-means for clustering problems.Apriori algorithm for association rule learning problems. | |
C3907 | In general, having high bias reduces the performance of the algorithm on training set while having high variance reduces performance on unseen data. This is known as Bias Variance Trade off. | |
C3908 | KMeans is a clustering algorithm which divides observations into k clusters. Since we can dictate the amount of clusters, it can be easily used in classification where we divide data into clusters which can be equal to or more than the number of classes. | |
C3909 | Weight is the parameter within a neural network that transforms input data within the network's hidden layers. A neural network is a series of nodes, or neurons. Within each node is a set of inputs, weight, and a bias value. Often the weights of a neural network are contained within the hidden layers of the network. | |
C3910 | A measure of central tendency is a single value that attempts to describe a set of data by identifying the central position within that set of data. The mean (often called the average) is most likely the measure of central tendency that you are most familiar with, but there are others, such as the median and the mode. | |
C3911 | Decision tree is a type of supervised learning algorithm that can be used in both regression and classification problems. It works for both categorical and continuous input and output variables. | |
C3912 | Global max pooling = ordinary max pooling layer with pool size equals to the size of the input (minus filter size + 1, to be precise). You can see that MaxPooling1D takes a pool_length argument, whereas GlobalMaxPooling1D does not. | |
C3913 | Most scientific calculators only calculate logarithms in base 10 and base e. A logarithm is a mathematical operation that determines how many times a certain number, called the base, is multiplied by itself to reach another number. | |
C3914 | The IOU is a number between 0 and 1, with larger being better. Ideally, the predicted box and the ground-truth have an IOU of 100% but in practice anything over 50% is usually considered to be a correct prediction. For the above example the IOU is 74.9% and you can see the boxes are a good match. | |
C3915 | RBMs were invented by Geoffrey Hinton and can be used for dimensionality reduction, classification, regression, collaborative filtering, feature learning, and topic modeling. RBMs are a special class of Boltzmann Machines and they are restricted in terms of the connections between the visible and the hidden units. | |
C3916 | Endogenous variables are used in econometrics and sometimes in linear regression. They are similar to (but not exactly the same as) dependent variables. Endogenous variables have values that are determined by other variables in the system (these “other” variables are called exogenous variables). | |
C3917 | One tool they can use to do so is a decision tree. Decision trees are flowchart graphs or diagrams that help explore all of the decision alternatives and their possible outcomes. Decision tree software helps businesses draw out their trees, assigns value and probabilities to each branch and analyzes each option. | |
C3918 | (retrogress) Opposite of to develop gradually. retrogress. diminish. regress. | |
C3919 | Right padding of string in Python Right padding a string means adding a given character at the right side of string to make it of a given length. | |
C3920 | Z Score is free of any scale, hence it is used as a transformation technique while we need to make any variable unit free in various statistical techniques. Also, it is used to identifying outliers in a univarite way. Z-test is a statistical technique to test the Null Hypothesis against the Alternate Hypothesis. | |
C3921 | If an infinite series converges, then the individual terms (of the underlying sequence being summed) must converge to 0. This can be phrased as a simple divergence test: If limn→∞an either does not exist, or exists but is nonzero, then the infinite series ∑nan diverges. | |
C3922 | Definition. Multi-label learning is an extension of the standard supervised learning setting. In contrast to standard supervised learning where one training example is associated with a single class label, in multi-label learning, one training example is associated with multiple class labels simultaneously. | |
C3923 | Contrapositive: The contrapositive of a conditional statement of the form "If p then q" is "If ~q then ~p". Symbolically, the contrapositive of p q is ~q ~p. | |
C3924 | A greater power requires a larger sample size. Effect size – This is the estimated difference between the groups that we observe in our sample. To detect a difference with a specified power, a smaller effect size will require a larger sample size. | |
C3925 | Model calibration is the process of adjustment of the model parameters and forcing within the margins of the uncertainties (in model parameters and / or model forcing) to obtain a model representation of the processes of interest that satisfies pre-agreed criteria (Goodness-of-Fit or Cost Function). | |
C3926 | Keyhole. Keyhole excels at four key things: Agorapulse. AgoraPulse is one of the greatest social media analytics tools that helps you identify your best content and see what users need. Brandwatch. Data is huge these days and BrandWatch is all about it. BrandMentions. Meltwater. Reputology. TapInfluence. Hootsuite.More items• | |
C3927 | In a box plot, we draw a box from the first quartile to the third quartile. A vertical line goes through the box at the median. The whiskers go from each quartile to the minimum or maximum. | |
C3928 | Another way of visualizing multivariate data for multiple attributes together is to use parallel coordinates. Basically, in this visualization as depicted above, points are represented as connected line segments. Each vertical line represents one data attribute. | |
C3929 | The total number of contravariant and covariant indices of a tensor. The rank of a tensor is independent of the number of dimensions. of the underlying space. | |
C3930 | For example, people may respond similarly to questions about income, education, and occupation, which are all associated with the latent variable socioeconomic status. In every factor analysis, there are the same number of factors as there are variables. | |
C3931 | The number of different treatment groups that we have in any factorial design can easily be determined by multiplying through the number notation. For instance, in our example we have 2 x 2 = 4 groups. In our notational example, we would need 3 x 4 = 12 groups. We can also depict a factorial design in design notation. | |
C3932 | When to use the sample or population standard deviation Therefore, if all you have is a sample, but you wish to make a statement about the population standard deviation from which the sample is drawn, you need to use the sample standard deviation. | |
C3933 | From our confusion matrix, we can calculate five different metrics measuring the validity of our model.Accuracy (all correct / all) = TP + TN / TP + TN + FP + FN.Misclassification (all incorrect / all) = FP + FN / TP + TN + FP + FN.Precision (true positives / predicted positives) = TP / TP + FP.More items | |
C3934 | The inductive bias (also known as learning bias) of a learning algorithm is the set of assumptions that the learner uses to predict outputs of given inputs that it has not encountered. In machine learning, one aims to construct algorithms that are able to learn to predict a certain target output. | |
C3935 | This list of requirements prioritization techniques provides an overview of common techniques that can be used in prioritizing requirements.Ranking. Numerical Assignment (Grouping) MoScoW Technique. Bubble Sort Technique. Hundred Dollar Method. Analytic Hierarchy Process (AHP) Five Whys. | |
C3936 | In linear regression, the function is a linear (straight-line) equation. In power or exponential regression, the function is a power (polynomial) equation of the form or an exponential function in the form . | |
C3937 | Perceptron for XOR: XOR is where if one is 1 and other is 0 but not both. A "single-layer" perceptron can't implement XOR. The reason is because the classes in XOR are not linearly separable. You cannot draw a straight line to separate the points (0,0),(1,1) from the points (0,1),(1,0). | |
C3938 | A type II error produces a false negative, also known as an error of omission. For example, a test for a disease may report a negative result, when the patient is, in fact, infected. This is a type II error because we accept the conclusion of the test as negative, even though it is incorrect. | |
C3939 | It is known as a top-down approach. Backward-chaining is based on modus ponens inference rule. In backward chaining, the goal is broken into sub-goal or sub-goals to prove the facts true. It is called a goal-driven approach, as a list of goals decides which rules are selected and used. | |
C3940 | Artificial Intelligence (AI) is a kind of simulation that involves a model intended to represent human intelligence or knowledge. An AI-based simulation model typically mimics human intelligence such as reasoning, learning, perception, planning, language comprehension, problem-solving, and decision making. | |
C3941 | Federated Learning enables mobile phones to collaboratively learn a shared prediction model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud. Your phone personalizes the model locally, based on your usage (A). | |
C3942 | The mean of a discrete random variable X is a weighted average of the possible values that the random variable can take. Unlike the sample mean of a group of observations, which gives each observation equal weight, the mean of a random variable weights each outcome xi according to its probability, pi. | |
C3943 | Gaussian RBF(Radial Basis Function) is another popular Kernel method used in SVM models for more. RBF kernel is a function whose value depends on the distance from the origin or from some point. | |
C3944 | A partially observable Markov decision process (POMDP) is a generalization of a Markov decision process (MDP). A POMDP models an agent decision process in which it is assumed that the system dynamics are determined by an MDP, but the agent cannot directly observe the underlying state. | |
C3945 | Least squares is an estimation technique that allows you to estimate the parameters of models. OLS (ordinary least squares) is the least squares technique used for estimating the parameters of linear regression models. The problem of linear regression is to fit a line to the data by minimizing the error. | |
C3946 | Direct link to this answer. Assuming he spectrogram function plots the power spectral density (PSD) in decibels. The values are relative, not negative, amplitudes, so -150 dB corresponds to an amplitude of about 3.2E-8. | |
C3947 | The term cognitive computing is typically used to describe AI systems that aim to simulate human thought. A number of AI technologies are required for a computer system to build cognitive models that mimic human thought processes, including machine learning, deep learning, neural networks, NLP and sentiment analysis. | |
C3948 | It turns out self-driving cars aren't dissimilar from self-driving humans: It takes about 16 years for them to be ready for the road. | |
C3949 | If exploding gradients are still occurring, you can check for and limit the size of gradients during the training of your network. This is called gradient clipping. Dealing with the exploding gradients has a simple but very effective solution: clipping gradients if their norm exceeds a given threshold. | |
C3950 | A Neural Network has got non linear activation layers which is what gives the Neural Network a non linear element. The function for relating the input and the output is decided by the neural network and the amount of training it gets. Similarly, a complex enough neural network can learn any function. | |
C3951 | Confidence Levelz0.951.960.962.050.982.330.992.586 more rows | |
C3952 | Activation functions are mathematical equations that determine the output of a neural network. The function is attached to each neuron in the network, and determines whether it should be activated (“fired”) or not, based on whether each neuron's input is relevant for the model's prediction. | |
C3953 | Gradient boosting refers to a class of ensemble machine learning algorithms that can be used for classification or regression predictive modeling problems. Gradient boosting is also known as gradient tree boosting, stochastic gradient boosting (an extension), and gradient boosting machines, or GBM for short. | |
C3954 | In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. The first expert systems were created in the 1970s and then proliferated in the 1980s. Expert systems were among the first truly successful forms of artificial intelligence (AI) software. | |
C3955 | In statistics, a sampling frame is the source material or device from which a sample is drawn. It is a list of all those within a population who can be sampled, and may include individuals, households or institutions. Importance of the sampling frame is stressed by Jessen and Salant and Dillman. | |
C3956 | In-group bias is notoriously difficult to avoid completely, but research shows it can be reduced through interaction with other groups, and by giving people an incentive to act in an unbiased manner. | |
C3957 | In statistics, linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression. Linear regression has many practical uses. | |
C3958 | There are three main steps to deploying on GCP:Upload your model to a Cloud Storage bucket.Create an AI Platform Prediction model resource.Create an AI Platform Prediction version resource, specifying the Cloud Storage path to your saved model. | |
C3959 | Kurtosis is a measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution. That is, data sets with high kurtosis tend to have heavy tails, or outliers. Data sets with low kurtosis tend to have light tails, or lack of outliers. | |
C3960 | You can tell if two random variables are independent by looking at their individual probabilities. If those probabilities don't change when the events meet, then those variables are independent. Another way of saying this is that if the two variables are correlated, then they are not independent. | |
C3961 | Updated: 04/26/2017 by Computer Hope. The degree of errors encountered during data transmission over a communications or network connection. The higher the error rate, the less reliable the connection or data transfer will be. The term error rate can refer to anything where errors can occur. | |
C3962 | To say it informally, the filter size is how many neighbor information you can see when processing the current layer. When the filter size is 3*3, that means each neuron can see its left, right, upper, down, upper left, upper right, lower left, lower right, as a total of 8 neighbor information. | |
C3963 | Lag sequential analysis is a method for analyzing the sequential dependency in a serially sequenced series of dichotomous codes representing different system states. The analysis assumes that the events are sequenced in time (a time series) but does not assume equal time intervals between events. | |
C3964 | In statistics, we usually say “random sample,” but in probability it's more common to say “IID.” Identically Distributed means that there are no overall trends–the distribution doesn't fluctuate and all items in the sample are taken from the same probability distribution. | |
C3965 | One of the newest and most effective ways to resolve the vanishing gradient problem is with residual neural networks, or ResNets (not to be confused with recurrent neural networks). ResNets refer to neural networks where skip connections or residual connections are part of the network architecture. | |
C3966 | The gamma distribution is the maximum entropy probability distribution (both with respect to a uniform base measure and with respect to a 1/x base measure) for a random variable X for which E[X] = kθ = α/β is fixed and greater than zero, and E[ln(X)] = ψ(k) + ln(θ) = ψ(α) − ln(β) is fixed (ψ is the digamma function). | |
C3967 | The survival function is a function that gives the probability that a patient, device, or other object of interest will survive beyond any specified time. The survival function is also known as the survivor function or reliability function. | |
C3968 | In simple linear regression a single independent variable is used to predict the value of a dependent variable. In multiple linear regression two or more independent variables are used to predict the value of a dependent variable. The difference between the two is the number of independent variables. | |
C3969 | P ∧ Q means P and Q. P ∨ Q means P or Q. An argument is valid if the following conditional holds: If all the premises are true, the conclusion must be true. So, when you attempt to write a valid argument, you should try to write out what the logical structure of the argument is by symbolizing it. | |
C3970 | Kernel function A kernel (or covariance function) describes the covariance of the Gaussian process random variables. Together with the mean function the kernel completely defines a Gaussian process. In the first post we introduced the concept of the kernel which defines a prior on the Gaussian process distribution. | |
C3971 | The chi-squared test applies an approximation assuming the sample is large, while the Fisher's exact test runs an exact procedure especially for small-sized samples. | |
C3972 | For symmetric and Hermitian matrices, the eigenvalues and singular values are obviously closely related. A nonnegative eigenvalue, λ ≥ 0, is also a singular value, σ = λ. The corresponding vectors are equal to each other, u = v = x. | |
C3973 | A conditional probability can always be computed using the formula in the definition. Sometimes it can be computed by discarding part of the sample space. Two events A and B are independent if the probability P(A∩B) of their intersection A∩B is equal to the product P(A)⋅P(B) of their individual probabilities. | |
C3974 | 1| Fast R-CNN Written in Python and C++ (Caffe), Fast Region-Based Convolutional Network method or Fast R-CNN is a training algorithm for object detection. This algorithm mainly fixes the disadvantages of R-CNN and SPPnet, while improving on their speed and accuracy. | |
C3975 | The reasoning is the mental process of deriving logical conclusion and making predictions from available knowledge, facts, and beliefs. In artificial intelligence, the reasoning is essential so that the machine can also think rationally as a human brain, and can perform like a human. | |
C3976 | Weaknesses. Histograms have many benefits, but there are two weaknesses. A histogram can present data that is misleading. For example, using too many blocks can make analysis difficult, while too few can leave out important data. | |
C3977 | Example: One nanogram of Plutonium-239 will have an average of 2.3 radioactive decays per second, and the number of decays will follow a Poisson distribution. | |
C3978 | Systematic sampling is a type of probability sampling method in which sample members from a larger population are selected according to a random starting point but with a fixed, periodic interval. This interval, called the sampling interval, is calculated by dividing the population size by the desired sample size. | |
C3979 | Simply put, an activation function is a function that is added into an artificial neural network in order to help the network learn complex patterns in the data. When comparing with a neuron-based model that is in our brains, the activation function is at the end deciding what is to be fired to the next neuron. | |
C3980 | Self Learning: Ability to recognize patterns, learn from data, and become more intelligent over time (can be AI or programmatically based). Machine Learning: AI systems with ability to automatically learn and improve from experience without being explicitly programmed via training. | |
C3981 | Stochastic gradient descent is, well, stochastic. Because you are no longer using your entire training set a once, and instead picking one or more examples at a time in some likely random fashion, each time you tun SGD you will obtain a different optimum and a unique cost vs. | |
C3982 | Two disjoint events can never be independent, except in the case that one of the events is null. Events are considered disjoint if they never occur at the same time. For example, being a freshman and being a sophomore would be considered disjoint events. | |
C3983 | Data preprocessing in Machine Learning refers to the technique of preparing (cleaning and organizing) the raw data to make it suitable for a building and training Machine Learning models. | |
C3984 | 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: | |
C3985 | (Note that how a support vector machine classifies points that fall on a boundary line is implementation dependent. In our discussions, we have said that points falling on the line will be considered negative examples, so the classification equation is w . u + b ≤ 0.) | |
C3986 | The range is the distance from the highest value to the lowest value. The Inter-Quartile Range is quite literally just the range of the quartiles: the distance from the largest quartile to the smallest quartile, which is IQR=Q3-Q1. | |
C3987 | Hyperparameter optimization in machine learning intends to find the hyperparameters of a given machine learning algorithm that deliver the best performance as measured on a validation set. Hyperparameters, in contrast to model parameters, are set by the machine learning engineer before training. | |
C3988 | Weighted accuracy is computed by taking the average, over all the classes, of the fraction of correct predictions in this class (i.e. the number of correctly predicted instances in that class, divided by the total number of instances in that class). | |
C3989 | According to Andrew Ng, the best methods of dealing with an underfitting model is trying a bigger neural network (adding new layers or increasing the number of neurons in existing layers) or training the model a little bit longer. | |
C3990 | Regression trees are used in Statistics, Data Mining and Machine learning. It is a very important and powerful technique when it comes to predictive analysis [5] . The goal is to predict the value of target variable on the basis of several input attributes that act as nodes of the regression tree. | |
C3991 | The gamma distribution can be used a range of disciplines including queuing models, climatology, and financial services. Examples of events that may be modeled by gamma distribution include: The amount of rainfall accumulated in a reservoir. The size of loan defaults or aggregate insurance claims. | |
C3992 | MNIST Data Formats The data is stored in a very simple file format designed for storing vectors and multidimensional matrices. The labels values are 0 to 9. Pixels are organized row-wise. 0 means background (white), 255 means foreground (black). | |
C3993 | Relationship between PDF and CDF for a Continuous Random VariableBy definition, the cdf is found by integrating the pdf: F(x)=x∫−∞f(t)dt.By the Fundamental Theorem of Calculus, the pdf can be found by differentiating the cdf: f(x)=ddx[F(x)] | |
C3994 | If all of the values in the sample are identical, the sample standard deviation will be zero. When discussing the sample mean, we found that the sample mean for diastolic blood pressure was 71.3. | |
C3995 | Image recognition is used to perform a large number of machine-based visual tasks, such as labeling the content of images with meta-tags, performing image content search and guiding autonomous robots, self-driving cars and accident avoidance systems. | |
C3996 | A knowledge-based system (KBS) is a computer program that reasons and uses a knowledge base to solve complex problems. Expert systems are designed to solve complex problems by reasoning about knowledge, represented primarily as if–then rules rather than through conventional procedural code. | |
C3997 | Graphically, the p value is the area in the tail of a probability distribution. It's calculated when you run hypothesis test and is the area to the right of the test statistic (if you're running a two-tailed test, it's the area to the left and to the right). | |
C3998 | Without replacement, each bootstrap sample would be identical to the original sample, so the sample statistics would all be the same and there would be no confidence "interval". | |
C3999 | Perceptron Learning Rule states that the algorithm would automatically learn the optimal weight coefficients. The input features are then multiplied with these weights to determine if a neuron fires or not. |
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