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C700 | 4:2514:33Suggested clip · 73 secondsDiscrete Uniform Distribution: Introduction, Mean and Variance YouTubeStart of suggested clipEnd of suggested clip | |
C701 | 0:004:30Suggested clip · 84 secondsExponential distribution moment generating function - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C702 | Law of large numbers, in statistics, the theorem that, as the number of identically distributed, randomly generated variables increases, their sample mean (average) approaches their theoretical mean. The law of large numbers was first proved by the Swiss mathematician Jakob Bernoulli in 1713. | |
C703 | Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model. | |
C704 | Data for two variables (usually two types of related data). Example: Ice cream sales versus the temperature on that day. The two variables are Ice Cream Sales and Temperature. | |
C705 | Because deeper networks capture the natural “hierarchy” that is present everywhere in nature. See a convnet for example, it captures low level features in first layer, a little better but still low level features in the next layer and at higher layers object parts and simple structures are captured. | |
C706 | Emotions can also be detected through body postures. Research has shown that body postures are more accurately recognised when an emotion is compared with a different or neutral emotion. For example, a person feeling angry would portray dominance over the other, and their posture would display approach tendencies. | |
C707 | ARIMA models allow both autoregressive (AR) components as well as moving average (MA) components. The (I) in ARIMA determines the level of differencing to use, which helps make the data stationary. ARIMA models are more flexible than other statistical models such as exponential smoothing or simple linear regression. | |
C708 | Chi-squared test | |
C709 | If a population is known to be normally distributed, then it follows that the sample mean must equal the population mean. If the sampled population distribution is skewed, then in most cases the sampling distribution of the mean can be approximated by the normal distribution if the sample size n is at least 30. | |
C710 | The Wilcoxon rank-sum test is commonly used for the comparison of two groups of nonparametric (interval or not normally distributed) data, such as those which are not measured exactly but rather as falling within certain limits (e.g., how many animals died during each hour of an acute study). | |
C711 | A knowledge representation is an encoding of this information or understanding in a particular substrate, such as a set of if-then rules, a semantic network, conditional probability tables, a Venn diagram, a mind map, or the axioms of formal logic. | |
C712 | The random effects assumption is that the individual-specific effects are uncorrelated with the independent variables. The fixed effect assumption is that the individual-specific effects are correlated with the independent variables. | |
C713 | Spectral analysis or Spectrum analysis is analysis in terms of a spectrum of frequencies or related quantities such as energies, eigenvalues, etc. In specific areas it may refer to: Spectroscopy in chemistry and physics, a method of analyzing the properties of matter from their electromagnetic interactions. | |
C714 | for a time series is one in which there is no trend or seasonal component and in which the observations are simply independent and identically distributed (iid) random variables with zero mean. We refer to such a sequence of random variables X1,X2, as iid noise. | |
C715 | = P(A)^P(B) which is just the probability of A times the probability of B. If they are dependent, then P(A and B) = P(A)*P(B|A) which is the probability of A times the probability of "B happening if A has occurred," which is different than the "Probability of B if A has not occurred." | |
C716 | A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. A t-test is used as a hypothesis testing tool, which allows testing of an assumption applicable to a population. | |
C717 | The inversion method relies on the principle that continuous cumulative distribution functions (cdfs) range uniformly over the open interval (0,1). If u is a uniform random number on (0,1), then x = F - 1 ( u ) generates a random number x from any continuous distribution with the specified cdf F . | |
C718 | The unit of measurement usually given when talking about statistical significance is the standard deviation, expressed with the lowercase Greek letter sigma (σ). The term refers to the amount of variability in a given set of data: whether the data points are all clustered together, or very spread out. | |
C719 | Transfer learning is a machine learning technique where a model trained on one task is re-purposed on a second related task. Transfer learning only works in deep learning if the model features learned from the first task are general. | |
C720 | Just as correlation measures the extent of a linear relationship between two variables, autocorrelation measures the linear relationship between lagged values of a time series. There are several autocorrelation coefficients, corresponding to each panel in the lag plot. | |
C721 | linear threshold unit (LTU) A linear threshold unit is a simple artificial neuron whose output is its thresholded total net input. That is, an LTU with threshold T calculates the weighted sum of its inputs, and then outputs 0 if this sum is less than T, and 1 if the sum is greater than T. | |
C722 | Labeled data is data that comes with a tag, like a name, a type, or a number. Unlabeled data is data that comes with no tag. The set of algorithms in which we use a labeled dataset is called supervised learning. The set of algorithms in which we use an unlabeled dataset, is called unsupervised learning. | |
C723 | The basic steps to build a stochastic model are:Create the sample space (Ω) — a list of all possible outcomes,Assign probabilities to sample space elements,Identify the events of interest,Calculate the probabilities for the events of interest. | |
C724 | Usually you don't want to find a global optimum. Because that usually requires overfitting the training data. An interesting alternative to gradient descent is the population-based training algorithms such as the evolutionary algorithms (EA) and the particle swarm optimisation (PSO). | |
C725 | Gradient descent is best used when the parameters cannot be calculated analytically (e.g. using linear algebra) and must be searched for by an optimization algorithm. | |
C726 | 1:085:00Suggested clip · 93 secondsInterpreting Hazard Ratios - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C727 | The main difference between the two, is that a Perceptron takes that binary response (like a classification result) and computes an error used to update the weights, whereas an Adaline uses a continous response value to update the weights (so before the binarized output is produced). | |
C728 | In the AI-enabled future, humans will be able to converse and interact with each other in the native language of choice, not having to worry about miscommunicating intentions. Machine learning models will be able to understand context, nuance, and colloquialisms that help to fill the gaps of human communication. | |
C729 | In short, it ensures each subgroup within the population receives proper representation within the sample. As a result, stratified random sampling provides better coverage of the population since the researchers have control over the subgroups to ensure all of them are represented in the sampling. | |
C730 | To find the relative frequency, divide the frequency by the total number of data values. To find the cumulative relative frequency, add all of the previous relative frequencies to the relative frequency for the current row. | |
C731 | Ensemble modeling is a process where multiple diverse models are created to predict an outcome, either by using many different modeling algorithms or using different training data sets. The ensemble model then aggregates the prediction of each base model and results in once final prediction for the unseen data. | |
C732 | A Classification tree labels, records, and assigns variables to discrete classes. A Classification tree is built through a process known as binary recursive partitioning. This is an iterative process of splitting the data into partitions, and then splitting it up further on each of the branches. | |
C733 | Covariance: An Overview. Variance and covariance are mathematical terms frequently used in statistics and probability theory. Variance refers to the spread of a data set around its mean value, while a covariance refers to the measure of the directional relationship between two random variables. | |
C734 | Definition 1. Suppose that events A and B are defined on the same probability space, and the event B is such that P(B) > 0. The conditional probability of A given that B has occurred is given by P(A|B) = P(A ∩ B)/P(B). | |
C735 | Initializers define the way to set the initial random weights of Keras layers. The keyword arguments used for passing initializers to layers depends on the layer. Usually, it is simply kernel_initializer and bias_initializer : from tensorflow.keras import layers from tensorflow.keras import initializers layer = layers. | |
C736 | In March 2014, just two months after DeepMind was acquired, Musk warned that AI is "potentially more dangerous than nukes," suggesting that his investment might have been made because he was concerned about where the technology was headed. | |
C737 | In the context of CNN, a filter is a set of learnable weights which are learned using the backpropagation algorithm. You can think of each filter as storing a single template/pattern. Filter is referred to as a set of shared weights on the input. | |
C738 | Scikit-learn is a Python library used for machine learning. The framework is built on top of several popular Python packages, namely NumPy, SciPy, and matplotlib. A major benefit of this library is the BSD license it's distributed under. | |
C739 | Statistical modeling is the process of applying statistical analysis to a dataset. A statistical model is a mathematical representation (or mathematical model) of observed data. “When you analyze data, you are looking for patterns,” says Mello. “You are using a sample to make an inference about the whole.” | |
C740 | A chi-square test is used when you want to see if there is a relationship between two categorical variables. In SPSS, the chisq option is used on the statistics subcommand of the crosstabs command to obtain the test statistic and its associated p-value. | |
C741 | Feature detection is a process by which the nervous system sorts or filters complex natural stimuli in order to extract behaviorally relevant cues that have a high probability of being associated with important objects or organisms in their environment, as opposed to irrelevant background or noise. | |
C742 | The recommended reference range of serum TNF-α was from nondetectable to 8.1 pg/mL. Among 147 patients with IgAN, 98 patients were with elevated serum TNF-α and 49 patients were without elevated serum TNF-α. | |
C743 | Gradient boosting is a greedy algorithm and can overfit a training dataset quickly. It can benefit from regularization methods that penalize various parts of the algorithm and generally improve the performance of the algorithm by reducing overfitting. | |
C744 | A linear regression equation simply sums the terms. While the model must be linear in the parameters, you can raise an independent variable by an exponent to fit a curve. For instance, you can include a squared or cubed term. Nonlinear regression models are anything that doesn't follow this one form. | |
C745 | Big Data Meets Machine Learning By feeding big data to a machine-learning algorithm, we might expect to see defined and analyzed results, like hidden patterns and analytics, that can assist in predictive modeling. For some companies, these algorithms might automate processes that were previously human-centered. | |
C746 | For a hypothesis test, a researcher collects sample data. If the statistic falls within a specified range of values, the researcher rejects the null hypothesis . The range of values that leads the researcher to reject the null hypothesis is called the region of rejection. | |
C747 | A little bit of coding skills is enough, but it's better to have knowledge of data structures, algorithms, and OOPs concept. Some of the popular programming languages to learn machine learning in are Python, R, Java, and C++. | |
C748 | A facial recognition system uses biometrics to map facial features from a photograph or video. It compares the information with a database of known faces to find a match. That's because facial recognition has all kinds of commercial applications. It can be used for everything from surveillance to marketing. | |
C749 | Exogenous causes are factors that influence the business cycle from outside of the system, e.g. climate (drought and other natural disasters) and the political situation of a country. Endogenous causes are factors that influence the business cycle from inside the system, e.g. total expenditure. | |
C750 | There are several ways to check your Linear Regression model accuracy. Usually, you may use Root mean squared error. You may train several Linear Regression models, adding or removing features to your dataset, and see which one has the lowest RMSE - the best one in your case. | |
C751 | Examples of Disjoint Events A football game can't be held at the same time as a rugby game on the same field. Heading East and West at the same time is impossible. Tossing a coin and getting a heads and a tails at the same time is impossible. You can't take the bus and the car to work at the same time. | |
C752 | The least squares method provides the overall rationale for the placement of the line of best fit among the data points being studied. An analyst using the least squares method will generate a line of best fit that explains the potential relationship between independent and dependent variables. | |
C753 | Stemming is the process of reducing a word to its word stem that affixes to suffixes and prefixes or to the roots of words known as a lemma. Stemming is important in natural language understanding (NLU) and natural language processing (NLP). Stemming is also a part of queries and Internet search engines. | |
C754 | The latent space is simply a representation of compressed data in which similar data points are closer together in space. Latent space is useful for learning data features and for finding simpler representations of data for analysis. | |
C755 | This learning process is independent. During the training of ANN under unsupervised learning, the input vectors of similar type are combined to form clusters. When a new input pattern is applied, then the neural network gives an output response indicating the class to which input pattern belongs. | |
C756 | The symbol epsilon in mathematics is often used as an “infinitesimal” quantity since you can definite it to be as arbitrarily close to zero as you want, and it is in this generality that the epsilon-neighborhood definition of a limit furnishes us with the properties of a limit that we desire. | |
C757 | Photo Credit: Pixabay. 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. | |
C758 | It's true that the unit step function is bounded. However, a system which has the unit step function as its impulse response is not stable, because the integral (of the absolute value) is infinite. Bounded and stable are not the same thing. | |
C759 | Word Embedding is really all about improving the ability of networks to learn from text data. By representing that data as lower dimensional vectors. This technique is used to reduce the dimensionality of text data but these models can also learn some interesting traits about words in a vocabulary. | |
C760 | Accuracy can be computed by comparing actual test set values and predicted values. Well, you got a classification rate of 96.49%, considered as very good accuracy. For further evaluation, you can also check precision and recall of model. | |
C761 | A turing machine is a theoretical machine that computes a function. It is theoretical because it has unlimited tape and time. A human brain is a limited object in space and time, hence we can ignore the unlimited tape requirement. | |
C762 | Target Concept Term used in the machine learning literature to denote the Bayes decision rule, or the regression function, depending on the context. The target concept is a member of the concept space. Synonyms: Bayes Decision Rule in classification, Regression Function in regression. | |
C763 | The simplest way to compare two distributions is via the Z-test. The error in the mean is calculated by dividing the dispersion by the square root of the number of data points. This is one way you can use to determine, in fact, the likelihood that your sample means it a good indicator of the true population mean. | |
C764 | Multivariate Regression is a method used to measure the degree at which more than one independent variable (predictors) and more than one dependent variable (responses), are linearly related. A mathematical model, based on multivariate regression analysis will address this and other more complicated questions. | |
C765 | Summary: Goodness of Fit: used to compare a single sample proportion against a publicized model. Homogeneity: used to examine whether things have changed or stayed the same or whether the proportions that exist between two populations are the same, or when comparing data from MULTIPLE samples. | |
C766 | 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. | |
C767 | Computing accuracy for clustering can be done by reordering the rows (or columns) of the confusion matrix so that the sum of the diagonal values is maximal. The linear assignment problem can be solved in O(n3) instead of O(n!). Coclust library provides an implementation of the accuracy for clustering results. | |
C768 | The regions of the brain comprising the “reward system” use the neurotransmitter dopamine to communicate. Neurons that release dopamine are activated when we expect to receive a reward. Dopamine also enhances reward-related memories. | |
C769 | A histogram is drawn like a bar chart, but often has bars of unequal width. It is the area of the bar that tells us the frequency in a histogram, not its height. Instead of plotting frequency on the y-axis, we plot the frequency density. To calculate this, you divide the frequency of a group by the width of it. | |
C770 | 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. | |
C771 | First step is to split predicted probability into 10 parts (decile) and then compute the cumulative % of events and non-events in each decile and check the decile where difference is maximum (as shown in the image below.) In the image below, KS is 57.8% and it is at third decile. KS curve is shown below. | |
C772 | Neural network activation functions are a crucial component of deep learning. Activation functions determine the output of a deep learning model, its accuracy, and also the computational efficiency of training a model—which can make or break a large scale neural network. | |
C773 | So instead of updating the weight by taking in the output of a neuron in the previous layer, multiplying it by the learning rate and delta value, then subtracting that final value from the current weight, it will multiply the delta value and learning rate by 1, then subtract that final value from the bias weight in | |
C774 | How to use them while designing a CNN: Conv2D filters are used only in the initial layers of a Convolutional Neural Network. They are put there to extract the initial high level features from an image. | |
C775 | The value function represent how good is a state for an agent to be in. It is equal to expected total reward for an agent starting from state s . The value function depends on the policy by which the agent picks actions to perform. | |
C776 | Bayesian decision making is the process in which a decision is made based on the probability of a successful outcome, where this probability is informed by both prior information and new evidence that the decision maker obtains. | |
C777 | There are three big-picture methods to understand if a continuous and categorical are significantly correlated — point biserial correlation, logistic regression, and Kruskal Wallis H Test. The point biserial correlation coefficient is a special case of Pearson's correlation coefficient. | |
C778 | A one-tailed test is a statistical test in which the critical area of a distribution is one-sided so that it is either greater than or less than a certain value, but not both. If the sample being tested falls into the one-sided critical area, the alternative hypothesis will be accepted instead of the null hypothesis. | |
C779 | Markov chains are used in a broad variety of academic fields, ranging from biology to economics. When predicting the value of an asset, Markov chains can be used to model the randomness. The price is set by a random factor which can be determined by a Markov chain. | |
C780 | Multiple regression is an extension of simple linear regression. It is used when we want to predict the value of a variable based on the value of two or more other variables. The variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable). | |
C781 | In probability theory and statistics, a categorical distribution (also called a generalized Bernoulli distribution, multinoulli distribution) is a discrete probability distribution that describes the possible results of a random variable that can take on one of K possible categories, with the probability of each | |
C782 | The difference between the two norms is that the standard deviation is calculating the square of the difference whereas the mean absolute deviation is only looking at the absolute difference. Hence large outliers will create a higher dispersion when using the standard deviation instead of the other method. | |
C783 | Noun. optimizer (plural optimizers) A person in a large business whose task is to maximize profits and make the business more efficient. (computing) A program that uses linear programming to optimize a process. (computing) A compiler or assembler that produces optimized code. | |
C784 | In regression analysis, those factors are called variables. You have your dependent variable — the main factor that you're trying to understand or predict. In Redman's example above, the dependent variable is monthly sales. | |
C785 | Bandish Bandits tells the story of the love between Tamanna, a popstar, and Radhe, a classical music prodigy in Jodhpur. Radhe's grandfather, Pandit Radhemohan Rathod, is a celebrated singer, a strict disciplinarian, and believes the purity of music should not be tainted by filthy lucre or light music. | |
C786 | If k is given, the K-means algorithm can be executed in the following steps: Partition of objects into k non-empty subsets. Identifying the cluster centroids (mean point) of the current partition. Compute the distances from each point and allot points to the cluster where the distance from the centroid is minimum. | |
C787 | In order to label some more of the data my idea is to do the following:Build a classifier on the whole data set separating the class 'A from the unlabelled data.Run the classifier on the unlabelled data.Add the unlabelled items classified as being in class 'A' to class 'A'.Repeat. | |
C788 | As a human can easily recognize the image by seeing its color, shape, texture or some other feature, the same way machine first extracts the features of the object and then it applies the classification algorithm to label a particular class of the recognized object according to the extracted features. | |
C789 | Detection theory or signal detection theory is a means to measure the ability to differentiate between information-bearing patterns (called stimulus in living organisms, signal in machines) and random patterns that distract from the information (called noise, consisting of background stimuli and random activity of the | |
C790 | "Controlling" for a variable means adding it to the model so its effect on your outcome variable(s) can be estimated and statistically isolated from the effect of the independent variable you're really interested in. | |
C791 | Geometrically, an eigenvector, corresponding to a real nonzero eigenvalue, points in a direction in which it is stretched by the transformation and the eigenvalue is the factor by which it is stretched. | |
C792 | A regression model will have unit changes between the x and y variables, where a single unit change in x will coincide with a constant change in y. Taking the log of one or both variables will effectively change the case from a unit change to a percent change. A logarithm is the base of a positive number. | |
C793 | The Pearson product-moment correlation coefficient, also known as r, R, or Pearson's r, is a measure of the strength and direction of the linear relationship between two variables that is defined as the covariance of the variables divided by the product of their standard deviations. | |
C794 | 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. The rectified linear activation function overcomes the vanishing gradient problem, allowing models to learn faster and perform better. | |
C795 | 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. A tensor can be originated from the input data or the result of a computation. | |
C796 | FFTs are great at analyzing vibration when there are a finite number of dominant frequency components; but power spectral densities (PSD) are used to characterize random vibration signals. | |
C797 | In a census, data about all individual units (e.g. people or households) are collected in the population. In a survey, data are only collected for a sub-part of the population; this part is called a sample. These data are then used to estimate the characteristics of the whole population. | |
C798 | RELU activation solves this by having a gradient slope of 1, so during backpropagation, there isn't gradients passed back that are progressively getting smaller and smaller. but instead they are staying the same, which is how RELU solves the vanishing gradient problem. | |
C799 | Data Collection & Analysis Tools Related TopicsBox & Whisker Plot.Check Sheet.Control Chart.Design of Experiments (DOE)Histogram.Scatter Diagram.Stratification.Survey. |
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