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C2900 | You calculate the mean, say it's 10. You calculate the standard deviation: it's 12. That means that any number from 10 to 22 is within one standard deviation away from the mean. Now if your data are symmetric (say normal), any number from -2 to 10 is also within a standard deviation from the mean. | |
C2901 | Here are the four most common ways of measuring reliability for any empirical method or metric:inter-rater reliability.test-retest reliability.parallel forms reliability.internal consistency reliability. | |
C2902 | An autocorrelation plot is designed to show whether the elements of a time series are positively correlated, negatively correlated, or independent of each other. (The prefix auto means “self”— autocorrelation specifically refers to correlation among the elements of a time series.) | |
C2903 | Sentiment analysis also means you'll be able to detect changes in the overall opinion towards your brand. Because it provides insight into the way your customers are feeling when they approach you, you can monitor trends and see if overall opinion towards your company drops or rises. | |
C2904 | Mini Batch Gradient Descent Batch : A CompromiseEasily fits in the memory.It is computationally efficient.Benefit from vectorization.If stuck in local minimums, some noisy steps can lead the way out of them.Average of the training samples produces stable error gradients and convergence. | |
C2905 | Jeffrey Jacob Abrams (born June 27, 1966), more commonly known as J.J. Abrams, is one of the creators and executive producers of Lost. He is also credited with being the driving force behind the show, along with writing and directing the episodes "Pilot, Part 1" and "Pilot, Part 2". | |
C2906 | Sample space is all the possible outcomes of an event. Sometimes the sample space is easy to determine. For example, if you roll a dice, 6 things could happen. You could roll a 1, 2, 3, 4, 5, or 6. | |
C2907 | The probability distribution of a discrete random variable can always be represented by a table. For example, suppose you flip a coin two times. The probability of getting 0 heads is 0.25; 1 head, 0.50; and 2 heads, 0.25. Thus, the table is an example of a probability distribution for a discrete random variable. | |
C2908 | Random Forest uses bootstrap sampling and feature sampling, i.e row sampling and column sampling. Therefore Random Forest is not affected by multicollinearity that much since it is picking different set of features for different models and of course every model sees a different set of data points. | |
C2909 | Because a chi-square test is a univariate test; it does not consider relationships among multiple variables at the same time. Therefore, dependencies detected by chi-square analyses may be unrealistic or non-causal. There may be other unseen factors that make the variables appear to be associated. | |
C2910 | The loss function is usually either the mean-squared error or cross-entropy between the output and the input, known as the reconstruction loss, which penalizes the network for creating outputs different from the input. | |
C2911 | The probability distribution for a random variable describes how the probabilities are distributed over the values of the random variable. For a discrete random variable, x, the probability distribution is defined by a probability mass function, denoted by f(x). | |
C2912 | Definition Quantile. A quantile defines a particular part of a data set, i.e. a quantile determines how many values in a distribution are above or below a certain limit. Special quantiles are the quartile (quarter), the quintile (fifth) and percentiles (hundredth). | |
C2913 | Discrete Variable. Discrete Variable. Variables that can only take on a finite number of values are called "discrete variables." All qualitative variables are discrete. Some quantitative variables are discrete, such as performance rated as 1,2,3,4, or 5, or temperature rounded to the nearest degree. | |
C2914 | Sampling Frame vs. A sampling frame is a list of things that you draw a sample from. A sample space is a list of all possible outcomes for an experiment. For example, you might have a sampling frame of names of people in a certain town for a survey you're going to be conducting on family size. | |
C2915 | A random variable Xk is referred to as a kth-order Erlang (or Erlang-k) random variable with parameter λ if its PDF is given by. f X k ( x ) = { λ k x k − 1 e − λ x ( k − 1 ) ! k = 1 , 2 , 3 , … ; x ≥ 0 0 x < 0. | |
C2916 | First, make a list of the possible outcomes for each flip. Next, count the number of the possible outcomes for each flip. There are two outcomes for each flip of a coin: heads or tails. Then, multiply the number of outcomes by the number of flips. | |
C2917 | Convolution neural network is a type of neural network which has some or all convolution layers. Feed forward neural network is a network which is not recursive. neurons in this layer were only connected to neurons in the next layer. neurons in this layer were only connected to neurons in the next layer. | |
C2918 | A dataset can be created in three different ways: As a copy of an existing dataset in the database or on your local computer. As a child dataset from an existing global dataset in the database or on your local computer. The time period and the dataset name cannot be changed in this case. | |
C2919 | Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data. | |
C2920 | It might take about 2-4 hours of coding and 1-2 hours of training if done in Python and Numpy (assuming sensible parameter initialization and a good set of hyperparameters). No GPU required, your old but gold CPU on a laptop will do the job. Longer training time is expected if the net is deeper than 2 hidden layers. | |
C2921 | Stochastic (from from Greek στόχος (stókhos) 'aim, guess'.) is any randomly determined process. In mathematics the terms stochastic process and random process are interchangeable. | |
C2922 | For linear algebra, it's very helpful to prepare by doing simple practice problems with the basic axioms of vector spaces and inner products. I was always mediocre at algebra, but good at visualizing 2D and 3D things. | |
C2923 | We can interpret the Poisson regression coefficient as follows: for a one unit change in the predictor variable, the difference in the logs of expected counts is expected to change by the respective regression coefficient, given the other predictor variables in the model are held constant. | |
C2924 | Here are the steps to split a decision tree using reduction in variance:For each split, individually calculate the variance of each child node.Calculate the variance of each split as the weighted average variance of child nodes.Select the split with the lowest variance.More items• | |
C2925 | The fact that two variables are strongly correlated does not in itself imply a cause-and-effect relationship between the variables. | |
C2926 | A point estimate is the value of a statistic that estimates the value of a parameter. For example, the sample mean is a point estimate of the population mean. The arithmetic mean is a single value meant to "sum up" a data set. To calculate the mean, add up all the values and divide by the number of values. | |
C2927 | Specifically, the learning rate is a configurable hyperparameter used in the training of neural networks that has a small positive value, often in the range between 0.0 and 1.0. The learning rate controls how quickly the model is adapted to the problem. | |
C2928 | To recap the differences between the two: Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. While both fall under the broad category of artificial intelligence, deep learning is what powers the most human-like artificial intelligence. | |
C2929 | The process of determining the frequency contents of a continuous-time signal in the discrete-time domain is known as spectral analysis. Hence, the main objective of spectral analysis is the determination of the power spectrum density (PSD) of a random process. | |
C2930 | The main difference between Binomial and Poisson Distribution is that the Binomial distribution is only for a certain frame or a probability of success and the Poisson distribution is used for events that could occur a very large number of times. | |
C2931 | Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like situation. Its goal is to maximize the total reward. | |
C2932 | Cross-validation is primarily used in applied machine learning to estimate the skill of a machine learning model on unseen data. That is, to use a limited sample in order to estimate how the model is expected to perform in general when used to make predictions on data not used during the training of the model. | |
C2933 | In the context of AB testing experiments, statistical significance is how likely it is that the difference between your experiment's control version and test version isn't due to error or random chance. It's commonly used in business to observe how your experiments affect your business's conversion rates. | |
C2934 | The t-test is commonly used in statistical analysis. It is an appropriate method for comparing two groups of continuous data which are both normally distributed. The most commonly used forms of the t- test are the test of hypothesis, the single-sample, paired t-test, and the two-sample, unpaired t-test. | |
C2935 | The DCT can be used to convert the signal (spatial information) into numeric data ("frequency" or "spectral" information) so that the image's information exists in a quantitative form that can be manipulated for compression. The signal for a graphical image can be thought of as a three-dimensional signal. | |
C2936 | Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. LSTM networks are well-suited to classifying, processing and making predictions based on time series data, since there can be lags of unknown duration between important events in a time series. | |
C2937 | Batch means that you use all your data to compute the gradient during one iteration. Mini-batch means you only take a subset of all your data during one iteration. | |
C2938 | The median filter is a non-linear digital filtering technique, often used to remove noise from an image or signal. Such noise reduction is a typical pre-processing step to improve the results of later processing (for example, edge detection on an image). | |
C2939 | The learning algorithm is called consistent with respect to F and P if the risk R(fn) converges in probability to the risk R(fF) of the best classifier in F, that is for all ε > 0, P(R(fn) − R(fF) > ε) → 0 as n → ∞. 2. | |
C2940 | A posterior probability, in Bayesian statistics, is the revised or updated probability of an event occurring after taking into consideration new information. In statistical terms, the posterior probability is the probability of event A occurring given that event B has occurred. | |
C2941 | In a hypothesis test, we:Evaluate the null hypothesis, typically denoted with H0. Always write the alternative hypothesis, typically denoted with Ha or H1, using less than, greater than, or not equals symbols, i.e., (≠, >, or <).More items | |
C2942 | The difference between multi-task learning and meta-learning is: in multitask learning, your goal would be to try to solve all of the training tasks shown in the gray box (on the left picture); whereas in meta-learning your goal is to use these training tasks in order to solve new tasks with a small amount of data, so | |
C2943 | In artificial intelligence and operations research, constraint satisfaction is the process of finding a solution to a set of constraints that impose conditions that the variables must satisfy. Constraint propagation methods are also used in conjunction with search to make a given problem simpler to solve. | |
C2944 | We can approximate the flux across Sr using the divergence theorem as follows: ∬SrF·dS=∭BrdivFdV≈∭BrdivF(P)dV=divF(P)V(Br). and we can consider the divergence at P as measuring the net rate of outward flux per unit volume at P. | |
C2945 | The tm package utilizes the Corpus as its main structure. A corpus is simply a collection of documents, but like most things in R , the corpus has specific attributes that enable certain types of analysis. Volitile Corpus (VCorpus) is a temporary object within R and is the default when assigning documents to a corpus. | |
C2946 | The population mean of the distribution of sample means is the same as the population mean of the distribution being sampled from. Thus as the sample size increases, the standard deviation of the means decreases; and as the sample size decreases, the standard deviation of the sample means increases. | |
C2947 | The margin of error increases as the level of confidence increases because the larger the expected proportion of intervals that will contain the parameter, the larger the margin of error. The larger the level of confidence is, the larger number of intervals that will contain the parameter. | |
C2948 | The workflow for using TensorFlow Lite involves the following steps:Pick a model. Bring your own TensorFlow model, find a model online, or pick a model from our Pre-trained models to drop in or retrain.Convert the model. Deploy to your device. Optimize your model. | |
C2949 | Algorithms consist of instructions that are carried out (performed) one after another. Sequencing is the specific order in which instructions are performed in an algorithm. For example, a very simple algorithm for brushing teeth might consist of these steps: put toothpaste on toothbrush. | |
C2950 | Learning rate is a hyper-parameter th a t controls how much we are adjusting the weights of our network with respect the loss gradient. Furthermore, the learning rate affects how quickly our model can converge to a local minima (aka arrive at the best accuracy). | |
C2951 | A distribution in statistics is a function that shows the possible values for a variable and how often they occur. | |
C2952 | If I know a programming language, where is a great place to start practicing algorithms? Become proficient at written communication. Learn Functional Programming. Learn Object Oriented Analysis and Design. Free Code Camp.More items• | |
C2953 | You are hereTraining an Artificial Neural Network.The Iterative Learning Process.Feedforward, Back-Propagation.Structuring the Network.Rule One: As the complexity in the relationship between the input data and the desired output increases, the number of the processing elements in the hidden layer should also increase.More items | |
C2954 | 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. | |
C2955 | A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). | |
C2956 | Pattern recognition is a process in which we use multiple senses in order to make decisions. As we go through our day, our brain's pattern recognition abilities help us recognise certain objects and situations. | |
C2957 | In probability, two events are independent if the incidence of one event does not affect the probability of the other event. If the incidence of one event does affect the probability of the other event, then the events are dependent. There is a red 6-sided fair die and a blue 6-sided fair die. | |
C2958 | 0:0010:07Suggested clip 119 secondsProbability Exponential Distribution Problems - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C2959 | Fundamentally, classification is about predicting a label and regression is about predicting a quantity. That classification is the problem of predicting a discrete class label output for an example. That regression is the problem of predicting a continuous quantity output for an example. | |
C2960 | NLP is short for natural language processing while NLU is the shorthand for natural language understanding. Similarly named, the concepts both deal with the relationship between natural language (as in, what we as humans speak, not what computers understand) and artificial intelligence. | |
C2961 | Random assignment of participants helps to ensure that any differences between and within the groups are not systematic at the outset of the experiment. Thus, any differences between groups recorded at the end of the experiment can be more confidently attributed to the experimental procedures or treatment. | |
C2962 | Subsampling is the process of sampling a signal with a frequency lower than twice the highest signal frequency, but higher than two times the signal bandwidth. | |
C2963 | The pdf represents the relative frequency of failure times as a function of time. The cdf is a function, F(x)\,\!, of a random variable X\,\!, and is defined for a number x\,\! | |
C2964 | These are the steps we are going to do:Make a stupid model as an example, train and store it.Fetch the variables you need from your stored model.Build the tensor info from them.Create the model signature.Create and save a model builder.Download a Docker image with TensorFlow serving already compile on it.More items• | |
C2965 | My main criticism of Bayes' Theorem is that it is stating the obvious, and much like the "Law of Large Numbers" (which essentially states that N * x = N*x = Nx) doesn't deserve to have a name. | |
C2966 | Mean Squared Error, commonly used for linear regression models, isn't convex for logistic regression. This is because the logistic function isn't always convex. The logarithm of the likelihood function is however always convex. | |
C2967 | The chi-square test is the most commonly used to test the goodness of fit tests and is used for discrete distributions like the binomial distribution and the Poisson distribution, whereas The Kolmogorov-Smirnov and Anderson-Darling goodness of fit tests are used for continuous distributions. | |
C2968 | The Loss Function is one of the important components of Neural Networks. Loss is nothing but a prediction error of Neural Net. And the method to calculate the loss is called Loss Function. In simple words, the Loss is used to calculate the gradients. And gradients are used to update the weights of the Neural Net. | |
C2969 | Two random variables are independent if they convey no information about each other and, as a consequence, receiving information about one of the two does not change our assessment of the probability distribution of the other. | |
C2970 | The letter "x" is often used in algebra to mean a value that is not yet known. It is called a "variable" or sometimes an "unknown". In x + 2 = 7, x is a variable, but we can work out its value if we try! A variable doesn't have to be "x", it could be "y", "w" or any letter, name or symbol. | |
C2971 | Running the ProcedureOpen the Frequencies window (Analyze > Descriptive Statistics > Frequencies) and double-click on variable Rank.To request the mode statistic, click Statistics. Check the box next to Mode, then click Continue.To turn on the bar chart option, click Charts. When finished, click OK. | |
C2972 | This is when the kernel trick comes in. It allows us to operate in the original feature space without computing the coordinates of the data in a higher dimensional space. In essence, what the kernel trick does for us is to offer a more efficient and less expensive way to transform data into higher dimensions. | |
C2973 | Real-time big data analytics means that big data is processed as it arrives and either a business user gets consumable insights without exceeding a time period allocated for decision-making or an analytical system triggers an action or a notification. | |
C2974 | Here are some examples of discrete variables: Number of children per family. Number of students in a class. Number of citizens of a country. | |
C2975 | Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data. | |
C2976 | A random process is a time-varying function that assigns the outcome of a random experiment to each time instant: X(t). • For a fixed (sample path): a random process is a time varying function, e.g., a signal. | |
C2977 | Mini-Max Algorithm in Artificial Intelligence. Mini-max algorithm is a recursive or backtracking algorithm which is used in decision-making and game theory. It provides an optimal move for the player assuming that opponent is also playing optimally. This Algorithm computes the minimax decision for the current state. | |
C2978 | In computer science, locality-sensitive hashing (LSH) is an algorithmic technique that hashes similar input items into the same "buckets" with high probability. It differs from conventional hashing techniques in that hash collisions are maximized, not minimized. | |
C2979 | The number of hidden neurons should be between the size of the input layer and the size of the output layer. The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer. The number of hidden neurons should be less than twice the size of the input layer. | |
C2980 | If r is the number of possible character codes on an computer, and if table_size is a prime such that r % table_size equal 1, then hash function h(key) = key % table_size is simply the sum of the binary representation of the characters in the key mod table_size. | |
C2981 | The batch size is a hyperparameter of gradient descent that controls the number of training samples to work through before the model's internal parameters are updated. The number of epochs is a hyperparameter of gradient descent that controls the number of complete passes through the training dataset. | |
C2982 | Introduction Nonparametric Test: Those procedures that test hypotheses that tests hypotheses that are not statements about population parameters are classified as nonparametric. Distribution free procedure: Those procedures that make no assumption about the sampled population are called distribution free procedures. | |
C2983 | The outcome variable is also called the response or dependent variable, and the risk factors and confounders are called the predictors, or explanatory or independent variables. In regression analysis, the dependent variable is denoted "Y" and the independent variables are denoted by "X". | |
C2984 | “Risk” refers to the probability of occurrence of an event or outcome. Statistically, risk = chance of the outcome of interest/all possible outcomes. The term “odds” is often used instead of risk. | |
C2985 | Sampling is done because you usually cannot gather data from the entire population. Even in relatively small populations, the data may be needed urgently, and including everyone in the population in your data collection may take too long. | |
C2986 | Median filtering A median filter is a nonlinear filter in which each output sample is computed as the median value of the input samples under the window – that is, the result is the middle value after the input values have been sorted. Ordinarily, an odd number of taps is used. | |
C2987 | Lambda is defined as an asymmetrical measure of association that is suitable for use with nominal variables. It may range from 0.0 to 1.0. Lambda provides us with an indication of the strength of the relationship between independent and dependent variables. | |
C2988 | Since the errors are squared before they are averaged, the RMSE gives a relatively high weight to large errors. This means the RMSE is most useful when large errors are particularly undesirable. | |
C2989 | In statistics, omitted-variable bias (OVB) occurs when a statistical model leaves out one or more relevant variables. The bias results in the model attributing the effect of the missing variables to those that were included. | |
C2990 | Quantile plots directly display the quantiles of a set of values. The sample quantiles are plotted against the fraction of the sample they correspond to. There is no built-in quantile plot in R, but it is relatively simple to produce one. Quantile-quantile plots allow us to compare the quantiles of two sets of numbers. | |
C2991 | Non-response bias is a type of bias that occurs when people are unwilling or unable to respond to a survey due to a factor that makes them differ greatly from people who respond. The difference between non-respondents and respondents is usually an influencing factor for the lack of response. | |
C2992 | In artificial intelligence, an intelligent agent (IA) refers to an autonomous entity which acts, directing its activity towards achieving goals (i.e. it is an agent), upon an environment using observation through sensors and consequent actuators (i.e. it is intelligent). | |
C2993 | Attention is simply a vector, often the outputs of dense layer using softmax function. However, attention partially fixes this problem. It allows machine translator to look over all the information the original sentence holds, then generate the proper word according to current word it works on and the context. | |
C2994 | Accuracy = TP+TN/TP+FP+FN+TN. Precision - Precision is the ratio of correctly predicted positive observations to the total predicted positive observations. | |
C2995 | Impressive Applications of Deep Learning Natural language processing is not “solved“, but deep learning is required to get you to the state-of-the-art on many challenging problems in the field. | |
C2996 | Hierarchical Clustering | |
C2997 | “And unlike what any one person can analyze, machine learning can take vast amounts of data over time and make predictions to improve the customer experience and provide real value to the end-user.” | |
C2998 | A frequency table is a method of organizing raw data in a compact form by displaying a series of scores in ascending or descending order, together with their frequencies—the number of times each score occurs in the respective data set. | |
C2999 | Covariance Matrix is a measure of how much two random variables gets change together. The Covariance Matrix is also known as dispersion matrix and variance-covariance matrix. The covariance between two jointly distributed real-valued random variables X and Y with finite second moments is defined as. |
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