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C4200 | Learning a new skill is often an extremely rewarding experience. If it's something you like, you'll quickly notice yourself improving, which can give you a great confidence boost. In most cases, trying something new is often about overcoming fear. | |
C4201 | Misleading statistics are simply the misusage - purposeful or not - of a numerical data. The results provide a misleading information to the receiver, who then believes something wrong if he or she does not notice the error or the does not have the full data picture. | |
C4202 | The term "c=0" I think was coined by Nicholas Squeglia. It is defined as a sampling plan which does not allow for the acceptance if any defects are found. | |
C4203 | The Minimax algorithm helps find the best move, by working backwards from the end of the game. At each step it assumes that player A is trying to maximize the chances of A winning, while on the next turn player B is trying to minimize the chances of A winning (i.e., to maximize B's own chances of winning). | |
C4204 | The major difference between using a Z score and a T statistic is that you have to estimate the population standard deviation. The T test is also used if you have a small sample size (less than 30). | |
C4205 | An eigenvalue is a number, telling you how much variance there is in the data in that direction, in the example above the eigenvalue is a number telling us how spread out the data is on the line. The eigenvector with the highest eigenvalue is therefore the principal component. | |
C4206 | In statistics, a rank correlation is any of several statistics that measure an ordinal association—the relationship between rankings of different ordinal variables or different rankings of the same variable, where a "ranking" is the assignment of the ordering labels "first", "second", "third", etc. to different | |
C4207 | While explanation for sudden death in certain infants remains incomplete, the term SIDS was only accepted as an official diagnosis on death certificates in 1971, with the term “sudden infant death” being allocated a separate code (coding number 798.0) in the World Health Organization's International Classification of | |
C4208 | “K-means can't handle non-convex sets”. Convex sets: In Euclidean space, an object is convex if for every pair of points within the object, every point on the straight line segment that joins them is also within the object. That's two non-convex shapes, and they are not spatially separated. | |
C4209 | Moments help in finding AM, standard deviation and variance of the population directly, and they help in knowing the graphic shapes of the population. We can call moments as the constants used in finding the graphic shape, as the graphic shape of the population also help a lot in characterizing a population. | |
C4210 | The general linear model requires that the response variable follows the normal distribution whilst the generalized linear model is an extension of the general linear model that allows the specification of models whose response variable follows different distributions. | |
C4211 | A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population. | |
C4212 | Separating data into training and testing sets is an important part of evaluating data mining models. By using similar data for training and testing, you can minimize the effects of data discrepancies and better understand the characteristics of the model. | |
C4213 | A kernel is the foundational layer of an operating system (OS). It functions at a basic level, communicating with hardware and managing resources, such as RAM and the CPU. The kernel performs a system check and recognizes components, such as the processor, GPU, and memory. It also checks for any connected peripherals. | |
C4214 | The normal distribution is a continuous probability distribution that is symmetrical on both sides of the mean, so the right side of the center is a mirror image of the left side. The area under the normal distribution curve represents probability and the total area under the curve sums to one. | |
C4215 | The sample covariance matrix is a square matrix whose i, j element is the sample covariance (an estimate of the population covariance) between the sets of observed values of two of the variables and whose i, i element is the sample variance of the observed values of one of the variables. | |
C4216 | Prior probability shows the likelihood of an outcome in a given dataset. For example, in the mortgage case, P(Y) is the default rate on a home mortgage, which is 2%. P(Y|X) is called the conditional probability, which provides the probability of an outcome given the evidence, that is, when the value of X is known. | |
C4217 | Singular value decomposition is essentially trying to reduce a rank matrix to a rank K matrix. But what does this mean? It means that we can take a list of unique vectors, and approximate them as a linear combination of unique vectors. Take this example, the image below is an image made of 400 unique row vectors. | |
C4218 | Gradient clipping is a technique to prevent exploding gradients in very deep networks, usually in recurrent neural networks. With gradient clipping, pre-determined gradient threshold be introduced, and then gradients norms that exceed this threshold are scaled down to match the norm. | |
C4219 | The system of IP address classes was developed for the purpose of Internet IP addresses assignment. The classes created were based on the network size. For example, for the small number of networks with a very large number of hosts, the Class A was created. | |
C4220 | An indicator random variable is a special kind of random variable associated with the occurence of an event. The indicator random variable IA associated with event A has value 1 if event A occurs and has value 0 otherwise. In other words, IA maps all outcomes in the set A to 1 and all outcomes outside A to 0. | |
C4221 | Statistical learning is a framework for understanding data based on statistics, which can be classified as supervised or unsupervised. | |
C4222 | Empirical definitions. The definition of empirical is something that is based solely on experiment or experience. An example of empirical is the findings of dna testing. | |
C4223 | Multiclass classification makes the assumption that each sample is assigned to one and only one label: a fruit can be either an apple or a pear but not both at the same time. - Multilabel classification assigns to each sample a set of target labels. The set of labels can be different for each output variable. | |
C4224 | A false negative is a test result that indicates a person does not have a disease or condition when the person actually does have it, according to the National Institute of Health (NIH). | |
C4225 | Support Vector Machine algorithms are supervised learning models that analyse data used for classification and regression analysis. They essentially filter data into categories, which is achieved by providing a set of training examples, each set marked as belonging to one or the other of the two categories. | |
C4226 | 1 — Linear Regression. 2 — Logistic Regression. 3 — Linear Discriminant Analysis. 4 — Classification and Regression Trees. 5 — Naive Bayes. 6 — K-Nearest Neighbors. 7 — Learning Vector Quantization. 8 — Support Vector Machines.More items• | |
C4227 | One way of finding a point estimate ˆx=g(y) is to find a function g(Y) that minimizes the mean squared error (MSE). Here, we show that g(y)=E[X|Y=y] has the lowest MSE among all possible estimators. That is why it is called the minimum mean squared error (MMSE) estimate. h(a)=E[(X−a)2]=EX2−2aEX+a2. | |
C4228 | A two layer (one input layer, one output layer; no hidden layer) neural network can represent the XOR function. We must compose multiple logical operations by using a hidden layer to represent the XOR function. | |
C4229 | Neurons are organized into bundle fibers called nerves. Dendrites are structures of neurons that conduct electrical impulses toward the cell body. | |
C4230 | Convenience sampling is a type of nonprobability sampling in which people are sampled simply because they are "convenient" sources of data for researchers. In probability sampling, each element in the population has a known nonzero chance of being selected through the use of a random selection procedure. | |
C4231 | A regression line is a straight line that de- scribes how a response variable y changes as an explanatory variable x changes. We often use a regression line to predict the value of y for a given value of x. | |
C4232 | A sparsity penalty term is included in the loss function to prevent the identity mapping by keeping only a selected set of neurons active at any instance. The constraint forces the AE to represent each input using only a small number of hidden neurons. | |
C4233 | In statistics, Bayesian linear regression is an approach to linear regression in which the statistical analysis is undertaken within the context of Bayesian inference. | |
C4234 | Binary cross-entropy is for multi-label classifications, whereas categorical cross entropy is for multi-class classification where each example belongs to a single class. | |
C4235 | A decision tree typically starts with a single node, which branches into possible outcomes. Each of those outcomes leads to additional nodes, which branch off into other possibilities. A decision node, represented by a square, shows a decision to be made, and an end node shows the final outcome of a decision path. | |
C4236 | To activate the Multinomial Logit Model dialog box, start XLSTAT, then select the XLSTAT / Modeling data / Logistic regression for binary response data command, or click on the logistic regression button of the Modeling Data toolbar (see below). When you click on the button, the Logistic regression dialog box appears. | |
C4237 | Specifically, you learned: Machine learning algorithms are procedures that are implemented in code and are run on data. Machine learning models are output by algorithms and are comprised of model data and a prediction algorithm. | |
C4238 | To calculate: Administer the two tests to the same participants within a short period of time. Correlate the test scores of the two tests. – Inter-Rater Reliability: Determines how consistent are two separate raters of the instrument. | |
C4239 | The t‐distribution is used as an alternative to the normal distribution when sample sizes are small in order to estimate confidence or determine critical values that an observation is a given distance from the mean. | |
C4240 | To calculate the learnable parameters here, all we have to do is just multiply the by the shape of width m, height n, previous layer's filters d and account for all such filters k in the current layer. Don't forget the bias term for each of the filter. | |
C4241 | The loss function used by the perceptron algorithm is called 0-1 loss. 0-1 loss simply means that for each mistaken prediction you incur a penalty of 1 and for each correct prediction incur no penalty. The problem with this loss function is given a linear classifier its hard to move towards a local optimum. | |
C4242 | 7 Steps of Machine LearningStep #1: Gathering Data. Step #2: Preparing that Data. Step #3: Choosing a Model. Step #4: Training. Step #5: Evaluation. Step #6: Hyperparameter Tuning. Step #7: Prediction. | |
C4243 | Yes, the vectors from a word2vec model can be used as input in the learning of a new task, and in some (not all) cases, may yield better performance in the new model. | |
C4244 | Mutual information is a quantity that measures a relationship between two random variables that are sampled simultaneously. In particular, it measures how much information is communicated, on average, in one random variable about another. | |
C4245 | The decision tree tool is used in real life in many areas, such as engineering, civil planning, law, and business. Decision trees can be divided into two types; categorical variable and continuous variable decision trees. | |
C4246 | Sampling from a 1D DistributionNormalize the function f(x) if it isn't already normalized.Integrate the normalized PDF f(x) to compute the CDF, F(x).Invert the function F(x). Substitute the value of the uniformly distributed random number U into the inverse normal CDF. | |
C4247 | Interpolation is making an educated guess with the information within a certain data set. It is a “best guess” using the information you have at hand. | |
C4248 | The problems that are addressed by AI search algorithms fall into three general classes: single-agent path-finding problems, two-players games, and constraint-satisfaction problems. Search plays a major role in solving many Artificial Intelligence (AI) problems. Search is a universal problem-solving mechanism in AI. | |
C4249 | Back-propagation is just a way of propagating the total loss back into the neural network to know how much of the loss every node is responsible for, and subsequently updating the weights in such a way that minimizes the loss by giving the nodes with higher error rates lower weights and vice versa. | |
C4250 | How many parity check bits must be included with the data word to achieve single-bit error correction and double error correction when data words are as follows: 16 bits. | |
C4251 | Word2vec is a technique for natural language processing. The word2vec algorithm uses a neural network model to learn word associations from a large corpus of text. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. | |
C4252 | Step 1: Load Python packages. Step 2: Pre-Process the data. Step 3: Subset the data. Step 4: Split the data into train and test sets. Step 5: Build a Random Forest Classifier. Step 6: Predict. Step 7: Check the Accuracy of the Model. Step 8: Check Feature Importance. | |
C4253 | From Wikipedia, the free encyclopedia. In mathematical optimization, constrained optimization (in some contexts called constraint optimization) is the process of optimizing an objective function with respect to some variables in the presence of constraints on those variables. | |
C4254 | Cross correlation presents a technique for comparing two time series and finding objectively how they match up with each other, and in particular where the best match occurs. It can also reveal any periodicities in the data. | |
C4255 | The most common evaluation metric that is used in object recognition tasks is 'mAP', which stands for 'mean average precision'. It is a number from 0 to 100 and higher values are typically better, but it's value is different from the accuracy metric in classification. | |
C4256 | How to Analyze Survey ResultsUnderstand the four measurement levels. Select your research question(s). Analyze quantitative data first. Use cross-tabulation to better understand your target audience. Understand the statistical significance. Take into consideration causation versus correlation. Compare data with that of past data. | |
C4257 | Different types of deep learning models.Autoencoders. An autoencoder is an artificial neural network that is capable of learning various coding patterns. Deep Belief Net. Convolutional Neural Networks. Recurrent Neural Networks. Reinforcement Learning to Neural Networks. | |
C4258 | Log-Log linear regression A regression model where the outcome and at least one predictor are log transformed is called a log-log linear model. | |
C4259 | Uninformative priors. An uninformative prior or diffuse prior expresses vague or general information about a variable. The term "uninformative prior" is somewhat of a misnomer. Such a prior might also be called a not very informative prior, or an objective prior, i.e. one that's not subjectively elicited. | |
C4260 | Univariate and multivariate represent two approaches to statistical analysis. Univariate involves the analysis of a single variable while multivariate analysis examines two or more variables. Most multivariate analysis involves a dependent variable and multiple independent variables. | |
C4261 | Markov Chain Monte–Carlo (MCMC) is an increasingly popular method for obtaining information about distributions, especially for estimating posterior distributions in Bayesian inference. This article provides a very basic introduction to MCMC sampling. | |
C4262 | Python is easy to learn and work with, and provides convenient ways to express how high-level abstractions can be coupled together. Nodes and tensors in TensorFlow are Python objects, and TensorFlow applications are themselves Python applications. The actual math operations, however, are not performed in Python. | |
C4263 | A utility-based agent is an agent that acts based not only on what the goal is, but the best way to reach that goal. Think about it this way: A goal-based agent (yes, another of the intelligent agents out there) makes decisions based simply on achieving a set goal. | |
C4264 | The three main metrics used to evaluate a classification model are accuracy, precision, and recall. Accuracy is defined as the percentage of correct predictions for the test data. It can be calculated easily by dividing the number of correct predictions by the number of total predictions. | |
C4265 | Word2vec being log-linear means we calculate the gradient at the output and then directly propagate this back into the embedding parameters (the main computational burden during training). This means faster trainer over bigger datasets yielding more accurate embedding vectors. | |
C4266 | Generative Models. LSTMs can be used as a generative model. Given a large corpus of sequence data, such as text documents, LSTM models can be designed to learn the general structural properties of the corpus, and when given a seed input, can generate new sequences that are representative of the original corpus. | |
C4267 | A few common responses to compliments are "you're welcome", "no problem", "my pleasure" or "glad I could help". The best of all is " My Pleasure". | |
C4268 | A one-brain AI would still not be a true intelligence, only a better general-purpose AI—Legg's multi-tool. But whether they're shooting for AGI or not, researchers agree that today's systems need to be made more general-purpose, and for those who do have AGI as the goal, a general-purpose AI is a necessary first step. | |
C4269 | The chi-square test may be used both as a test of goodness-of-fit (comparing frequencies of one nominal variable to theoretical expectations) and as a test of independence (comparing frequencies of one nominal variable for different values of a second nominal variable). | |
C4270 | Gradient descent is a simple optimization procedure that you can use with many machine learning algorithms. Stochastic gradient descent refers to calculating the derivative from each training data instance and calculating the update immediately. | |
C4271 | They are often confused with each other. The 'K' in K-Means Clustering has nothing to do with the 'K' in KNN algorithm. k-Means Clustering is an unsupervised learning algorithm that is used for clustering whereas KNN is a supervised learning algorithm used for classification. | |
C4272 | A random variable, usually written X, is a variable whose possible values are numerical outcomes of a random phenomenon. There are two types of random variables, discrete and continuous. | |
C4273 | A one hot encoding allows the representation of categorical data to be more expressive. Many machine learning algorithms cannot work with categorical data directly. The categories must be converted into numbers. This is required for both input and output variables that are categorical. | |
C4274 | Ridge regression is a term used to refer to a linear regression model whose coefficients are not estimated by ordinary least squares (OLS), but by an estimator, called ridge estimator, that is biased but has lower variance than the OLS estimator. | |
C4275 | Weka has a lot of machine learning algorithms. This is great, it is one of the large benefits of using Weka as a platform for machine learning. A down side is that it can be a little overwhelming to know which algorithms to use, and when. | |
C4276 | An (ordinary) Poisson process is a special Markov process [ref. to Stadje in this volume], in continuous time, in which the only possible jumps are to the next higher state. A Poisson process may also be viewed as a counting process that has particular, desirable, properties. | |
C4277 | In general, logistic regression performs better when the number of noise variables is less than or equal to the number of explanatory variables and random forest has a higher true and false positive rate as the number of explanatory variables increases in a dataset. | |
C4278 | In the AI lexicon this is known as “inference.” Inference is where capabilities learned during deep learning training are put to work. Inference can't happen without training. Makes sense. That's how we gain and use our own knowledge for the most part. | |
C4279 | Hidden Markov model | |
C4280 | You should put it after the non-linearity (eg. relu layer). If you are using dropout remember to use it before. | |
C4281 | If the outcomes are mutually independent, then yes the method is valid. If the outcomes are mutually exclusive, then no, the method is not valid. It's easy to see why this is the case. If you have three binary models, then the sum of the outcomes do not necessarily sum to one. | |
C4282 | Advantages of distributed representations Mapping efficiency: a micro-feature-based distributed representation often allows a simple mapping (that uses few connections or weights) to solve a task. For example, suppose we wish to classify 100 different colored shapes as to whether or not they are yellow. | |
C4283 | Categorical data clustering refers to the case where the data objects are defined over categorical attributes. That is, there is no single ordering or inherent distance function for the categorical values, and there is no mapping from categorical to numerical values that is semantically sensible. | |
C4284 | A Poisson queue is a queuing model in which the number of arrivals per unit of time and the number of completions of service per unit of time, when there are customers waiting, both have the Poisson distribution. The Poisson distribution is good to use if the arrivals are all random and independent of each other. | |
C4285 | Let's Start with NLP and NLG Setting aside NLU for the moment, we can draw a really simple distinction: Natural Language Processing (NLP) is what happens when computers read language. NLP processes turn text into structured data. Natural Language Generation (NLG) is what happens when computers write language. | |
C4286 | The way it works is that, NMF decomposes (or factorizes) high-dimensional vectors into a lower-dimensional representation. These lower-dimensional vectors are non-negative which also means their coefficients are non-negative. Using the original matrix (A), NMF will give you two matrices (W and H). | |
C4287 | It is called Laplace smoothing because the smoothing proceeds from a logic of slightly correcting the observed proportions (in the case of categorical variables) in the direction of a uniform distribution among the categories (i.e., injecting a bit of equi-probability among them). | |
C4288 | The geometric mean is used in finance to calculate average growth rates and is referred to as the compounded annual growth rate. Consider a stock that grows by 10% in year one, declines by 20% in year two, and then grows by 30% in year three. | |
C4289 | The convolutional neural networks (CNNs) have proven to be a powerful tool for discriminative learning. Recently researchers have also started to show interest in the generative aspects of CNNs in order to gain a deeper understanding of what they have learned and how to further improve them. | |
C4290 | An estimator of a given parameter is said to be unbiased if its expected value is equal to the true value of the parameter. In other words, an estimator is unbiased if it produces parameter estimates that are on average correct. | |
C4291 | 0:3910:15Suggested clip · 118 secondsConducting a Multiple Regression using Microsoft Excel Data YouTubeStart of suggested clipEnd of suggested clip | |
C4292 | The golden section search is a technique for finding the extremum (minimum or maximum) of a strictly unimodal function by successively narrowing the range of values inside which the extremum is known to exist. | |
C4293 | Research involves core signal processing, emphasising its use in a wide range of applications and integrated engineering. The work involves fast transforms and algorithms, multidimensional signal processing, 3-D image and video compression for storage and transmission. | |
C4294 | Mallick: Absolutely. A degree from Stanford in AI is worth a lot more than many other universities because you get to work with top researchers who are at the cutting edge of AI research. The choice of your major also makes a huge difference. | |
C4295 | Identifying Good Problems for MLClear Use Case. Start with the problem, not the solution. Know the Problem Before Focusing on the Data. Be prepared to have your assumptions challenged. Lean on Your Team's Logs. ML requires a lot of relevant data. Predictive Power. Your features contain predictive power. Predictions vs. Decisions. | |
C4296 | Multi hot encoding is one of such popular encoding technique in order to successfully convert categorical variables into numerical variables. Now, both independent variables and dependent variable became encoded and converted to numerical values from categorical values. | |
C4297 | Disadvantages of randomised control trial study designTrials which test for efficacy may not be widely applicable. Trials which test for effectiveness are larger and more expensive.Results may not always mimic real life treatment situation (e.g. inclusion / exclusion criteria; highly controlled setting) | |
C4298 | To interpret the PCA result, first of all, you must explain the scree plot. From the scree plot, you can get the eigenvalue & %cumulative of your data. The eigenvalue which >1 will be used for rotation due to sometimes, the PCs produced by PCA are not interpreted well. | |
C4299 | TensorFlow 2.0 is an updated version of TensorFlow that has been designed with a focus on simple execution, ease of use, and developer's productivity. TensorFlow 2.0 makes the development of machine learning applications even easier. |
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