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C5100 | Loss function for Logistic Regression The loss function for linear regression is squared loss. The loss function for logistic regression is Log Loss, which is defined as follows: Log Loss = ∑ ( x , y ) ∈ D − y log ( y ′ ) − ( 1 − y ) log where: ( x , y ) ∈ D. | |
C5101 | Active learningSet tasks which have purpose and relevance to the students.Encourage students to reflect on the meaning of what they have learnt.Allow students to negotiate goals and methods of learning with the teacher.Encourage students to critically evaluate different ways and means of learning the content.More items | |
C5102 | Multilayer Perceptron (MLP): used to apply in computer vision, now succeeded by Convolutional Neural Network (CNN). MLP is now deemed insufficient for modern advanced computer vision tasks. Has the characteristic of fully connected layers, where each perceptron is connected with every other perceptron. | |
C5103 | How to useDownload MNIST Dataset.Put and Extract it in executable directory.Run Extractor.Enter Images and Labels File (e.g t10k-images.idx3-ubyte and t10k-labels.idx1-ubyte for training and train-images.idx3-ubyte and train-labels.idx1-ubyte for testing)Enter output directory. | |
C5104 | For a regression problem, the outputs of individual models can literally be averaged to obtain the output of the ensemble model. Bagging consists in fitting several base models on different bootstrap samples and build an ensemble model that “average” the results of these weak learners. | |
C5105 | Shared weights basically means that the same weights is used for two layers in the model. This basically means that the same parameters will be used to represent two different transformations in the system. | |
C5106 | Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. This Specialization will equip you with the state-of-the-art deep learning techniques needed to build cutting-edge NLP systems. | |
C5107 | Step 1: Learn the fundamental data structures and algorithms. First, pick a favorite language to focus on and stick with it. Step 2: Learn advanced concepts, data structures, and algorithms. Step 1+2: Practice. Step 3: Lots of reading + writing. Step 4: Contribute to open-source projects. Step 5: Take a break. | |
C5108 | Feature Selection is the process where you automatically or manually select those features which contribute most to your prediction variable or output in which you are interested in. Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features. | |
C5109 | Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. | |
C5110 | Normalization basically means bringing all the values to once scale and there is nothing wrong using percentage but there must be a base value for normalizing the data and if you are asking about 100 as a base value and then converting everything as % it will not be equal to normalization as in normalization the base | |
C5111 | word2vec itself is a simple bi-layered neural network architecture, it turns text into meaningful vectors form that deeper networks can understand. In other words the out put of simple neural word2vec model is used as input for Deep Networks. | |
C5112 | There are four basic sequence learning problems: sequence prediction, sequence generation, sequence recognition, and sequential decision making. These “problems” show how sequences are formulated. | |
C5113 | "Describe what works for you.Explain your time management strategies.Demonstrate your level of organization.Give past examples.Be honest. | |
C5114 | Definition. Average precision is a measure that combines recall and precision for ranked retrieval results. For one information need, the average precision is the mean of the precision scores after each relevant document is retrieved. | |
C5115 | Method comparisonCorrelation coefficient. A correlation coefficient measures the association between two methods.Scatter plot. A scatter plot shows the relationship between two methods.Fit Y on X. Linearity. Residual plot. Average bias. Difference plot (Bland-Altman plot) Fit differences.More items• | |
C5116 | Every probability pi is a number between 0 and 1, and the sum of all the probabilities is equal to 1. Examples of discrete random variables include: The number of eggs that a hen lays in a given day (it can't be 2.3) The number of people going to a given soccer match. | |
C5117 | Spectroscopy in chemistry and physics, a method of analyzing the properties of matter from their electromagnetic interactions. Spectral estimation, in statistics and signal processing, an algorithm that estimates the strength of different frequency components (the power spectrum) of a time-domain signal. | |
C5118 | The null hypothesis is a general statement that states that there is no relationship between two phenomenons under consideration or that there is no association between two groups. An alternative hypothesis is a statement that describes that there is a relationship between two selected variables in a study. | |
C5119 | The sampling distribution of the sample mean can be thought of as "For a sample of size n, the sample mean will behave according to this distribution." Any random draw from that sampling distribution would be interpreted as the mean of a sample of n observations from the original population. | |
C5120 | The weighted kappa is calculated using a predefined table of weights which measure the degree of disagreement between the two raters, the higher the disagreement the higher the weight. | |
C5121 | In a Data Mining sense, the similarity measure is a distance with dimensions describing object features. That means if the distance among two data points is small then there is a high degree of similarity among the objects and vice versa. The similarity is subjective and depends heavily on the context and application. | |
C5122 | Gradient descent subtracts the step size from the current value of intercept to get the new value of intercept. This step size is calculated by multiplying the derivative which is -5.7 here to a small number called the learning rate. Usually, we take the value of the learning rate to be 0.1, 0.01 or 0.001. | |
C5123 | 0:5612:03Suggested clip · 85 secondsPart 7 - Absorbing Markov Chains and Absorbing States - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C5124 | AlphaGo is a computer program that plays the board game Go. In October 2015, in a match against Fan Hui, the original AlphaGo became the first computer Go program to beat a human professional Go player without handicap on a full-sized 19×19 board. | |
C5125 | If you need to change the shape of a variable, you can do the following (e.g. for a 32-bit floating point tensor): var = tf. Variable(tf.They are:reshape.squeeze (removes dimensions of size 1 from the shape of a tensor)expand_dims (adds dimensions of size 1) | |
C5126 | A test statistic is a standardized value that is calculated from sample data during a hypothesis test. A t-value of 0 indicates that the sample results exactly equal the null hypothesis. | |
C5127 | Inductive probability refers to the likelihood that an inductive argument with true premises will give a true conclusion. An argument with low inductive probability is less likely to have a true conclusion even if its premises are true. | |
C5128 | Beyond the agent and the environment, there are four main elements of a reinforcement learning system: a policy, a reward, a value function, and, optionally, a model of the environment. A policy defines the way the agent behaves in a given time. | |
C5129 | Independent and dependent variablesThe independent variable is the cause. Its value is independent of other variables in your study.The dependent variable is the effect. Its value depends on changes in the independent variable. | |
C5130 | Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. It is the endeavor to replicate or simulate human intelligence in machines. | |
C5131 | Semi-supervised clustering is a bridge between Supervised Learning and Cluster Analysis. it's about learning with both labeled and unlabeled data: sometimes we have some prior knowledge about clusters, e.g. we could have some label information. | |
C5132 | How to train your Deep Neural NetworkTraining data. Choose appropriate activation functions. Number of Hidden Units and Layers. Weight Initialization. Learning Rates. Hyperparameter Tuning: Shun Grid Search - Embrace Random Search. Learning Methods. Keep dimensions of weights in the exponential power of 2.More items• | |
C5133 | The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the differences between values (sample or population values) predicted by a model or an estimator and the values observed. | |
C5134 | Problems that require more than two hidden layers were rare prior to deep learning. Two or fewer layers will often suffice with simple data sets. However, with complex datasets involving time-series or computer vision, additional layers can be helpful. | |
C5135 | Random errors often have a Gaussian normal distribution (see Fig. 2). In such cases statistical methods may be used to analyze the data. The mean m of a number of measurements of the same quantity is the best estimate of that quantity, and the standard deviation s of the measurements shows the accuracy of the estimate. | |
C5136 | In short, linear regression is one of the mathematical models to describe the (linear) relationship between input and output. Least squares, on the other hand, is a method to metric and estimate models, in which the optimal parameters have been found. | |
C5137 | The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena. For example, heights, blood pressure, measurement error, and IQ scores follow the normal distribution. It is also known as the Gaussian distribution and the bell curve. | |
C5138 | Principal Component Analysis, or PCA, is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set. | |
C5139 | Which category? What group does this fall into? Is this weird or is something not normal? What options should we take? | |
C5140 | AlphaGo was initially trained to mimic human play by attempting to match the moves of expert players from recorded historical games, using a database of around 30 million moves. | |
C5141 | Naive Bayes assumes conditional independence, P(X|Y,Z)=P(X|Z), Whereas more general Bayes Nets (sometimes called Bayesian Belief Networks) will allow the user to specify which attributes are, in fact, conditionally independent. | |
C5142 | The disadvantage of the ANOVA F-test is that if we reject the null hypothesis, we do not know which treatments can be said to be significantly different from the others, nor, if the F-test is performed at level α, can we state that the treatment pair with the greatest mean difference is significantly different at level | |
C5143 | The t distributions were discovered by William S. Gosset was a statistician employed by the Guinness brewing company which had stipulated that he not publish under his own name. He therefore wrote under the pen name ``Student. | |
C5144 | 2:4125:32Suggested clip · 98 secondsStructural Equation Modeling: what is it and what can we use it for YouTubeStart of suggested clipEnd of suggested clip | |
C5145 | 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. | |
C5146 | Rule of Multiplication The probability that Events A and B both occur is equal to the probability that Event A occurs times the probability that Event B occurs, given that A has occurred. | |
C5147 | Autocorrelation, also known as serial correlation, is the correlation of a signal with a delayed copy of itself as a function of delay. It is often used in signal processing for analyzing functions or series of values, such as time domain signals. | |
C5148 | There are three basic concepts in reinforcement learning: state, action, and reward. The state describes the current situation. For a robot that is learning to walk, the state is the position of its two legs. For a Go program, the state is the positions of all the pieces on the board. | |
C5149 | The Poisson counting process can be viewed as a continuous-time Markov chain. Suppose that takes values in and is independent of . Define X t = X 0 + N t for t ∈ [ 0 , ∞ ) . | |
C5150 | Maximization Bias is a technical way of saying that Q-Learning algorithm overestimates the value function estimates (V) and action-value estimates (Q). Given the large variance in rewards, it is quite possible that the initial few estimates of the actions might be positive or more negative. | |
C5151 | Simple random sampling is a method used to cull a smaller sample size from a larger population and use it to research and make generalizations about the larger group. The advantages of a simple random sample include its ease of use and its accurate representation of the larger population. | |
C5152 | A baseball player can have higher batting average than another on each of two years, but lower than the other when the two are combined. In one case, David Justice had a higher batting average than Derek Jeter in 1995 and 1996, but across the two years, Jeter's average was higher. | |
C5153 | In review, beta-endorphins are proteins that are primarily synthesized by the pituitary gland in response to physiologic stressors such as pain. They function through various mechanisms in both the central and peripheral nervous system to relieve pain when bound to their mu-opioid receptors. | |
C5154 | 4:306:35Suggested clip · 77 secondsMarginal PDF from Joint PDF - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C5155 | Abstract: The dimensionality curse phenomenon states that in high dimensional spaces distances between nearest and farthest points from query points become almost equal. Therefore, nearest neighbor calculations cannot discriminate candidate points. | |
C5156 | A population is called multinomial if its data is categorical and belongs to a collection of discrete non-overlapping classes. The null hypothesis for goodness of fit test for multinomial distribution is that the observed frequency fi is equal to an expected count ei in each category. | |
C5157 | A correlation analysis provides information on the strength and direction of the linear relationship between two variables, while a simple linear regression analysis estimates parameters in a linear equation that can be used to predict values of one variable based on the other. | |
C5158 | The bootstrap method is a resampling technique used to estimate statistics on a population by sampling a dataset with replacement. It is used in applied machine learning to estimate the skill of machine learning models when making predictions on data not included in the training data. | |
C5159 | The linear regression model describes the dependent variable with a straight line that is defined by the equation Y = a + b × X, where a is the y-intersect of the line, and b is its slope. | |
C5160 | In statistics, a studentized residual is the quotient resulting from the division of a residual by an estimate of its standard deviation. It is a form of a Student's t-statistic, with the estimate of error varying between points. This is an important technique in the detection of outliers. | |
C5161 | Now we'll check out the proven way to improve the performance(Speed and Accuracy both) of neural network models:Increase hidden Layers. Change Activation function. Change Activation function in Output layer. Increase number of neurons. Weight initialization. More data. Normalizing/Scaling data.More items• | |
C5162 | CNNs are the best image classifier algorithm we know of, and they work particularly well when given lots and lots of data to work with. Progressive resizing is a technique for building CNNs that can be very helpful during the training and optimization phases of a machine learning project. | |
C5163 | Model selection is the process of selecting one final machine learning model from among a collection of candidate machine learning models for a training dataset. Model selection is a process that can be applied both across different types of models (e.g. logistic regression, SVM, KNN, etc.) | |
C5164 | 4:1213:02Suggested clip · 101 secondsThe Transition Matrix - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C5165 | Cross-sectional data refers to a setoff observations taken at a single point in time. Samples are constructed by collecting the data of interest across a range of observational units – people, objects, firms – at the same time. | |
C5166 | In regression analysis, the dependent variable is denoted Y and the independent variable is denoted X. So, in this case, Y=total cholesterol and X=BMI. When there is a single continuous dependent variable and a single independent variable, the analysis is called a simple linear regression analysis . | |
C5167 | A scatter plot is a special type of graph designed to show the relationship between two variables. With regression analysis, you can use a scatter plot to visually inspect the data to see whether X and Y are linearly related. | |
C5168 | The learning rate is a hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated. The learning rate may be the most important hyperparameter when configuring your neural network. | |
C5169 | In statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a | |
C5170 | Definition. An estimator is said to be unbiased if its bias is equal to zero for all values of parameter θ, or equivalently, if the expected value of the estimator matches that of the parameter. | |
C5171 | Definition. An entropy source is an input device or a measured characteristic of an I/O device on a computer that supplies random bits: specifically, bits that an attacker cannot know. | |
C5172 | You can improve your pattern recognition skills by practising. Now you know that patterns can appear in numbers, objects, symbols, music and more, you can pay attention to this. Looking and listening while being aware that there are patterns in things most of the time, helps you to eventually find them easier. | |
C5173 | At the lowest, simplest level, a discriminative system (or model) is one to which you present one input and it produces a single result, which is commonly the discrimination (classification) of the input. | |
C5174 | A version space is a hierarchial representation of knowledge that enables you to keep track of all the useful information supplied by a sequence of learning examples without remembering any of the examples. | |
C5175 | So, For hidden layers the best option to use is ReLU, and the second option you can use as SIGMOID. For output layers the best option depends, so we use LINEAR FUNCTIONS for regression type of output layers and SOFTMAX for multi-class classification. | |
C5176 | In random forest different features are used for each tree while in bagging different subsets of the training data are used. Gradient boosting generates an ensemble of trees too but does so in a different way, motivated by different ideas. | |
C5177 | In General, A Discriminative model models the decision boundary between the classes. A Generative Model explicitly models the actual distribution of each class. A Discriminative model learns the conditional probability distribution p(y|x). Both of these models were generally used in supervised learning problems. | |
C5178 | A binomial random variable is the number of successes x in n repeated trials of a binomial experiment.Binomial DistributionThe mean of the distribution (μx) is equal to n * P .The variance (σ2x) is n * P * ( 1 - P ).The standard deviation (σx) is sqrt[ n * P * ( 1 - P ) ]. | |
C5179 | Answer. When the ROC curve dips prominently into the lower right half of the graph, this is likely a sign that either the wrong State Value has been specified or the wrong Test-State association direction has been specified in the "Test Direction" area of the "ROC Curve:Options" dialog. | |
C5180 | Data are skewed right when most of the data are on the left side of the graph and the long skinny tail extends to the right. Data are skewed left when most of the data are on the right side of the graph and the long skinny tail extends to the left. | |
C5181 | Google's self-driving car, Waymo, is an example of prescriptive analytics in action. The vehicle makes millions of calculations on every trip that helps the car decide when and where to turn, whether to slow down or speed up and when to change lanes — the same decisions a human driver makes behind the wheel. | |
C5182 | 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. | |
C5183 | Wilcoxon – The Wilcoxon signed rank test has the null hypothesis that both samples are from the same population. Sign – The sign test has the null hypothesis that both samples are from the same population. The sign test compares the two dependent observations and counts the number of negative and positive differences. | |
C5184 | The original LSTM model is comprised of a single hidden LSTM layer followed by a standard feedforward output layer. The Stacked LSTM is an extension to this model that has multiple hidden LSTM layers where each layer contains multiple memory cells. | |
C5185 | In mathematics and statistics, an asymptotic distribution is a probability distribution that is in a sense the "limiting" distribution of a sequence of distributions. | |
C5186 | A type II error is also known as a false negative and occurs when a researcher fails to reject a null hypothesis which is really false. | |
C5187 | Sampling From a Distribution. When we say we sample from a distribution, we mean that we choose some discrete points, with likelihood defined by the distribution's probability density function. | |
C5188 | Chi-Square DistributionThe mean of the distribution is equal to the number of degrees of freedom: μ = v.The variance is equal to two times the number of degrees of freedom: σ2 = 2 * v.When the degrees of freedom are greater than or equal to 2, the maximum value for Y occurs when Χ2 = v - 2.More items | |
C5189 | Variance (σ2) in statistics is a measurement of the spread between numbers in a data set. That is, it measures how far each number in the set is from the mean and therefore from every other number in the set. | |
C5190 | All Answers (6) Chi square test requires 2 categorical variables. T test requires 1 categorical and 1 continuous variables. You can't use them interchangeably. | |
C5191 | It uses data with several classes to predict the classification of the new sample point. KNN is non-parametric since it doesn't make any assumptions on the data being studied, i.e., the model is distributed from the data. | |
C5192 | Cognitive computing tools such as IBM Watson, artificial intelligence tools such as expert systems, and intelligent personal assistant tools such as Amazon Echo, Apple Siri, Google Assistant, and Microsoft Cortana can be used to extend the ability of humans to understand, decide, act, learn, and avoid problems. | |
C5193 | Causal inference is a statistical tool that enables our AI and machine learning algorithms to reason in similar ways. We're interested in understanding how changes in our network settings affect latency, so we use causal inference to proactively choose our settings based on this knowledge. | |
C5194 | Applications of Real-time SystemReal Time System is a system that is put through real time which means response is obtained within a specified timing constraint or system meets the specified deadline. Applications of Real-time System: Industrial application: Medical Science application: Peripheral Equipment applications:More items• | |
C5195 | The Analysis of covariance (ANCOVA) is done by using linear regression. This means that Analysis of covariance (ANCOVA) assumes that the relationship between the independent variable and the dependent variable must be linear in nature. | |
C5196 | Each approach uses several methods as follows:Clustering. hierarchical clustering, k-means. mixture models. Anomaly detection. Local Outlier Factor. Isolation Forest.Neural Networks. Autoencoders. Deep Belief Nets. Approaches for learning latent variable models such as. Expectation–maximization algorithm (EM) Method of moments. | |
C5197 | This probability is written P(B|A), notation for the probability of B given A. In the case where events A and B are independent (where event A has no effect on the probability of event B), the conditional probability of event B given event A is simply the probability of event B, that is P(B). P(A and B) = P(A)P(B|A). | |
C5198 | Important classes of stochastic processes are Markov chains and Markov processes. A Markov chain is a discrete-time process for which the future behaviour, given the past and the present, only depends on the present and not on the past. A Markov process is the continuous-time version of a Markov chain. | |
C5199 | One common method of consolidating two probability distributions is to simply average them - for every set of values A, set If the distributions both have densities, for example, averaging the probabilities results in a probability distribution with density the average of the two input densities (Figure 1). |
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