MLAI
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Categorical DataMLAI/Preprocessing 2020. 1. 18. 20:02
1. Overview 2. Description 2.1 Encode Categorical Data Since machine learning models are based on mathematical equations you can intuitively understand that it would cause some problem if we keep the text here and the categorical variables in the equations because we would only want numbers in the equations. So that's why we need to encode the catacombs variables. That is to encode the text that..
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Missing DataMLAI/Preprocessing 2020. 1. 18. 18:36
1. Overview to start preparing the data so that our machine learning models run correctly and the first problem that we have to deal with is the case where you have some missing data in your data set and that happens quite a lot actually in real life. 2. Description 2.1 Handling Missing Data 2.1.1 Deletion to remove this line and remove this line but that can be quite dangerous because imagine t..
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Error MetricsMLAI 2019. 12. 19. 18:25
1. Overview An Error Metric is a type of Metric used to measure the error of a forecasting model. They can provide a way for forecasters to quantitatively compare the performance of competing models. These are also called loss functions. 2. Description 2.1 Error (Residual Error) $$Error=y-\hat{y}$$ Where actual values are denoted by y. Predicted values are denoted by $\hat{y}$ Ideal condition (h..
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Logistic RegressionMLAI/Regression 2019. 10. 20. 19:07
1. Overview We can predict categorical outcomes through a logistic regression yes or no will buy or won't buy 0 or 1. 2. Introduction Not only is this linear regression an awful fit but it also predicts values which are consist of only 0 and 1 which fall outside the natural domain of admission. This regression doesn't even know that our values are bounded between 0 and 1. The first assumption of..
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Simple Linear RegressionMLAI/Regression 2019. 10. 20. 18:20
1. Overview Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. For example, a modeler might want to relate the weights of individuals to their heights using a linear regression model. 2. Description 2.1 Process Ge..
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Boltzmann Machine with Energy-Based Models and Restricted Boltzmann machines(RBM)MLAI/DeepLearning 2019. 10. 19. 19:36
1. Overview A Boltzmann machine (also called stochastic Hopfield network with hidden units) is a type of stochastic recurrent neural network and Markov random field. Boltzmann machines can be seen as the stochastic, generative counterpart of Hopfield networks. They were one of the first neural networks capable of learning internal representations, and are able to represent and (given sufficient ..
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Classify Deep LearningMLAI/DeepLearning 2019. 10. 16. 22:01
1. Overview Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and the desired output value (also called the supervisory..
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Softmax and Cross-Entropy with CNNMLAI/DeepLearning 2019. 10. 16. 15:56
1. Overview 2. Description How come two output values add up to one? 2.1 Softmax function(Normalized exponential function) $$f_{j}(z)=\frac{e^{zj}}{\sum_{k}e^{zk}}$$ Normally, the dog and the cat neurons would have any kind of real values. Applying the softmax function which is written up over there at the top, and that would bring these values to be between zero and one and it would make them a..