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Principal component analysis(PCA)MLAI/DimensionalityReduction 2019. 10. 5. 17:32
1. Overview Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. This transformation is defined in such a way that the first principal compo..
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Artificial neural network(ANN)MLAI/DeepLearning 2019. 10. 5. 13:39
1. Overview Artificial neural networks (ANN) or connectionist systems are computing systems that are inspired by, but not identical to, biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules. For example, in image recognition, they might learn to identify images that cont..
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Hidden Markov Model(HMM)MLAI/Classification 2019. 10. 4. 21:47
1. Overview HMM is called hidden because only the symbols emitted by the system are observable, not the underlying random walk between states. An HMM can be visualized as a finite state machine. it generates a protein sequence by emitting amino acids as it progresses through a series of states. 2. Description 2.1 Definition of Markov Model There are four common Markov models used in different si..
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Relationship between MLE and MAPStats 2019. 10. 4. 20:33
1. Overview Both Maximum Likelihood Estimation (MLE) and Maximum A Posterior (MAP) are used to estimate parameters for a distribution. MLE is also widely used to estimate the parameters for a Machine Learning model, including Naïve Bayes and Logistic regression. It is so common and popular that sometimes people use MLE even without knowing much of it. For example, when fitting a Normal distribut..
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Different Machine Learning Categories and AlgorithmsMLAI 2019. 9. 30. 22:17
1. Overview At a high-level, machine learning is simply the study of teaching a computer program or algorithm how to progressively improve upon a set task that it is given. On the research side of things, machine learning can be viewed through the lens of theoretical and mathematical modeling of how this process works. However, more practically it is the study of how to build applications that e..
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Conditional ProbabilityMath/Probability 2019. 9. 30. 21:25
1. Overview In probability theory, conditional probability is a measure of the probability of an event occurring given that another event has (by assumption, presumption, assertion or evidence) occurred. The concept of conditional probability is one of the most fundamental and one of the most important in probability theory. But conditional probabilities can be quite slippery and require careful..
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Convolutional Neural Networks(CNN)MLAI/DeepLearning 2019. 9. 30. 17:28
1. Overview In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. CNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The "fully-connectedness" of the..
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Dirty ReadDB/RDB 2019. 9. 30. 13:14
1. Overview Database transactions are defined by the four properties known as ACID. The Isolation Level (I in ACID) allows you to trade off data integrity for performance. The weaker the isolation level, the more anomalies can occur, and in this article, we are going to describe the Dirty Read phenomenon. 2. Description 2.1 Phenomenon As previously mentioned, all database changes are applied to ..