Search for tag: "mathematics"
DSPA Chapter 22 Deep LearningChapter 22 demonstrates the R deep learning package MXNetR and demonstrate state-of-the-art deep learning models utilizing CPU and GPU for fast training (learning) and testing (validation).
From Tina Chang
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DSPA Chapter 21 Function OptimizationChapter 21 covers (1) constrained and unconstrained optimization, (2) Lagrange multipliers, (3) linear, quadratic and (general) non-linear programming, and (4) data denoising.
From Tina Chang
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DSPA Chapter 17 (Regularized Linear Modeling and Controlled Variable Selection)DSPA Chapter 17 (Regularized Linear Modeling and Controlled Variable Selection) Classical techniques for choosing important covariates to include in a model of complex multivariate data relied on…
From Tina Chang
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DSPA Chapter 4 Linear Algebra and Matrix ComputingDSPA Chapter 4 Linear Algebra and Matrix Computing Linear algebra is a branch of mathematics that studies linear associations using vectors, vector-spaces, linear equations, linear…
From Tina Chang
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DSPA Chapter 15 Specialized ML TechniquesDSPA Chapter 15 Specialized ML Techniques In this chapter, we will discuss some technical details about data formats, streaming, optimization of computation, and distributed deployment of…
From Tina Chang
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DSPA Chapter 12: k-Means ClusteringDSPA Chapter 12: k-Means Clustering In this chapter, we will present (1) clustering as a machine learning task, (2) the silhouette plots for classification evaluation, (3) the k-Means clustering…
From Tina Chang
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DSPA Chapter 13 Model EvaluationDSPA Chapter 13 Model Evaluation In this chapter, we will discuss (1) various evaluation strategies for prediction, clustering, classification, regression, and decision trees, (2) visualization…
From Tina Chang
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DSPA Chapter 14 Improvement of Model PerformanceDSPA Chapter 14 Improvement of Model Performance We already explored several alternative machine learning (ML) methods for prediction, classification, clustering and outcome forecasting. In many…
From Tina Chang
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DSPA Chapter 8 Decision Tree ClassificationDSPA Chapter 8 Decision Tree Classification In this chapter, we will (1) see a simple motivational example of decision trees based on the Iris data, (2) describe decision-tree divide and conquer…
From Tina Chang
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DSPA Chapter 5 Dimensionality ReductionDSPA Chapter 5 Dimensionality Reduction Dimensionality reduction techniques enable exploratory data analyses by reducing the complexity of the dataset, still approximately preserving important…
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DSPA Chapter 10: SVM ClassificationDSPA Chapter 10 SVM Classification In this chapter, we are going to cover two very powerful machine-learning algorithms. These techniques have complex mathematical formulations, however,…
From Tina Chang
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DSPA Chapter 3 Data VisualizationDSPA Chapter 3 Data Visualization In this chapter, we use a broad range of simulations and hands-on activities to highlight some of the basic data visualization techniques using R. A brief…
From Tina Chang
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DSPA Chapter 9: Regression ClassificationDSPA Chapter 9: Regression Classification In previous chapters (6, 7, and 8), we covered some classification methods that use mathematical formalism to address everyday life prediction problems.…
From Tina Chang
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DSPA Chapter 7 Naive Bayes ClassificationDSPA Chapter 7 Naive Bayes Classification Please review the introduction to Chapter 6, where we described the types of machine learning methods and presented lazy classification for numerical…
From Tina Chang
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