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DSPA Chapter 22 Deep Learning

Chapter 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 on August 30th, 2017 0 likes 79 plays 0  

DSPA Chapter 21 Function Optimization

Chapter 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 on August 30th, 2017 0 likes 22 plays 0  

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 on July 17th, 2017 0 likes 18 plays 0  

DSPA Chapter 4 Linear Algebra and Matrix Computing

DSPA 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 transformations…

From  Tina Chang on July 17th, 2017 0 likes 66 plays 0  

DSPA Chapter 15 Specialized ML Techniques

DSPA 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 on July 17th, 2017 1 likes 13 plays 0  

DSPA Chapter 12: k-Means Clustering

DSPA 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 on July 17th, 2017 0 likes 35 plays 0  

DSPA Chapter 13 Model Evaluation

DSPA Chapter 13 Model Evaluation In this chapter, we will discuss (1) various evaluation strategies for prediction, clustering, classification, regression, and decision trees, (2) visualization of…

From  Tina Chang on July 17th, 2017 0 likes 14 plays 0  

DSPA Chapter 14 Improvement of Model Performance

DSPA 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 on July 17th, 2017 0 likes 16 plays 0  

DSPA Chapter 8 Decision Tree Classification

DSPA 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 on July 17th, 2017 0 likes 42 plays 0  

DSPA Chapter 5 Dimensionality Reduction

DSPA Chapter 5 Dimensionality Reduction Dimensionality reduction techniques enable exploratory data analyses by reducing the complexity of the dataset, still approximately preserving important…

From  Tina Chang on July 17th, 2017 0 likes 67 plays 0  

DSPA Chapter 10: SVM Classification

DSPA 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, efficient…

From  Tina Chang on July 17th, 2017 0 likes 37 plays 0  

DSPA Chapter 3 Data Visualization

DSPA 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 on July 17th, 2017 0 likes 111 plays 0  

DSPA Chapter 9: Regression Classification

DSPA 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 on July 17th, 2017 0 likes 44 plays 0  

DSPA Chapter 7 Naive Bayes Classification

DSPA 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 on July 17th, 2017 0 likes 37 plays 0