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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).
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August 29th, 2017
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Chapter 21 covers (1) constrained and unconstrained
optimization, (2) Lagrange multipliers, (3) linear, quadratic and
(general) non-linear programming, and (4) data denoising.
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August 29th, 2017
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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…
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July 1st, 2017
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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…
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August 1st, 2017
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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…
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August 1st, 2017
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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…
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August 1st, 2017
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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…
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August 1st, 2017
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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…
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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…
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August 1st, 2017
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DSPA 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 Classification In this chapter, we are going to cover two very powerful
machine-learning algorithms. These techniques have complex mathematical
formulations, however, efficient…
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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…
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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.…
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August 1st, 2017
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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…
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August 1st, 2017
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