
Chapter 22 demonstrates the R deep learning package MXNetR and
demonstrate stateoftheart deep learning models utilizing CPU and GPU
for fast training (learning) and testing (validation).
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Chapter 21 covers (1) constrained and unconstrained
optimization, (2) Lagrange multipliers, (3) linear, quadratic and
(general) nonlinear programming, and (4) data denoising.
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Chapter 20 uses Google Flu Trends, Autism, and Parkinson’s
disease casestudies to illustrate (1) alternative forecasting types
using linear and nonlinear predictions, (2) exhaustive and…
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DSPA Chapter 19 Text Mining (TM) and Natural Language Processing (NLP)Natural Language Processing (NLP) and Text Mining (TM) refer to
automated machinedriven algorithms for semantically mapping,…
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DSPA Chapter 18 Big Longitudinal Data Analysis (Timeseries GEE GLMM SEM)The timevarying (longitudinal) characteristics of large information
flows represent a special case of the complexity, dynamic…
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DSPA Chapter 16 (Variable Selection) As we mentioned in Chapter 15,
variable selection is very important when dealing with bioinformatics,
healthcare, and biomedical data where we may have more…
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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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DSPA Chapter 4 Linear Algebra and Matrix Computing Linear algebra is a branch of mathematics that studies linear
associations using vectors, vectorspaces, linear equations, linear
transformations…
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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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DSPA Chapter 12: kMeans Clustering In this chapter, we will present (1) clustering as a machine learning task, (2) the silhouette plots for classification evaluation, (3) the kMeans
clustering…
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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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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 decisiontree divide
and conquer…
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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
machinelearning algorithms. These techniques have complex mathematical
formulations, however, efficient…
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DSPA Chapter 11 Apriori Association Rule Learning HTTP cookies are
used to track websurfing the Internet traffic. We often notice that
promotions (ads) on websites tend to match our needs, reveal…
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DSPA Chapter 3 Data Visualization In this chapter, we use a broad range of simulations and handson
activities to highlight some of the basic data visualization techniques
using R. A brief…
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DSPA Chapter 6: kNearest Neighbors Classification In the next several chapters we will concentrate of various
progressively advanced machine learning, classificaiton and clustering
techniques.…
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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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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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