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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).

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DSPA Chapter 20 Prediction and Internal Statistical Cross-validation

Chapter 20 uses Google Flu Trends, Autism, and Parkinson’s disease case-studies to illustrate (1) alternative forecasting types using linear and non-linear predictions, (2) exhaustive and…

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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…

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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…

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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…

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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,…

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DSPA Chapter 6: k-Nearest Neighbors Classification

DSPA Chapter 6: k-Nearest Neighbors Classification In the next several chapters we will concentrate of various progressively advanced machine learning, classificaiton and clustering techniques.…

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