Learning Commons | Cardinal Cushing Library
Library Catalog - Books/Videos

Mastering Bootstrap 4 - Second Edition : Master the latest version of Bootstrap 4 to build highly customized responsive web apps, 2nd Edition. by Jakobus, Benjamin

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Ceramics for environmental and energy applications II : a collection of papers presented at the 10th Pacific Rim Conference on Ceramic and Glass Technology, June 2-6, 2013, Coronad...

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Python game programming by example : a pragmatic guide for developing your own games with Python / Pragmatic guide for developing your own games with Python by Rodas de Paz, Alejandro, Howse, Joseph

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Soul survivors : stories of wounded women warriors and the battles they fight long after they've left the war zone / Stories of wounded women warriors and the battles they fight lo... by Holmstedt, Kirsten A.

Table of Contents: “…Introduction; Trial and Error: The Early Stages of Healing: SPC Jen Elliott; Ain't No Mountain High Enough: Lt. Col. Kathy Champion; Give 'em Hell: Sgt. …”
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A promising problem : the new Chicana/o history

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Contented Cows Still Give Better Milk, Revised and Expanded : the Plain Truth about Employee Engagement and Your Bottom Line. by Catlette, Bill

Table of Contents: “…Part II: Contented Cows Are CommittedChapter 3: The "Vision Thing": Passengers or Crew; Burning Off the Fog; Making It Stick; "Moments of Truth"; High Expectations Beget High Performance; Chapter Summary; Chapter 4: The Path to Commitment; So How Do You Get People Committed?…”
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Mastering Bootstrap 4. by Benjamin Jakobus; Jason Marah

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Clowns to the left of me, jokers to the right : American life in columns by Smerconish, Michael A.

Table of Contents: “…-- It's Bunk -- Rebuild 'Em! -- Want Ratings? Bring Back the Beauty -- Conspiracy: The Okla. …”
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Foundations of Antenna Engineering: A Unified Approach for Line-of-Sight and Multipath.

Table of Contents: “…Foundations of Antenna Engineering: A Unified Approach for Line-of-Sight and Multipath; Contents; Preface; Acknowledgments; Chapter 1 Introduction; 1.1 Antenna Types and Classes; 1.2 Brief History of Antennas and Analysis Methods; 1.3 Terminology, Quantities, Units, and Symbols; 1.3.1 Radiation or Scattering; 1.3.2 Reflection, Refraction, and Diffraction; 1.3.3 Rays, Waves, Phase Fronts, and Phase Paths; 1.3.4 SI Units for Fields and Sources and Decibels; 1.3.5 Symbols; 1.4 Vector Notation and Coordinate Transformations; 1.4.1 Some Vector Formulas; 1.4.2 Coordinate Transformations.…”
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Forensic photography : a practitioner's guide by Marsh, Nick (Nicholas)

Table of Contents: “…Title Page; Copyright; Foreword; Preface; Acknowledgements; About the Companion Website; Chapter 1: Image Processing; 1.1 Introduction; 1.2 The digital image; 1.3 Image acquisition; 1.4 Colour images; 1.5 The imaging chain and workflow; 1.6 White balance; 1.7 Image histogram; 1.8 Image processing terminology; 1.9 Digital image processing operations; 1.10 Classes of operations; 1.11 Noise reduction; 1.12 Sharpening filters; 1.13 History log; 1.14 Layers; 1.15 Bit depth and dynamic range; 1.16 File formats; 1.17 Image compression; 1.18 Image processing at image capture.…”
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Mastering Java Machine Learning. by Kamath, Dr. Uday

Table of Contents: “…Case Study -- Horse Colic Classification -- Business problem -- Machine learning mapping -- Data analysis -- Label analysis -- Features analysis -- Supervised learning experiments -- Weka experiments -- RapidMiner experiments -- Results, observations, and analysis -- Summary -- References -- Chapter 3: Unsupervised Machine Learning Techniques -- Issues in common with supervised learning -- Issues specific to unsupervised learning -- Feature analysis and dimensionality reduction -- Notation -- Linear methods -- Principal component analysis (PCA) -- Random projections (RP) -- Multidimensional Scaling (MDS) -- Nonlinear methods -- Kernel Principal Component Analysis (KPCA) -- Manifold learning -- Clustering -- Clustering algorithms -- k-Means -- DBSCAN -- Mean shift -- Expectation maximization (EM) or Gaussian mixture modeling (GMM) -- Hierarchical clustering -- Self-organizing maps (SOM) -- Spectral clustering -- Affinity propagation -- Clustering validation and evaluation -- Internal evaluation measures -- External evaluation measures -- Outlier or anomaly detection -- Outlier algorithms -- Statistical-based -- Distance-based methods -- Density-based methods -- Clustering-based methods -- High-dimensional-based methods -- One-class SVM -- Outlier evaluation techniques -- Supervised evaluation -- Unsupervised evaluation -- Real-world case study -- Tools and software -- Business problem -- Machine learning mapping -- Data collection -- Data quality analysis -- Data sampling and transformation -- Feature analysis and dimensionality reduction -- Observations on feature analysis and dimensionality reduction -- Clustering models, results, and evaluation -- Observations and clustering analysis -- Outlier models, results, and evaluation -- Summary -- References -- Chapter 4: Semi-Supervised and Active Learning -- Semi-supervised learning.…”
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