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

Machine learning : hands-on for developers and technical professionals / by Bell, Jason (Computer scientist)

Table of Contents: “…What is machine learning? -- Planning machine learning -- Working with decision trees -- Bayesian networks -- Artificial neural networks -- Association rules learning -- Support vector machines -- Clustering -- Machine learning in real time with Spring XD -- Maching learning as a batch process -- Apache Spark -- Machine learning with R.…”
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Machine learning : hands-on for developers and technical professionals / by Bell, Jason (Computer scientist)

Table of Contents: “…What is machine learning? -- Planning for machine learning -- Data acquisition techniques -- Statistics, linear regression, and randomness -- Working with decision trees -- Clustering -- Association rules learning -- Support vector machines -- Artificial neural networks -- Machine learning with text documents -- Machine learning with images -- Machine learning streaming with Kafka -- Apache Spark -- Machine learning with R.…”
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Machine learning : a Bayesian and optimization perspective / by Theodoridis, Sergios, 1951-

Subjects: “…Machine learning.…”
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Machine Learning Algorithms : Popular Algorithms for Data Science and Machine Learning, 2nd Edition. by Bonaccorso, Giuseppe

Table of Contents: “…Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: A Gentle Introduction to Machine Learning; Introduction -- classic and adaptive machines; Descriptive analysis; Predictive analysis; Only learning matters; Supervised learning; Unsupervised learning; Semi-supervised learning; Reinforcement learning; Computational neuroscience; Beyond machine learning -- deep learning and bio-inspired adaptive systems; Machine learning and big data; Summary; Chapter 2: Important Elements in Machine Learning; Data formats; Multiclass strategies.…”
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Mastering Machine Learning Algorithms : Expert techniques to implement popular machine learning algorithms and fine-tune your models. by Bonaccorso c/o Quandoo, Giuseppe

Table of Contents: “…Cover; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Machine Learning Model Fundamentals; Models and data; Zero-centering and whitening; Training and validation sets; Cross-validation; Features of a machine learning model; Capacity of a model; Vapnik-Chervonenkis capacity; Bias of an estimator; Underfitting; Variance of an estimator; Overfitting; The Cramér-Rao bound; Loss and cost functions; Examples of cost functions; Mean squared error; Huber cost function; Hinge cost function; Categorical cross-entropy; Regularization; Ridge; Lasso.…”
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How Can We Use Machine Learning in the Search for Exoplanets?.

Subjects: “…Machine learning.…”
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Ensemble machine learning techniques /

Subjects: “…Machine learning.…”
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Deep Learning for Beginners A Beginner's Guide to Getting up and Running with Deep Learning from Scratch Using Python. by Rivas Perea, Pablo, 1980-

Table of Contents: “…Cover -- Title Page -- Copyright and Credits -- About Packt -- Foreword -- Contributors -- Table of Contents -- Preface -- Section 1: Getting Up to Speed -- Chapter 1: Introduction to Machine Learning -- Diving into the ML ecosystem -- Training ML algorithms from data -- Introducing deep learning -- The model of a neuron -- The perceptron learning algorithm -- Shallow networks -- The input-to-hidden layer -- The hidden-to-hidden layer -- The hidden-to-output layer -- Deep networks -- Why is deep learning important today? …”
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The the TensorFlow Workshop A Hands-On Guide to Building Deep Learning Models from Scratch Using Real-world Datasets. by Moocarme, Matthew

Table of Contents: “…Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Machine Learning with TensorFlow -- Introduction -- Implementing Artificial Neural Networks in TensorFlow -- Advantages of TensorFlow -- Disadvantages of TensorFlow -- The TensorFlow Library in Python -- Exercise 1.01: Verifying Your Version of TensorFlow -- Introduction to Tensors -- Scalars, Vectors, Matrices, and Tensors -- Exercise 1.02: Creating Scalars, Vectors, Matrices, and Tensors in TensorFlow -- Tensor Addition -- Exercise 1.03: Performing Tensor Addition in TensorFlow…”
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Mastering Machine Learning with Spark 2.x. by Tellez, Alex

Table of Contents: “…Cover ; Copyright; Credits; About the Authors; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Introduction to Large-Scale Machine Learning and Spark; Data science; The sexiest role of the 21st century -- data scientist?…”
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Learning and reasoning in hybrid structured spaces by Morettin, Paolo

Subjects: “…Machine learning.…”
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Machine Learning Algorithms. by Bonaccorso, Giuseppe

Table of Contents: “…Cover -- Copyright -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: A Gentle Introduction to Machine Learning -- Introduction -- classic and adaptive machines -- Only learning matters -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Beyond machine learning -- deep learning and bio-inspired adaptive systems -- Machine learning and big data -- Further reading -- Summary -- Chapter 2: Important Elements in Machine Learning -- Data formats -- Multiclass strategies -- One-vs-all -- One-vs-one -- Learnability -- Underfitting and overfitting -- Error measures -- PAC learning -- Statistical learning approaches -- MAP learning -- Maximum-likelihood learning -- Elements of information theory -- References -- Summary -- Chapter 3: Feature Selection and Feature Engineering -- scikit-learn toy datasets -- Creating training and test sets -- Managing categorical data -- Managing missing features -- Data scaling and normalization -- Feature selection and filtering -- Principal component analysis -- Non-negative matrix factorization -- Sparse PCA -- Kernel PCA -- Atom extraction and dictionary learning -- References -- Summary -- Chapter 4: Linear Regression -- Linear models -- A bidimensional example -- Linear regression with scikit-learn and higher dimensionality -- Regressor analytic expression -- Ridge, Lasso, and ElasticNet -- Robust regression with random sample consensus -- Polynomial regression -- Isotonic regression -- References -- Summary -- Chapter 5: Logistic Regression -- Linear classification -- Logistic regression -- Implementation and optimizations -- Stochastic gradient descent algorithms -- Finding the optimal hyperparameters through grid search -- Classification metrics -- ROC curve -- Summary -- Chapter 6: Naive Bayes.…”
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Deep Learning By Example : a hands-on guide to implementing advanced machine learning algorithms and neural networks. by Menshawy, Ahmed

Table of Contents: “…Cover; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Data Science -- A Birds' Eye View; Understanding data science by an example; Design procedure of data science algorithms; Data pre-processing; Data cleaning; Data pre-processing; Feature selection; Model selection; Learning process; Evaluating your model; Getting to learn; Challenges of learning; Feature extraction â#x80;#x93; feature engineering; Noise; Overfitting; Selection of a machine learning algorithm; Prior knowledge; Missing values; Implementing the fish recognition/detection model.…”
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Machine Learning Applications in Electromagnetics and Antenna Array Processing by Martínez-Ramón, Manel

Table of Contents: “…7.5.1 Kernel Array Processors with Temporal Reference -- 7.5.2 Kernel Array Processor with Spatial Reference -- 7.6 RBF NN Beamformer -- 7.7 Hybrid Beamforming with Q-Learning -- References -- 8 Computational Electromagnetics -- 8.1 Introduction -- 8.2 Finite-Difference Time Domain -- 8.2.1 Deep Learning Approach -- 8.3 Finite-Difference Frequency Domain -- 8.3.1 Deep Learning Approach -- 8.4 Finite Element Method -- 8.4.1 Deep Learning Approach -- 8.5 Inverse Scattering -- 8.5.1 Nonlinear Electromagnetic Inverse Scattering Using DeepNIS -- References -- 9 Reconfigurable Antennas and Cognitive Radio -- 9.1 Introduction -- 9.2 Basic Cognitive Radio Architecture -- 9.3 Reconfiguration Mechanisms in Reconfigurable Antennas -- 9.4 Examples -- 9.4.1 Reconfigurable Fractal Antennas -- 9.4.2 Pattern Reconfigurable Microstrip Antenna -- 9.4.3 Star Reconfigurable Antenna -- 9.4.4 Reconfigurable Wideband Antenna -- 9.4.5 Frequency Reconfigurable Antenna -- 9.5 Machine Learning Implementation on Hardware -- 9.6 Conclusion -- References -- About the Authors -- Index.…”
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Contributions to Machine Learning and Psychometrics : Computational, Graphical, and Statistical Methods for Assessing Stability. by Philipp, Michel

Subjects: “…Machine learning.…”
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Information Discovery with Machine Intelligence for Language by He, Wu

Subjects: “…Machine learning.…”
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Machine learning in non-stationary environments : introduction to covariate shift adaptation / by Sugiyama, Masashi, 1974-

Table of Contents: “…Foreword; Preface; I INTRODUCTION; 1 Introduction and Problem Formulation; 1.1 Machine Learning under Covariate Shift; 1.2 Quick Tour of Covariate Shift Adaptation; 1.3 Problem Formulation; 1.4 Structure of This Book; II LEARNING UNDER COVARIATE SHIFT; 2 Function Approximation; 2.1 Importance-Weighting Techniques for Covariate Shift Adaptation; 2.2 Examples of Importance-Weighted Regression Methods; 2.3 Examples of Importance-Weighted Classification Methods; 2.4 Numerical Examples; 2.5 Summary and Discussion; 3 Model Selection; 3.1 Importance-Weighted Akaike Information Criterion.…”
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Practical Machine Learning. by Gollapudi, Sunila

Subjects: “…Machine learning.…”
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Machine learning engineering with MLflow manage the end-to-end machine learning lifecycle with MLflow / by Lauchande, Natu

Table of Contents: “…-- Getting started with MLflow -- Developing your first model with MLflow -- Exploring MLflow modules -- Exploring MLflow projects -- Exploring MLflow tracking -- Exploring MLflow Models -- Exploring MLflow Model Registry -- Summary -- Further reading -- Chapter 2: Your Machine Learning Project -- Technical requirements -- Exploring the machine learning process -- Framing the machine learning problem…”
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Deep Learning with TensorFlow. by Zaccone, Giancarlo

Table of Contents: “…Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Getting Started with Deep Learning; Introducing machine learning; Supervised learning; Unsupervised learning; Reinforcement learning; What is deep learning?…”
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Machine Learning Quick Reference : Quick and Essential Machine Learning Hacks for Training Smart Data Models. by Kumar, Rahul

Table of Contents: “…Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Quantifying Learning Algorithms; Statistical models; Learning curve; Machine learning; Wright's model; Curve fitting; Residual; Statistical modeling -- the two cultures of Leo Breiman; Training data development data -- test data; Size of the training, development, and test set; Bias-variance trade off; Regularization; Ridge regression (L2); Least absolute shrinkage and selection operator ; Cross-validation and model selection; K-fold cross-validation…”
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Mastering Machine Learning with R - Second Edition. by Lesmeister, Cory

Table of Contents: “…Cover; Credits; About the Author; About the Reviewers; Packt Upsell; Customer Feedback; Table of Contents; Preface; Chapter 1: A Process for Success; The process; Business understanding; Identifying the business objective; Assessing the situation; Determining the analytical goals; Producing a project plan; Data understanding; Data preparation; Modeling; Evaluation; Deployment; Algorithm flowchart; Summary; Chapter 2: Linear Regression -- The Blocking and Tackling of Machine Learning; Univariate linear regression; Business understanding; Multivariate linear regression; Business understanding.…”
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Effective Amazon Machine Learning. by Perrier, Alexis

Table of Contents: “…Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Introduction to Machine Learning and Predictive Analytics; Introducing Amazon Machine Learning; Machine Learning as a Service; Leveraging full AWS integration; Comparing performances; Engineering data versus model variety; Amazon's expertise and the gradient descent algorithm; Pricing; Understanding predictive analytics; Building the simplest predictive analytics algorithm; Regression versus classification.…”
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Optimal learning by Powell, Warren B., 1955-

Subjects: “…Machine learning.…”
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Data intelligence and risk analytics : Industrial Management & Data Systems

Subjects: “…Machine learning.…”
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Dataset shift in machine learning /

Subjects: “…Machine learning.…”
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Test-Driven Machine Learning. by Bozonier, Justin

Subjects: “…Machine learning.…”
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Automated machine learning with AutoKeras : deep learning made accessible for everyone with just few lines of coding / by Sobrecueva, Luis

Table of Contents: “…Table of Contents Introduction to Automated Machine Learning Getting Started with AutoKeras Automating the Machine Learning Pipeline with AutoKeras Image Classification and Regression Using AutoKeras Text Classification and Regression Using AutoKeras Working with Structured Data Using AutoKeras Sentiment Analysis Using AutoKeras Topic Classification Using AutoKeras Working with Multi-Modal Data and Multi-Task Exporting and Visualizing the Models.…”
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Machine Learning for Future Wireless Communications by Luo, Fa-Long

Subjects: “…Machine learning.…”
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Machine learning for dummies / by Mueller, John, 1958-, Massaron, Luca

Table of Contents: “…Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Part 1 Introducing How Machines Learn -- Chapter 1 Getting the Real Story about AI -- Moving beyond the Hype -- Dreaming of Electric Sheep -- Understanding the history of AI and machine learning -- Exploring what machine learning can do for AI -- Considering the goals of machine learning -- Defining machine learning limits based on hardware -- Overcoming AI Fantasies.…”
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Hands-On Reinforcement Learning with Python : Master Reinforcement and Deep Reinforcement Learning Using OpenAI Gym and TensorFlow. by Ravichandiran, Sudharsan

Subjects: “…Machine learning.…”
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Machine Learning with Core ML : an IOS Developer's Guide to Implementing Machine Learning in Mobile Apps. by Newnham, Joshua

Table of Contents: “…Intro; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Machine Learning; What is machine learning?; A brief tour of ML algorithms; Netflix -- making recommendations ; Shadow draw -- real-time user guidance for freehand drawing; Shutterstock -- image search based on composition; iOS keyboard prediction -- next letter prediction; A typical ML workflow ; Summary; Chapter 2: Introduction to Apple Core ML; Difference between training and inference; Inference on the edge; A brief introduction to Core ML; Workflow.…”
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Towards a Theory for Designing Machine Learning Systems for Complex Decision Making Problems by Tofangchi, Schahin

Subjects: “…Machine learning.…”
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Mastering machine learning with scikit-learn : learn to implement and evaluate machine learning solutions with scikit-learn / by Hackeling, Gavin

Subjects: “…Machine learning.…”
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Machine Learning with Spark and Python : Essential Techniques for Predictive Analytics / by Bowles, Michael

Subjects: “…Machine learning.…”
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Deep Learning with Pytorch Lightning : Swiftly Build High-Performance Artificial Intelligence (AI) Models Using Python. by Sawarkar, Kunal

Subjects: “…Machine learning.…”
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AI and machine learning / by Rahman, Was, 1965-

Table of Contents: “…What artificial intelligence is, isn't and might become -- A long and troubled life story -- How artificial intelligence and machine learning work -- Transforming business and society -- The risks, consequences and dilemmas AI brings -- End of the beginning or beginning of the end?…”
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Hands-On Ensemble Learning with R : a Beginner's Guide to Combining the Power of Machine Learning Algorithms Using Ensemble Techniques. by Tattar, Prabhanjan Narayanachar

Table of Contents: “…Cover; Copyright; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Ensemble Techniques; Datasets; Hypothyroid; Waveform; German Credit; Iris; Pima Indians Diabetes; US Crime; Overseas visitors; Primary Biliary Cirrhosis; Multishapes; Board Stiffness; Statistical/machine learning models; Logistic regression model; Logistic regression for hypothyroid classification; Neural networks; Neural network for hypothyroid classification; Naïve Bayes classifier; Naïve Bayes for hypothyroid classification; Decision tree; Decision tree for hypothyroid classification.…”
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Learning with kernels : support vector machines, regularization, optimization, and beyond by Schölkopf, Bernhard

Subjects: “…Machine learning.…”
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Advanced Natural Language Processing with TensorFlow 2 Build Effective Real-World NLP Applications Using NER, RNNs, Seq2seq Models, Transformers, and More. by Bansal, Ashish

Subjects: “…Machine learning.…”
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