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

Machine learning and statistical approaches to image retrieval / by Chen, Yixin, 1972-

Table of Contents: “…Preface -- Introduction -- Image Retrieval and Linguistic Indexing -- Machine Learning and Statistical Modeling -- A Robust Region-Based Similarity Measure -- Cluster-Based Retrieval by Unsupervised Learning -- Categorization by Learning and Reasoning with Regions -- Automatic Linguistic Indexing of Pictures -- Modeling Ancient Paintings -- Conclusions and Future Work -- References -- Index.…”
Full text (Emmanuel users only)

Hands-On Computer Vision with TensorFlow 2 : Leverage Deep Learning to Create Powerful Image Processing Apps with TensorFlow 2. 0 and Keras. by Planche, Benjamin

Table of Contents: “…Scene reconstructionA brief history of computer vision; First steps to first successes; Underestimating the perception task; Hand-crafting local features; Adding some machine learning on top; Rise of deep learning; Early attempts and failures; Rise and fall of the perceptron; Too heavy to scale; Reasons for a comeback; The internet -- the new El Dorado of data science; More power than ever; Deep learning or the rebranding of artificial neural networks; What makes learning deep?…”
Full text (Emmanuel users only)

Practical big data analytics : hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R / by Dasgupta, Nataraj

Subjects: Full text (Emmanuel users only)

Applied Supervised Learning with R : Use Machine Learning Libraries of R to Build Models That Solve Business Problems and Predict Future Trends. by Ramasubramanian, Karthik

Subjects: “…Machine learning.…”
Full text (Emmanuel users only)

Hands-On Machine Learning on Google Cloud Platform : Implementing smart and efficient analytics using Cloud ML Engine. by Ciaburro, Giuseppe

Table of Contents: “…Chapter 5: Transforming Your DataHow to clean and prepare the data; Google Cloud Dataprep; Exploring Dataprep console; Removing empty cells; Replacing incorrect values; Mismatched values; Finding outliers in the data; Visual functionality; Statistical information; Removing outliers; Run Job; Scale of features; Min-max normalization; z score standardization; Google Cloud Dataflow; Summary; Chapter 6: Essential Machine Learning; Applications of machine learning; Financial services; Retail industry; Telecom industry; Supervised and unsupervised machine learning.…”
Full text (Emmanuel users only)

Boosting : foundations and algorithms by Schapire, Robert E.

Table of Contents: “…Foundations of machine learning -- Using AdaBoost to minimize training error -- Direct bounds on the generalization error -- The margins explanation for boosting's effectiveness -- Game theory, online learning, and boosting -- Loss minimization and generalizations of boosting -- Boosting, convex optimization, and information geometry -- Using confidence-rated weak predictions -- Multiclass classification problems -- Learning to rank -- Attaining the best possible accuracy -- Optimally efficient boosting -- Boosting in continuous time.…”
Full text (Emmanuel users only)

Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5 / by Pastor Sanz, Iván

Table of Contents: “…Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: R Fundamentals for Machine Learning; R and RStudio installation; Things to know about R; Using RStudio; RStudio installation ; Some basic commands; Objects, special cases, and basic operators in R; Working with objects; Working with vectors; Vector indexing; Functions on vectors; Factor; Factor levels; Strings; String functions; Matrices; Representing matrices; Creating matrices; Accessing elements in a matrix; Matrix functions; Lists; Creating lists…”
Full text (Emmanuel users only)

Hands-on artificial intelligence on Amazon Web Services : decrease the time to market for AI and ML applications with the power of AWS by Tripuraneni, Subhashini, Song, Charles

Table of Contents: “…Section 1: Introduction and anatomy of a modern AI -- Chapter 1: Introduction to artificial intelligence on Amazon Web Services -- Chapter 2: Anatomy of a modern AI application -- Section 2: Building applications with AWS AI -- Chapter 3: Detecting and translating text with Amazon Rekognition -- Chapter 4: Performing speech-to-text and vice versa with Amazon transcribe and Polly -- Chapter 5: Extracting information from text with Amazon comprehend -- Chapter 6: Building a voice chatbot with Amazon Lex -- Section 3: Training machine learning models with Amazon SageMaker -- Chapter 7: Working with Amazon Sagemaker -- Chapter 8 : Creating machine learning inference pipelines -- Chapter 9 : Discovering topics in text collection -- Chapter 10: Classifying images using Amazon Sagemaker -- Chapter 11: Sales forecasting with Deep Learning and Auto Regression -- Section 4: Machine learning model monitoring and governance -- Chapter 12: Model accuracy degradation and feedback loops.…”
Full text (Emmanuel users only)

Deep learning : principios y fundamentos by Bosch Rué, Anna, Casas-Roma, Jordi

Subjects: Full text (Emmanuel users only)

Content-based image classification : efficient machine learning using robust feature extraction techniques / by Das, Rik, 1978-

Subjects: Full text (Emmanuel users only)

PyTorch deep learning in 7 days

Subjects: “…Machine learning.…”
Full text (Emmanuel users only)

Deep learning. / Deep learning. by Lazy Programmer

Subjects: Full text (Emmanuel users only)

Codeless Deep Learning with KNIME Build, Train, and Deploy Various Deep Neural Network Architectures Using KNIME Analytics Platform. by Melcher, Kathrin

Subjects: Full text (Emmanuel users only)

Predictive medicine : artificial intelligence and its impact on health care business strategy by Fombu, Emmanuel

Table of Contents: “…Introduction -- Chapter 1: An Introduction to artificial intelligence -- Chapter 2: The dark side of AI -- Chapter 3: Machine learning in healthcare -- Chapter 4: natural language processing in healthcare -- Chapter 5: Robotics in healthcare -- Chapter 6: Data in healthcare -- Chapter 7: Artificial intelligence in healthcare -- Chapter 8: The future of artificial intelligence -- Chapter 9: Implementing an AI strategy Chapter 10: Predictive medicine -- Chapter 11: The Future -- Join the conversation -- About the author -- Index -- Adpage -- Backcover.…”
Full text (Emmanuel users only)

Bridging the gap between graph edit distance and kernel machines by Neuhaus, Michel

Subjects: Full text (Emmanuel users only)

Social media analytics for user behavior modeling : a task heterogeneity perspective / Task heterogeneity perspective by Nelakurthi, Arun Reddy, He, Jingrui

Subjects: “…Machine learning.…”
Full text (Emmanuel users only)

Artificial intelligence with Power BI : take your data analytics skills to the next level by leveraging the AI capabilities in Power BI by Diepeveen, Mary-Jo

Table of Contents: “…Table of Contents Introducing AI in Power BI Exploring Data in Power BI Data Preparation Forecasting Time-Series Data Detecting Anomalies in Your Data Using Power BI Using Natural Language to Explore Data with the Q&A Visual Using Cognitive Services Integrating Natural Language Understanding with Power BI Integrating an Interactive Question and Answering App into Power BI Getting Insights from Images with Computer Vision Using Automated Machine Learning with Azure and Power BI Training a Model with Azure Machine Learning Responsible AI.…”
Full text (Emmanuel users only)

Crash course statistics. / Unsupervised machine learning

Subjects: Full text (Emmanuel users only)

The Fraud Analyst : Humans vs. Machine Learning

Full text (Emmanuel users only)

Big Data and Machine Learning in Quantitative Investment by Guida, Tony

Table of Contents: “…; 1.1 Introduction; 1.2 Replication or Reinvention; 1.3 Reinvention with Machine Learning; 1.4 A Matter of Trust; 1.5 Economic Existentialism: A Grand Design or an Accident?…”
Full text (Emmanuel users only)

Intrusion Detection : a Machine Learning Approach. by Tsai, Jeffrey J. P.

Table of Contents: “…Countermeasures of Attacks; Chapter 3 Machine Learning Methods; 3.1. Background; 3.2. Concept Learning; 3.3. …”
Full text (Emmanuel users only)

Practical Convolutional Neural Networks : Implement advanced deep learning models using Python by Karim, Md. Rezaul

Subjects: Full text (Emmanuel users only)

Hands-On Java Deep Learning for Computer Vision : Implement Machine Learning and Neural Network Methodologies to Perform Computer Vision-Related Tasks. by Ramo, Klevis

Subjects: Full text (Emmanuel users only)

SN Video coding and web development. / Build an emoting robot using Arduino and machine learning

Subjects: Full text (Emmanuel users only)

Dictionary learning in visual computing by Zhang, Qiang (Computer scientist), Li, Baoxin

Subjects: Full text (Emmanuel users only)

Learning OWL class expressions by Lehmann, Jens, 1982-

Subjects: Full text (Emmanuel users only)

Agile Machine Learning with DataRobot Automate Each Step of the Machine Learning Life Cycle, from Understanding Problems to Delivering Value. by Chadha, Bipin

Subjects: “…Machine learning.…”
Full text (Emmanuel users only)

Concept Data Analysis. by Carpineto, Claudio

Subjects: Full text (Emmanuel users only)

Personalized deeper learning : blueprints for teaching complex cognitive, social-emotional, and digital skills by Bellanca, James A., 1937-

Subjects: Full text (Emmanuel users only)

Support vector machine in chemistry

Table of Contents: “…Underfitting and overfitting: problems of machine learning. 1.4. Theory of overfitting and underfitting control, ERM and SRM principles of statistical learning theory. 1.5. …”
Full text (Emmanuel users only)

Deep learning technologies for social impact by Benedict, Shajulin

Table of Contents: “…Technology -- blockchain -- 2.6. AI/machine learning/deep learning techniques -- 2.7. The Internet of things/sensor technology -- 2.8. …”
Full text (Emmanuel users only)

Natural Language Processing and Computational Linguistics : a Practical Guide to Text Analysis with Python, Gensim, SpaCy, and Keras. by Srinivasa-Desikan, Bhargav

Subjects: Full text (Emmanuel users only)

Data Mining and Machine Learning in Building Energy Analysis. by Fr©¡a℗Œd©¡a℗Œric Magoules

Subjects: Full text (Emmanuel users only)

Intelligence emerging : adaptivity and search in evolving neural systems by Downing, Keith L.

Subjects: Full text (Emmanuel users only)

Machine learning for computer and cyber security : principles, algorithms, and practices /

Table of Contents: “…A Deep Learning-based System for Network Cyber Threat Detection -- 2. Machine Learning for Phishing Detection and Mitigation -- 3. …”
Full text (Emmanuel users only)

Deep learning with Microsoft Cognitive Toolkit quick start guide : a practical guide to building neural networks using Microsoft's open source deep learning framework by Meints, Willem

Table of Contents: “…Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Chapter1: Getting Started with CNTK; The relationship between AI, machine learning, and deep learning; Limitations of machine learning; How does deep learning work?…”
Full text (Emmanuel users only)

Deep reinforcement learning hands-on : apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more by Lapan, Maxim

Subjects: Full text (Emmanuel users only)

Getting started with machine learning in R /

Subjects: Full text (Emmanuel users only)

Getting Started with Machine Learning in R / by Rennert, Phil

Full text (Emmanuel users only)

Section 1: Machine Learning Techniques in R (excluded).

Full text (Emmanuel users only)