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    Browsing by Subject Machine learning

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    • TVS.005119_TT_Fei Hu, Xiali Hei - AI, Machine Learning and Deep Learning_ A Security Perspective-CRC Press (2023).pdf.jpg
    • Sách/Book

    • AI, machine learning and deep learning : a security perspective

      • Authors: Hu, Fei (2023)

    • Today Artificial Intelligence (AI) and Machine/Deep Learning (ML/DL) have become the hottest areas in the information technology. In our society, there are so many intelligent devices that rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms/tools have used in many Internet applications and electronic devices, they are also vulnerable to various attacks and threats. The AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary, and many other attacks/threats. Those attacks make the AI products dangerous to use
    • TVS.004622_TT_(Textbooks in Mathematics) Jeffrey Paul Wheeler - An Introduction to Optimization. With Applications in Machine Learning and Data Analyt.pdf.jpg
    • Sách/Book

    • An introduction to optimization with applications in machine learning and data analytics

      • Authors: Wheeler, Jeffrey Paul (2023)

    • The primary goal of this text is a practical one. Equipping students with the enough knowledge and creating an independent research platform, the author strives to prepare students for professional careers. Providing students with a marketable skill set requires topics from many areas of optimization. The initial goal of this text is to develop marketable skill set for mathematics majors but also for students of engineering, computer science, economics, statistics, and business
    • TVS.003947_(Adaptive Computation and Machine Learning series) Ethem Alpaydin - Introduction to Machine Learning-The MIT Press (2014)-1.pdf.jpg
    • Book

    • Introduction to machine learning

      • Authors: Ethem Alpaydin (2014)

    • Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods.
    • TVS.006015_TT_Marc Loy, Patrick Niemeyer, and Daniel Leuck - Learning Java, 6th Edition (Third Early Release)-O_Reilly Media, Inc. (2023).pdf.jpg
    • Sách/Book

    • Learning Java: An Introduction to Real-World Programming with Java

      • Authors: Loy, Marc (2023)

    • This guide helps you: Learn the structure of the Java language and Java applications Write, compile, and execute Java applications Understand the basics of Java threading and concurrent programming Learn Java I/O basics, including local files and network resources Create compelling interfaces with an eye toward usability Learn how functional features have been integrated in Java Keep up with Java developments as new versions are released
    • TVS.005253_TT_(Wiley Finance) Ignacio Ruiz_ M. Zeron - Machine Learning for Risk Calculations_ A Practitioner_s View-Wiley (2022).pdf.jpg
    • Sách/Book

    • Machine learning for risk calculations : a practitioner's view

      • Authors: Ruiz, Ignacio (2022)

    • "The computational demand of risk calculations in financial institutions has ballooned. Traditionally, this has led to the acquisition of more and more computer power -- some banks have farms in the order of 50,000 CPUs, with running costs in the multimillions of dollars -- but this path is no longer economically or operationally viable. Algorithmic solutions represent a viable way to reduce costs while simultaneously increasing risk calculation capabilities
    • TVS.004946_TT_A. Mansurali, P. Mary Jeyanthi - Marketing Analytics_ A Machine Learning Approach-CRC Press_Apple Academic Press (2023).pdf.jpg
    • Sách/Book

    • Marketing analytics : a machine learning approach

      • Authors: Mansurali, A (2023)

    • With businesses becoming ever more competitive, marketing strategies need to be more precise and performance oriented. Companies are investing considerably in analytical infrastructure for marketing. This new volume, Marketing Analytics: A Machine Learning Approach, enlightens readers on the application of analytics in marketing and the process of analytics, providing a foundation on the concepts and algorithms of machine learning and statistics
    • TVS.005245_TT_Ram Shankar Siva Kumar_ Hyrum Anderson - Not with a Bug, But with a Sticker _ Attacks on Machine Learning Systems and What To Do About T.pdf.jpg
    • Sách/Book

    • Not with a Bug, but with a Sticker : attacks on machine learning systems and what to do about them

      • Authors: Siva Kumar, Ram Shankar (2023)

    • A robust and engaging account of the single greatest threat faced by AI and ML systems In Not With A Bug, But With A Sticker: Attacks on Machine Learning Systems and What To Do About Them, a team of distinguished adversarial machine learning researchers deliver a riveting account of the most significant risk to currently deployed artificial intelligence systems: cybersecurity threats.
    • TVS.005280_TT_Deepak Kanungo - Probabilistic Machine Learning for Finance and Investing (Sixth Early Release)-O_Reilly Media, Inc. (2023).pdf.jpg
    • Sách/Book

    • Probablistic machine learning for finance and investing : a primer to generative AI with Python

      • Authors: Kanungo, Deepak K (2023)

    • Unlike conventional AI, these systems are capable of warning us when their inferences and predictions are no longer useful in the current market environment. By moving away from flawed statistical methodologies and a restrictive conventional view of probability as a limiting frequency, you’ll move toward an intuitive view of probability as logic within an axiomatic statistical framework that comprehensively and successfully quantifies uncertainty. This book shows you how
    • TVS.000504_TT_ Adi Polak - Scaling Machine Learning with Spark_ Distributed ML with MLlib, TensorFlow, and PyTorch-O_Reilly Media (2023).pdf.jpg
    • Sách/Book

    • Scaling machine learning with Spark : distributed ML with MLlib, TensorFlow, and PyTorch

      • Authors: Polak, Adi (2023)

    • Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing data and ML practitioners to collaborate and understand each other better.