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  • 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
  • Sách/Book

  • Python data science handbook : essential tools for working with data

    • Authors: Vanderplas, Jacob T (2023)

  • Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, pandas, Matplotlib, scikit-learn, and other related tools
  • Sách/Book

  • Frontiers of Quality Electronic Design (QED): AI, IoT and Hardware Security

    • Authors: Ali Iranmanesh (2023)

  • This book explores the latest trends in several key topics related to quality electronic design, with emphasis on Hardware Security, Cybersecurity, Machine Learning, and application of Artificial Intelligence (AI). The book includes topics in nonvolatile memories (NVM), Internet of Things (IoT), FPGA, and Neural Networks.
  • Sách/Book

  • Practical data privacy : enhancing privacy and security in data

    • Authors: Jarmul, Katharine (2023)

  • Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems.
  • 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
  • Sách/Book

  • Fundamentals of Logic and Computation: With Practical Automated Reasoning and Verification

    • Authors: Hou, Zhe (2021)

  • This textbook aims to help the reader develop an in-depth understanding of logical reasoning and gain knowledge of the theory of computation. The book combines theoretical teaching and practical exercises; the latter is realised in Isabelle/HOL, a modern theorem prover, and PAT, an industry-scale model checker. I also give entry-level tutorials on the two software to help the reader get started.
  • Sách/Book

  • Embedded Analytics: Integrating Analysis With the Business Workflow

    • Authors: Farmer, Donald (2023)

  • This book explores the most important techniques for taking that adoption further: embedding analytics into the workflow of our everyday operations. Author Donald Farmer, principal of TreeHive Strategy, shows business users how to improve decision-making without becoming analytic specialists.
  • Sách/Book

  • Learning Ray : flexible distributed Python for machine learning

    • Authors: Pumperla, Max (2023)

  • Get started with Ray, the open source distributed computer framework that simplifies the process of scaling comute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin with compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale.