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  • Sách/Book


  • Authors: Wu, Yulei (2023)

  • AI and Machine Learning for Network and Security Management covers a range of key topics of network automation for network and security management, including resource allocation and scheduling, network planning and routing, encrypted traffic classification, anomaly detection, and security operations. In addition, the authors introduce their large-scale intelligent network management and operation system and elaborate on how the aforementioned areas can be integrated into this system, plus how the network service can benefit.

  • Sách/Book


  • Authors: - (2023)

  • This book allows readers to reach their solutions using various predictive algorithm-based approaches and some curated real-time protective examples that are defined herein. Artificial intelligence (AI) concepts are devised and proposed for helping readers understand the core concepts of efficiencies of threats, and the parallel solutions are covered. The chapters also state the challenges in privacy and security levels for various algorithms and various techniques and tools are proposed for each challenge.

  • Sách/Book


  • Authors: - (2023)

  • This book thoroughly explains deep learning models and how to use Python programming to implement them in applications such as NLP, face detection, face recognition, face analysis, and virtual assistance (chatbot, machine translation, etc.). It provides hands-on guidance in using Python for implementing deep learning application models. It also identifies future research directions for deep learning

  • Sách/Book


  • Authors: Jago, Maxim (2019)

  • The 18 project-based lessons in this book show users step-by-step the key techniques for working in Premiere Pro. Learn to edit video in Adobe Premiere Pro CC 2019 and get the most out of your workflow.

  • Sách/Book


  • Authors: Tanenbaum, Andrew S (2024)

  • The 5th Edition keeps pace with modern OS with a new chapter on Windows 11, new security coverage, an emphasis on flash-based solid-state drives and more.

  • Sách/Book


  • Authors: - (2023)

  • This book discusses in a practical manner some of the critical security challenges facing the ever-evolving networking technologies of today. In an age of explosive worldwide growth of electronic data storage and communications, effective protection of information has become a critical requirement, especially when used in coordination with other tools for information security and cryptography in all of its applications, including data confidentiality, data integrity, and user authentication

  • Sách/Book


  • Authors: Bell, Michael (2023)

  • A strategic state-of-the-art software architecture manual for all skill levels In Software Architect, veteran enterprise and solution architect Michael Bell delivers a hands-on playbook of best practices for aspiring and practicing software architects, seeking to improve their software design, integration, communication, presentation, and knowledge acquisition skills

  • Sách/Book


  • Authors: - (2021)

  • Artificial Intelligence in Data Mining: Theories and Applications offers a comprehensive introduction to data mining theories, relevant AI techniques, and their many real-world applications. This book is written by experienced engineers for engineers, biomedical engineers, and researchers in neural networks, as well as computer scientists with an interest in the area.

  • Sách/Book


  • Authors: Beaulieu, Alan (2024)

  • To help you on the path to becoming a Snowflake pro, this concise yet comprehensive guide reviews fundamentals and best practices for Snowflake's SQL and Scripting languages. Developers and data professionals will learn how to generate, modify, and query data in the Snowflake relational database management system as well as how to apply analytic functions for reporting.

  • Sách/Book


  • 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.