The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematica...

Managing Information Security offers focused coverage of how to protect mission critical systems, and how to deploy security management systems, IT security, ID management, intrusion detection and prevention systems, computer forensics, network forensics, firewalls, penetration testing, vulnerability assessment, and more. It offers in-depth coverage of the current technology and practice as it relates to information security management solutions. Individual chapters are authored by leading ex...

Developers, take note: databases aren't just for the IS group any more. You can build database-backed applications for the desktop, Web, embedded systems, or operating systems without linking to heavy-duty client-server databases such as Oracle and MySQL. This book shows how you to use SQLite, a small and lightweight database that you can build right into your application during development. Applications that handle data have an enormous advantage today, and with SQLite, you'll discover how t...