Adversarial Learning Secure by Miller David (12 results)

Paperback. Condition: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

hardcover. Condition: Very Good. Cover and edges may have some wear.

Hardcover. Condition: Like New. First Edition. A firm and square hardback with strong joints and sharp corners, just showing a few very minor cosmetic rubs. Hence a non-text page has a small 'damaged' stamp. Despite such this book is actually in nearly new condition and appears unread. Thus the contents are crisp, fresh and tight; no pen-marks. Now offered for sale at a very sensible price.…

Condition: As New. Unread book in perfect condition.

Encuadernación de tapa dura. Condition: Aceptable. Dust Jacket Condition: Aceptable. HOJAS SUELTAS, SELLO EN INTERIOR.

Condition: New.

Hardcover. Condition: new. New Copy. Customer Service Guaranteed.

Condition: As New. Unread book in perfect condition.

Condition: New.

Buch. Condition: Neu. Neuware - Providing a logical framework for student learning, this is the first textbook on adversarial learning. It introduces vulnerabilities of deep learning, then demonstrates methods for defending against attacks and making AI generally more robust. To help students connect theory with practice, it explains and evaluates attack-and-defense scenarios alongside real-world examples. Feasible, hands-on student projects, which increase in difficulty throughout the book, give students practical experience and help to improve their Python and PyTorch skills. Book chapters conclude with questions that can be used for classroom discussions. In addition to deep neural networks, students will also learn about logistic regression, naïve Bayes classifiers, and support vector machines. Written for senior undergraduate and first-year graduate courses, the book offers a window into research methods and current challenges. Online resources include lecture slides and image files for instructors, and software for early course projects for students.…

Hardcover. Condition: new. Hardcover. Providing a logical framework for student learning, this is the first textbook on adversarial learning. It introduces vulnerabilities of deep learning, then demonstrates methods for defending against attacks and making AI generally more robust. To help students connect theory with practice, it explains and evaluates attack-and-defense scenarios alongside real-world examples. Feasible, hands-on student projects, which increase in difficulty throughout the book, give students practical experience and help to improve their Python and PyTorch skills. Book chapters conclude with questions that can be used for classroom discussions. In addition to deep neural networks, students will also learn about logistic regression, naive Bayes classifiers, and support vector machines. Written for senior undergraduate and first-year graduate courses, the book offers a window into research methods and current challenges. Online resources include lecture slides and image files for instructors, and software for early course projects for students. Designed for upper undergraduate and graduate courses on adversarial learning and AI security, this textbook connects theory with practice using real-world examples, case studies, and hands-on student projects. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Condition: new. Hardcover. Providing a logical framework for student learning, this is the first textbook on adversarial learning. It introduces vulnerabilities of deep learning, then demonstrates methods for defending against attacks and making AI generally more robust. To help students connect theory with practice, it explains and evaluates attack-and-defense scenarios alongside real-world examples. Feasible, hands-on student projects, which increase in difficulty throughout the book, give students practical experience and help to improve their Python and PyTorch skills. Book chapters conclude with questions that can be used for classroom discussions. In addition to deep neural networks, students will also learn about logistic regression, naive Bayes classifiers, and support vector machines. Written for senior undergraduate and first-year graduate courses, the book offers a window into research methods and current challenges. Online resources include lecture slides and image files for instructors, and software for early course projects for students. Designed for upper undergraduate and graduate courses on adversarial learning and AI security, this textbook connects theory with practice using real-world examples, case studies, and hands-on student projects. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…