Adversarial Learning and Secure AI
ኦገስ 2023 · Cambridge University Press
ኢ-መጽሐፍ
376
ገጾች
reportየተሰጡት ደረጃዎች እና ግምገማዎች የተረጋገጡ አይደሉም የበለጠ ለመረዳት
ስለዚህ ኢ-መጽሐፍ
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.
ስለደራሲው
David J. Miller is Professor of Electrical Engineering at the Pennsylvania State University.
Zhen Xiang is a post-doctoral research associate in Computer Science at the University of Illinois, Urbana-Champaign.
George Kesidis is Professor of Computer Science and Engineering, and of Electrical Engineering, at the Pennsylvania State University.
ለዚህ ኢ-መጽሐፍ ደረጃ ይስጡ
ምን እንደሚያስቡ ይንገሩን።
የንባብ መረጃ
ዘመናዊ ስልኮች እና ጡባዊዎች
የGoogle Play መጽሐፍት መተግበሪያውን ለAndroid እና iPad/iPhone ያውርዱ። ከእርስዎ መለያ ጋር በራስሰር ይመሳሰላል እና ባሉበት የትም ቦታ በመስመር ላይ እና ከመስመር ውጭ እንዲያነቡ ያስችልዎታል።
ላፕቶፖች እና ኮምፒውተሮች
የኮምፒውተርዎን ድር አሳሽ ተጠቅመው በGoogle Play ላይ የተገዙ ኦዲዮ መጽሐፍትን ማዳመጥ ይችላሉ።
ኢሪደሮች እና ሌሎች መሳሪያዎች
እንደ Kobo ኢ-አንባቢዎች ባሉ ኢ-ቀለም መሣሪያዎች ላይ ለማንበብ ፋይል አውርደው ወደ መሣሪያዎ ማስተላለፍ ይኖርብዎታል። ፋይሎቹን ወደሚደገፉ ኢ-አንባቢዎች ለማስተላለፍ ዝርዝር የእገዛ ማዕከል መመሪያዎቹን ይከተሉ።
