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[null,"1.","Record Nr.",null,"UNINA9910402050903321"]
[null,null,"Titolo",null,"Country review Mali"]
[null,null,"Pubbl/distr/stampa",null,"Houston, TX, : Commercial Data International"]
[null,null,"Descrizione fisica",null,"1 online resource"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Periodico"]
[null,"2.","Record Nr.",null,"UNINA9910409667903321"]
[null,null,"Autore",null,"Zhang Xian-Da"]
[null,null,"Titolo",null,"A Matrix Algebra Approach to Artificial Intelligence / / by Xian-Da Zhang"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020"]
[null,null,"ISBN",null,"981-15-2770-9"]
[null,null,"Edizione",null,"[1st ed. 2020.]"]
[null,null,"Descrizione fisica",null,"1 online resource (xxxiv, 820 pages)"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Computer science - Mathematics"]
[{"$nulls":4},"Matrices"]
[{"$nulls":4},"Algebra"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Math Applications in Computer Science"]
[{"$nulls":4},"Linear and Multilinear Algebras, Matrix Theory"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Part 1. Introduction to Matrix Algebra -- Chapter 1. Basic Matrix Computation -- Chapter 2. Matrix Differential -- Chapter 3. Gradient and Optimization -- Chapter 4. Solution of Linear Systems -- Chapter 5. Eigenvalue Decomposition -- Part 2. Artificial Intelligence -- Chapter 6. Machine Learning -- Chapter 7. Neural Networks -- Chapter 8. Support Vector Machines -- Chapter 9. Evolutionary Computation."]
[null,null,null,"Sommario/riassunto","Matrix algebra plays an important role in many core artificial intelligence (AI) areas, including machine learning, neural networks, support vector machines (SVMs) and evolutionary computation. This book offers a comprehensive and in-depth discussion of matrix algebra theory and methods for these four core areas of AI, while also approaching AI from a theoretical matrix algebra perspective. The book consists of two parts: the first discusses the fundamentals of matrix algebra in detail, while the second focuses on the applications of matrix algebra approaches in AI. Highlighting matrix algebra in graph-based learning and embedding, network embedding, convolutional neural networks and Pareto optimization theory, and discussing recent topics and advances, the book offers a valuable resource for scientists, engineers, and graduate students in various disciplines, including, but not limited to, computer science, mathematics and engineering. ."]