Understanding deep learning von prince simon (15 Ergebnisse)

Hardcover. Zustand: Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Zustand: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

Hardback. Zustand: Very Good. An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice. Deep learning is a fast-moving field with sweeping relevance in today's increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics. Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models Short, focused chapters progress in complexity, easing students into difficult concepts Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models Streamlined presentation separates critical ideas from background context and extraneous detail Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible Programming exercises offered in accompanying Python Notebooks.…

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: NEW.

Zustand: New. In English.

Zustand: New. Simon J. D. Prince is Honorary Professor of Computer Science at the University of Bath and author of Computer Vision: Models, Learning and Inference. A research scientist specializing in artificial intelligence and deep learning, he has led teams of .

Buch. Zustand: Neu. Neuware -An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.Deep learning is a fast-moving field with sweeping relevance in today's increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.- Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models- Short, focused chapters progress in complexity, easing students into difficult concepts - Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models- Streamlined presentation separates critical ideas from background context and extraneous detail- Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible - Programming exercises offered in accompanying Python Not Elektronisches BuchEasy Access System Europe Oü, 16879218, EU GPSR Authorised Representative, Englisch.…

Buch. Zustand: Neu. Understanding Deep Learning | Simon J. D. Prince | Buch | Einband - fest (Hardcover) | Englisch | 2023 | The MIT Press | EAN 9780262048644 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Buch. Zustand: Neu. Neuware - An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.Deep learning is a fast-moving field with sweeping relevance in today's increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.- Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models- Short, focused chapters progress in complexity, easing students into difficult concepts - Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models- Streamlined presentation separates critical ideas from background context and extraneous detail- Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible - Programming exercises offered in accompanying Python Not Elektronisches Buch.…

Hardcover. Zustand: Brand New. 496 pages. 9.25x8.25x1.50 inches. In Stock.

Zustand: New. 2023. Hardcover. . . . . . Books ship from the US and Ireland.

Buch. Zustand: Neu. Neuware -An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.Deep learning is a fast-moving field with sweeping relevance in today's increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.- Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models- Short, focused chapters progress in complexity, easing students into difficult concepts - Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models- Streamlined presentation separates critical ideas from background context and extraneous detail- Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible - Programming exercises offered in accompanying Python Not Elektronisches Buch Englisch.…