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Dive Into Deep Learning
Taschenbuch von Aston Zhang (u. a.)
Sprache: Englisch

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Beschreibung
Deep learning has revolutionized pattern recognition, introducing tools that power a wide range of technologies in such diverse ¿elds as computer vision, natural language processing, and automatic speech recognition. Applying deep learning requires you to simultaneously understand how to cast a problem, the basic mathematics of modeling, the algorithms for ¿tting your models to data, and the engineering techniques to implement it all. This book is a comprehensive resource that makes deep learning approachable, while still providing sufficient technical depth to enable engineers, scientists, and students to use deep learning in their own work. No previous background in machine learning or deep learning is required-every concept is explained from scratch and the appendix provides a refresher on the mathematics needed. Runnable code is featured throughout, allowing you to develop your own intuition by putting key ideas into practice.
Deep learning has revolutionized pattern recognition, introducing tools that power a wide range of technologies in such diverse ¿elds as computer vision, natural language processing, and automatic speech recognition. Applying deep learning requires you to simultaneously understand how to cast a problem, the basic mathematics of modeling, the algorithms for ¿tting your models to data, and the engineering techniques to implement it all. This book is a comprehensive resource that makes deep learning approachable, while still providing sufficient technical depth to enable engineers, scientists, and students to use deep learning in their own work. No previous background in machine learning or deep learning is required-every concept is explained from scratch and the appendix provides a refresher on the mathematics needed. Runnable code is featured throughout, allowing you to develop your own intuition by putting key ideas into practice.
Über den Autor
Aston Zhang is Senior Scientist at Amazon Web Services.
Inhaltsverzeichnis
Installation; Notation; 1. Introduction; 2. Preliminaries; 3. Linear neural networks for regression; 4. Linear neural networks for classification; 5. Multilayer perceptrons; 6. Builders guide; 7. Convolutional neural networks; 8. Modern convolutional neural networks; 9. Recurrent neural networks; 10. Modern recurrent neural networks; 11. Attention mechanisms and transformers; Appendix. Tools for deep learning; Bibliography; Index.
Details
Erscheinungsjahr: 2023
Genre: Importe, Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
ISBN-13: 9781009389433
ISBN-10: 1009389432
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Zhang, Aston
Lipton, Zachary C
Li, Mu
Smola, Alexander J
Hersteller: Cambridge University Press
Verantwortliche Person für die EU: Produktsicherheitsverantwortliche/r, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de
Maße: 255 x 203 x 30 mm
Von/Mit: Aston Zhang (u. a.)
Erscheinungsdatum: 07.12.2023
Gewicht: 1,376 kg
Artikel-ID: 126725655
Über den Autor
Aston Zhang is Senior Scientist at Amazon Web Services.
Inhaltsverzeichnis
Installation; Notation; 1. Introduction; 2. Preliminaries; 3. Linear neural networks for regression; 4. Linear neural networks for classification; 5. Multilayer perceptrons; 6. Builders guide; 7. Convolutional neural networks; 8. Modern convolutional neural networks; 9. Recurrent neural networks; 10. Modern recurrent neural networks; 11. Attention mechanisms and transformers; Appendix. Tools for deep learning; Bibliography; Index.
Details
Erscheinungsjahr: 2023
Genre: Importe, Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
ISBN-13: 9781009389433
ISBN-10: 1009389432
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Zhang, Aston
Lipton, Zachary C
Li, Mu
Smola, Alexander J
Hersteller: Cambridge University Press
Verantwortliche Person für die EU: Produktsicherheitsverantwortliche/r, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de
Maße: 255 x 203 x 30 mm
Von/Mit: Aston Zhang (u. a.)
Erscheinungsdatum: 07.12.2023
Gewicht: 1,376 kg
Artikel-ID: 126725655
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