Jeremiah D. Deng: Machine Learning with Julia, Gebunden
Machine Learning with Julia
- An Algorithmic Exploration
(soweit verfügbar beim Lieferanten)
- Verlag:
- Springer, 04/2026
- Einband:
- Gebunden
- Sprache:
- Englisch
- ISBN-13:
- 9789819696888
- Artikelnummer:
- 12776669
- Umfang:
- 448 Seiten
- Gewicht:
- 1336 g
- Maße:
- 285 x 215 mm
- Stärke:
- 30 mm
- Erscheinungstermin:
- 17.4.2026
- Serie:
- Machine Learning: Foundations, Methodologies, and Applications
- Hinweis
-
Achtung: Artikel ist nicht in deutscher Sprache!
Klappentext
This textbook offers a comprehensive and accessible introduction to machine learning with the Julia programming language. It bridges mathematical theory and real-world practice, guiding readers through both foundational concepts and advanced algorithms. Covering topics from essential principles like Kullback--Leibler divergence and eigen-analysis to cutting-edge techniques such as deep transfer learning and differential privacy, each chapter delivers clear explanations and detailed algorithmic treatments. Sample code accompanies every major topic, enabling hands-on learning and faster implementation.
By leveraging Julia's powerful machine learning ecosystem---including libraries such as Flux. jl, MLJ. jl, and more---this book empowers readers to build robust, state-of-the-art machine learning models.
Ideal for students, researchers, and professionals alike, this textbook is designed for those seeking a solid theoretical foundation in machine learning, along with deep algorithmic insight and practical problem-solving inspiration.
Mehr von Machine Learnin...