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Nº de artículo:
12904979
Linear Algebra and Learning from Data
Nº de artículo:
12904979
USD 102
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Linear algebra and the foundations of deep learning, together at last!
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What Stands Out
Comprehensive Coverage
This book integrates linear algebra with modern data science, offering a thorough exploration that bridges theory and practical applications for students and professionals alike.
Accessible Learning
Designed for readers at all levels, it presents complex concepts in a clear manner, making advanced topics in linear algebra and data analysis approachable.
Real-World Applications
By emphasizing real-world examples, the text equips readers with the necessary tools to apply linear algebra techniques to machine learning and data-driven fields.
Detalles de producto
- Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
| Publisher | Wellesley-Cambridge Press |
| Publication date | January 31, 2019 |
| Edition | First Edition |
| Language | English |
| Print length | 446 pages |
| ISBN-10 | 0692196382 |
| ISBN-13 | 978-0692196380 |
| Item Weight | 2.05 pounds (930 grams) |
| Reading age | 18 years and up |
| Dimensions | 7.72 x 0.98 x 9.53 inches (19.6 x 2.5 x 24.2 cm) |
Who Should Buy?
-
Engineering Students
Ideal for engineering students who require a strong foundation in linear algebra for applied technical courses.
-
Data Scientists
Useful for data scientists looking to understand linear algebra concepts applied in machine learning and data analysis.
-
Graduate Researchers
Beneficial for graduate researchers needing to apply linear algebra techniques in their thesis or research projects.
-
Beginner Learners
Not suitable for absolute beginners who have no prior knowledge of mathematics or linear algebra concepts.
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Preguntas y respuestas de los clientes
-
Pregunta:
What is 'Linear Algebra and Learning from Data First Edition' about?
Respuesta: This book merges linear algebra concepts with practical data learning applications. It focuses on how linear algebra can be used as a foundational tool for understanding machine learning algorithms and concepts. By exploring topics like matrix operations, eigenvalues, and more, readers can grasp how these mathematical principles drive algorithms used in data science today. The integration of theoretical frameworks with practical examples makes it a valuable resource for students and professionals alike. -
Pregunta:
Who is the author of 'Linear Algebra and Learning from Data First Edition'?
Respuesta: The author of this insightful text is Gilbert Strang, a renowned mathematician and professor at MIT. His expertise in linear algebra and its application to data science shines throughout the book. Strang’s teaching methods are reflected in the clear explanations and real-world applications presented in the text, making complex topics accessible. His work is highly respected in the academic community, ensuring readers benefit from a well-grounded approach to both theory and practice. -
Pregunta:
What topics are covered in 'Linear Algebra and Learning from Data First Edition'?
Respuesta: The book covers a wide array of topics that include fundamentals of linear algebra, dimensionality reduction techniques, optimization methods, and the interplay between statistical learning and linear algebra. Each topic is tailored to bridge the gap between theory and practical usage in data-driven technologies. Readers can expect to gain insights into how linear algebra facilitates machine learning processes, providing a comprehensive understanding beneficial for both academic and industry applications. -
Pregunta:
Is 'Linear Algebra and Learning from Data First Edition' suitable for beginners?
Respuesta: Yes, this book caters to both newcomers and those with a foundational understanding of mathematics. It starts with fundamental principles before advancing to complex applications, making it approachable for beginners. The clear explanations, supported by examples, help demystify challenging concepts. For students interested in machine learning, this text serves as a crucial stepping stone to more advanced studies in data science and analytics. -
Pregunta:
Can 'Linear Algebra and Learning from Data First Edition' be used for self-study?
Respuesta: Absolutely! This book is well-structured for self-study. Each chapter includes exercises and examples that encourage readers to practice and apply the concepts learned. The logical progression from basic to advanced topics makes it easier for readers to track their understanding. Whether you’re preparing for academic courses or simply looking to enhance your data analytic skills, this text provides a robust framework for independent learning. -
Pregunta:
How does 'Linear Algebra and Learning from Data First Edition' relate to machine learning?
Respuesta: Linear algebra is at the core of many machine learning algorithms, and this book highlights those connections. It explains how concepts like vector spaces and transformations impact data processing and model performance. Understanding this relationship equips readers with essential knowledge to implement algorithms effectively. This text can serve both as a reference and a guide to those looking to implement machine learning techniques in their projects. -
Pregunta:
Are there practical examples in 'Linear Algebra and Learning from Data First Edition'?
Respuesta: Yes, the book is rich with practical examples that illustrate how linear algebra is applied in real-world scenarios. From analyzing data sets to running experiments with algorithms, the author integrates examples that reflect actual challenges faced in data science. These cases not only enhance understanding but also inspire readers to explore how they might apply similar techniques in their own work. -
Pregunta:
What is the target audience for 'Linear Algebra and Learning from Data First Edition'?
Respuesta: The target audience includes undergraduate and graduate students in mathematics, computer science, and engineering, as well as professionals engaged in data science and machine learning. The blend of theory and practical applications makes it suitable for both academic study and professional development. Its accessible approach ensures that anyone interested in the intersection of linear algebra and data should find value in this comprehensive resource. -
Pregunta:
Where can I buy 'Linear Algebra and Learning from Data First Edition' in Puerto Rico?
Respuesta: You can purchase 'Linear Algebra and Learning from Data First Edition' from Ubuy, which provides a reliable platform for acquiring a variety of educational materials. Ubuy caters to a broad audience and often offers additional information on the product, ensuring that you make an informed decision before your purchase. Explore their selection today to find the book you need. -
Pregunta:
Is there a digital version of 'Linear Algebra and Learning from Data First Edition' available?
Respuesta: Yes, a digital version is typically available for readers who prefer eBooks. The digital format presents the same content as the print version but offers advantages like portability and accessibility on various devices. This makes it convenient for those who want to study on the go or for readers who prefer interactive features found in eBooks. Check major eBook platforms for availability.
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USD 102
Haz tu pedido ahora y recíbelo por ahí Viernes, Agosto 07
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Solo quedan 9 artículos en stock.
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características y beneficios
- Combines linear algebra and deep learning in one textbook
- Covers the fundamental subspaces and singular value decompositions
- Includes topics like large matrix computation techniques and compressed sensing
- Addresses probability, statistics, and optimization for learning from data
- Provides insights into the architecture of neural nets and stochastic gradient descent
- Ideal for students looking to understand and apply deep learning concepts
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