Title
Linear Algebra And Learning From Data
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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.
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
- Q: How many pages are in the book? A: This book contains four hundred forty-six pages. It provides a comprehensive overview of linear algebra and its applications in deep learning.
- Q: What are the dimensions of this book? A: The book measures seven point seventy-two inches in length, zero point ninety-eight inches in width, and nine point fifty-three inches in height. These dimensions make it easy to handle and read.
- Q: What type of binding does this book have? A: This book is bound in hardcover. The hardcover design enhances durability and provides a professional appearance.
- Q: What is the main focus of this book? A: The book focuses on linear algebra and its relationship with deep learning. It covers essential mathematical concepts needed for understanding data-driven learning.
- Q: Who is the author of this book? A: The author is Gilbert Strang, a renowned professor known for his expertise in linear algebra. His previous works have been highly acclaimed in the academic community.
- Q: What is the target audience for this book? A: This book is suitable for students and professionals interested in linear algebra and machine learning. It is ideal for those looking to understand the mathematical foundations of deep learning.
- Q: Can I use this book for self-study? A: Yes, this book is designed for self-study. It includes worked examples and exercises that help reinforce the concepts presented.
- Q: Is this book appropriate for beginners? A: Yes, the book is appropriate for beginners. It introduces linear algebra in a readable and accessible manner, making it easier for newcomers to grasp the subject.
- Q: How should I store this book? A: Store this book in a cool, dry place to maintain its condition. Avoid exposing it to direct sunlight to prevent fading of the cover and pages.
- Q: What if I need to return the book? A: You can return the book within the specified return period if you are not satisfied. Be sure to check the return policy for details on conditions and procedures.
- Q: Does this book come with a warranty? A: No, this book does not come with a warranty. However, you can return it if there are any issues upon receipt.
- Q: What should I do if the book arrives damaged? A: If the book arrives damaged, contact customer service immediately for assistance. They will guide you through the return or replacement process.
- Q: Is there any online support for this book? A: Yes, there may be online resources and forums for discussing the content of this book. Check the publisher's website for additional support materials.
- Q: How does this book compare to other linear algebra books? A: This book uniquely integrates linear algebra with deep learning concepts, setting it apart from traditional linear algebra texts. It offers a modern perspective on the subject.
- Q: Is this book suitable for advanced learners? A: Yes, advanced learners can benefit from this book. It presents complex topics in a structured way, making it a valuable resource for deepening understanding.