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This textbook introduces linear algebra and optimization in the context of machine learning.
Item #:
49984643
Linear Algebra and Optimization for Machine Learning: A Textbook 1st ed. 2020 Edition
Item #:
49984643
R$ 232
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This textbook introduces linear algebra and optimization in the context of machine learning.
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What Stands Out
Comprehensive Coverage
This textbook provides an in-depth exploration of linear algebra and optimization tailored for machine learning practitioners, ensuring a solid foundational understanding critical for tailoring algorithms effectively.
Practical Examples
Includes numerous practical examples and real-world applications, bridging the gap between theory and practice, making complex concepts accessible and relevant for aspiring machine learning professionals.
Clear Explanations
The book is designed with clear, concise explanations and visual aids, aiding comprehension and retention of challenging mathematical concepts, ideal for learners at various levels of expertise.
Product Details
- Introduces linear algebra and optimization in the context of machine learning
- Includes examples and exercises throughout the book with a solution manual for teaching instructors
- Target audience: graduate level students, professors in computer science, mathematics, and data science, and advanced undergraduate students
- Chapters organized into Linear algebra and its applications and Optimization and its applications
- Focuses on the most relevant aspects of linear algebra for machine learning and teaches readers how to apply these concepts
- Discusses the extensive background required in linear algebra and optimization specifically for machine learning
| Item Weight | 3 lbs (1.36 kg) |
Who Should Buy?
-
Machine Learning Students
Ideal for students wanting a comprehensive understanding of linear algebra and optimization in machine learning contexts.
-
Data Science Practitioners
Useful for data scientists looking to enhance their mathematical foundations applicable to algorithms and models.
-
Academic Researchers
Beneficial for researchers needing a solid reference for mathematical techniques used in machine learning studies.
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Casual Learners
Not suitable for those seeking a light introduction without rigorous mathematical treatments or applications.
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Beginner Mathematicians
Beginners might find the content too advanced, lacking fundamental explanations and gradual development of concepts.
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Non-Technical Users
Users without a technical background may struggle to grasp the mathematical concepts essential for understanding.
Product Description
Linear Algebra and Optimization for Machine Learning: A Textbook 1st ed. 2020 Edition
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Customer Questions & Answers
-
Question:
What topics are covered in 'Linear Algebra and Optimization for Machine Learning'?
Answer: This textbook delves into essential topics such as linear algebra principles, matrix operations, optimization techniques, and their applications in machine learning. It offers explanations on vector spaces, eigenvalues, and convex optimization. By focusing on the mathematical foundations, it enables learners to grasp complex machine learning algorithms. Practical use cases include implementing machine learning models, improving data analysis processes, and enhancing algorithm efficiency. -
Question:
Who is the target audience for this textbook?
Answer: The book caters specifically to students, researchers, and professionals in computer science, data science, and artificial intelligence. It serves as an excellent resource for those seeking to reinforce their understanding of linear algebra and optimization within the context of machine learning. By offering foundational and advanced insights, it empowers individuals in academic and practical settings to tackle machine learning challenges effectively. -
Question:
Is prior knowledge of mathematics necessary to understand the textbook?
Answer: While a basic understanding of mathematics is beneficial, the book is structured to accommodate various levels of expertise. However, readers with a solid grasp of linear algebra concepts and fundamental calculus will find it easier to engage with the material. The approach balances introductory explanations with in-depth discussions, making it suitable for self-learners and those pursuing formal education in machine learning. -
Question:
Can I find real-world applications of linear algebra in this textbook?
Answer: Yes, the textbook provides multiple real-world applications to illustrate the importance of linear algebra in machine learning. By integrating case studies, it demonstrates how linear transformations, dimensionality reduction techniques, and optimization frameworks apply to various domains, such as image recognition and natural language processing. This practical perspective equips learners to utilize the concepts in their projects and career pursuits. -
Question:
How does this textbook differ from other machine learning books?
Answer: This textbook distinctly focuses on the mathematical underpinnings of machine learning, particularly linear algebra and optimization. Unlike many other books that primarily cover algorithms and applications, it emphasizes a comprehensive understanding of the theoretical aspects that drive these algorithms. This focus helps bridge the gap between mathematical theory and practical machine learning, giving readers a unique advantage. -
Question:
Is there any supplementary material provided with the textbook?
Answer: The textbook may offer supplementary resources such as problem sets, solutions, and online interactive tools to enhance learning. These additional materials are designed to reinforce concepts taught in the chapters, allowing students to practice and apply their understanding effectively. Utilizing these resources aids in better grasping the complexities of linear algebra and optimization as they pertain to machine learning. -
Question:
Is this textbook suitable for self-study or only for classroom use?
Answer: This book is well-suited for both self-study and classroom use. The structured layout, clear explanations, and comprehensive examples facilitate independent learning. It encourages personal exploration of topics, making it an ideal choice for individuals seeking to advance their skills at their own pace. In a classroom setting, it serves as an excellent primary text for courses on machine learning. -
Question:
What software tools are recommended when studying this textbook?
Answer: Readers studying this textbook can enhance their learning experience by using software tools like Python, NumPy, and TensorFlow. These tools are instrumental in implementing the mathematical concepts discussed, such as matrix operations and optimization algorithms. By actively engaging with these software applications, learners can see the practical implications of linear algebra and optimization in real-world machine learning tasks. -
Question:
Are there exercises included in the textbook?
Answer: Yes, the textbook includes a variety of exercises and problems at the end of each chapter. These exercises aim to reinforce understanding and challenge readers to apply the concepts learned. Engaging with these exercises is crucial for mastering the material, as they provide practical scenarios that encourage deeper comprehension and hands-on problem-solving skills. -
Question:
Where can I buy 'Linear Algebra and Optimization for Machine Learning: A Textbook' in Brazil?
Answer: You can purchase 'Linear Algebra and Optimization for Machine Learning: A Textbook' from Ubuy in Brazil. Ubuy is known for a wide selection of academic resources and textbooks, providing a convenient shopping experience for educational materials online. Ensure you check Ubuy for availability and trustworthy transactions when seeking this essential resource.
Linear Editorial Review
This textbook, "Linear Algebra and Optimization for Machine Learning" by Charu Aggarwal, has received mostly positive reviews from customers. Some praise the book for its clear explanations and concise presentation of concepts, making it easy to understand. Many appreciate the focus on both mathematical rigor and applications of concepts in machine learning, particularly in regards to eigenvectors, eigendecomposition, principal component analysis, and singular value decomposition. Others find the book useful in providing coverage of background topics in linear algebra and optimization needed to understand machine learning papers and tools, without having to read separate books on these topics. Additionally, there are many good exercises in each chapter that help users to understand the material. However, some customers express disappointment in the fact that solutions to the exercises are not provided.
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Pros
- Concise presentation of concepts
- Clear explanations
- Focus on mathematical rigor and applications in machine learning
- Useful coverage of background topics in linear algebra and optimization
- Good exercises in each chapter
Cons
- Does not provide solutions to exercises
Product Price History
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R$ 232
Order now and get it around Thursday, June 25
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Features & Benefits
- Targeted towards graduate level students and professors in computer science, mathematics, and data science.
- Provides examples and exercises throughout the book.
- Covers basics of linear algebra and its applications in machine learning.
- Discusses optimization and its applications in machine learning.
- Focuses on the most relevant aspects of linear algebra and optimization for machine learning.
- Offers a specific perspective on linear algebra and optimization for machine learning.