Mathematics for Machine Learning Hardcover – April 23, 2020
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Mathematics for Machine Learning Hardcover – April 23, 2020
This self-contained textbook bridges the gap between mathematical and machine learning texts by introducing mathematical concepts with a minimum of prerequisites.
Mathematics for Machine Learning Hardcover – April 23, 2020
Nº de artículo: 192089962

Mathematics for Machine Learning Hardcover – April 23, 2020

90% of respondents would recommend this to a friend

Nº de artículo: 192089962

USD 77

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Detalles de producto

Shop Mathematics for Machine Learning Hardcover – April 23, 2020 online at a best price in Ecuador. 110845514X
Publisher Cambridge University Press
Publication date April 23, 2020
Language English
Print length 390 pages
ISBN-10 1108470041
ISBN-13 978-1108470049
Item Weight 980 g
Dimensions 7 x 1.11 x 10 inches (17.8 x 2.8 x 25.4 cm)

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Mathematics for Machine Learning Hardcover – April 23, 2020

About This Item

Introducing the Mathematics for Machine Learning 1st Edition As the field of machine learning continues to revolutionize various industries, it is essential to have a solid understanding of the mathematical concepts that underpin this powerful technology. The Mathematics for Machine Learning 1st Edition is a comprehensive textbook that covers all the key mathematical foundations needed for successful implementation and application of machine learning algorithms. With endorsements from esteemed experts in the field, such as Joelle Pineau from McGill University and Christopher Bishop from Microsoft Research Cambridge, this book comes highly recommended for both beginners and experienced machine learning researchers and engineers. This self-contained textbook is designed to be accessible to a wide range of readers, with a minimum of prerequisites. It starts with a thorough introduction to linear algebra, which serves as the basis for many machine learning techniques.

From there, it delves into topics such as analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics – all of which are crucial for developing a strong understanding of machine learning algorithms. Whether you are a student, a colleague, or simply someone interested in building a solid foundation in machine learning, this book will be an invaluable resource. It presents the necessary mathematical concepts in a clear and concise manner, making it easy to grasp complex ideas and apply them to real-world scenarios. The Mathematics for Machine Learning 1st Edition is not just a tutorial; it is a comprehensive reference text that you can turn to time and time again. It will help you gain a deeper understanding of the mathematical principles behind machine learning algorithms, enabling you to unlock the full potential of this transformative technology. Don't miss out on this essential resource for anyone interested in machine learning.

Order your copy of the Mathematics for Machine Learning 1st Edition today and take your understanding of this exciting field to new heights.

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Preguntas y respuestas de los clientes

  • Pregunta: What are the central machine learning methods discussed in the book?

    Respuesta: Linear regression, principal component analysis, Gaussian mixture models and support vector machines.
  • Pregunta: Are there prerequisites needed for understanding the mathematical concepts?

    Respuesta: No, the book introduces mathematical concepts with a minimum of prerequisites.
  • Pregunta: Where can programming tutorials be accessed?

    Respuesta: Programming tutorials are offered on the book's web site.

English Edition Marc Peter Deisenroth , A. Aldo Faisal Applied Editorial Review

**Editorial Review of "Mathematics for Machine Learning"** "Mathematics for Machine Learning" has garnered positive feedback from customers, particularly appreciating its clear and concise approach to complex mathematical concepts that are essential for understanding machine learning. Reviewers noted that the book effectively bridges the gap between abstract mathematics and practical machine learning applications, making it accessible even to those with a less rigorous math background. The structured layout of the book has been highlighted as a significant advantage, allowing readers to build their knowledge incrementally, starting from fundamental topics and gradually progressing to more advanced material. Many readers commended the inclusion of practical examples and exercises that reinforce the theoretical concepts, aiding in the comprehension and application of the material. Furthermore, users have praised the author's ability to simplify difficult topics without diluting the content, resulting in a resource that is both educational and engaging. Some readers mentioned that this book serves as a great supplementary text alongside other machine learning resources, enhancing their understanding and implementation of various algorithms. However, a few users pointed out that while the book is well-written, it may require some prior knowledge of calculus and linear algebra to fully grasp the material. Despite this, the overall Consensus is that "Mathematics for Machine Learning" is an invaluable resource for anyone looking to deepen their understanding of the mathematical foundations crucial for machine learning. **

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ventajas

  • Clear and concise explanation of complex concepts
  • Effective bridge between mathematics and machine learning applications
  • Structured layout allows for incremental learning
  • Inclusion of practical examples and exercises
  • Engaging content that simplifies difficult topics

Contras

  • May require prior knowledge of calculus and linear algebra for full comprehension

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características y beneficios

  • Fundamental mathematical tools needed to understand machine learning
  • Self-contained textbook for building intuition and practical experience
  • Bridges gap between mathematical and machine learning texts
  • Introduces mathematical concepts with a minimum of prerequisites
  • Chapter exercises and programming tutorials available on the book's website

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