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- Mathematics for Machine Learning
منتج #:
192089962
Mathematics for Machine Learning
89% من المشترين سيوصون بهذا المنتج لصديق
منتج #:
192089962
DZD 10897
تفاصيل السعر
باستثناء رسوم الشحن والجمارك ( سيتم احتساب رسوم الشحن والجمارك عند إتمام الشراء )
*سيتم استيراد جميع العناصر من JP
6%
متوفر فى المخزون
مستورد من متجر Japan
كمية:
بقي 8 فقط من المنتج في المخزن.
اطلب الآن واحصل عليه حول الجمعة, أكتوبر 09
معاملة آمنة
تعمل يوباي جاهدة لحماية أمنك وخصوصيتك. يضمن نظام أمان الدفع المتقدم لدينا السرية من خلال تشفير معلوماتك أثناء النقل باستخدام بروتوكولات AES (معايير التشفير المتقدمة) وSSL (طبقة المنافذ الآمنة). تفاصيل الدفع الخاصة بك آمنة بنسبة %100 لأننا لا نشارك تفاصيل الدفع الخاصة بك مع بائعين تابعين لجهات خارجية
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This self-contained textbook bridges the gap between mathematical and machine learning texts by introducing mathematical concepts with a minimum of prerequisites.
شحن
سريع
استرجاع
مجاني*
تغليف آمن
منتجات أصلية %100
الامتثال لمعيار PCI DSS
حاصل على شهادة ISO 27001
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أبرز ما يلفت الانتباه
Comprehensive Coverage
Covers essential mathematical concepts like linear algebra, calculus, and statistics, tailored for machine learning applications, ensuring learners grasp foundational knowledge needed for algorithm implementation.
Practical Applications
Integrates real-world examples and practical exercises, bridging the gap between theory and practice, helping learners apply mathematical concepts directly to machine learning problems.
Accessible Learning
Designed for a broad audience, from beginners to advanced practitioners, featuring clear explanations and step-by-step guidance, making complex topics accessible without a heavy mathematical background.
تفاصيل المنتج
- The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
| Publisher | Cambridge University Press |
| Publication date | April 23, 2020 |
| Language | English |
| Print length | 390 pages |
| ISBN-10 | 110845514X |
| ISBN-13 | 978-1108455145 |
| Item Weight | 800 g |
| Dimensions | 7 x 0.88 x 10 inches (17.8 x 2.2 x 25.4 cm) |
من يجب أن يشتري؟
-
Aspiring Data Scientists
Ideal for individuals looking to understand the mathematical foundations crucial for data science and machine learning applications.
-
Computer Science Students
Beneficial for students wanting to enhance their programming skills with essential mathematical concepts for advanced studies in AI.
-
Industry Professionals
Useful for professionals seeking to upskill in machine learning, providing necessary mathematical knowledge for practical implementations.
-
Complete Mathematics Novices
Not suitable for those without basic mathematical knowledge, as it presumes understanding of fundamental concepts.
وصف المنتج
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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أسئلة العملاء & الإجابات
-
سؤال:
What are the central machine learning methods discussed in the book?
إجابه: Linear regression, principal component analysis, Gaussian mixture models and support vector machines. -
سؤال:
Are there prerequisites needed for understanding the mathematical concepts?
إجابه: No, the book introduces mathematical concepts with a minimum of prerequisites. -
سؤال:
Where can programming tutorials be accessed?
إجابه: Programming tutorials are offered on the book's web site.
English Edition Marc Peter Deisenroth Format: Paperback Applied Editorial Review
Mathematics for Machine Learning is a comprehensive resource published by Cambridge University Press on April 23, 2020. With a 390-page print length, this book provides an in-depth analysis of the mathematical principles important in machine learning. Written in English, it effectively bridges the gap between mathematics and practical machine learning applications. The dimensions of the book are 7 x 0.88 x 10 inches, making it a suitable reference for both students and professionals in the field. Although there are no reviews available, the content is structured to cater to a wide range of readers interested in enhancing their mathematical foundations for machine learning.
مراجعات العملاء وتقييماتهم
1024 تقييمات العملاء
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5 نجمة
77%
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4 نجمة
14%
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3 نجمة
3%
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2 نجمة
2%
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1 نجمة
4%
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إيجابيات
- Comprehensive coverage of essential mathematical concepts
- Well-structured for machine learning applications
- Published by a reputable academic press
- Appropriate for both students and professionals
- Clear explanations of complex topics
سلبيات
- No available user reviews to gauge personal experiences
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الخروج الآمن
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DZD 10897
اطلب الآن واحصل عليه حول الجمعة, أكتوبر 09
هذا المنتج غير ممنوع في بلدي. (الرجاء الضغط على الرابط أعلاه إذا لم يكن هذا المنتج ممنوعاً في بلدك ، لذلك سيقوم فريقنا بمراجعته والسماح به.)
كمية:
بقي 8 فقط من المنتج في المخزن.
معاملة آمنة
نوفر لك مدفوعات مشفّرة، وحماية متكاملة للمشتري، مع الالتزام بمعايير PCI DSS وشهادة ISO 27001:2022 لضمان أعلى مستويات الأمان في كل عملية شراء.
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المميزات والفوائد
- 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
ضمان Ubuy
تسوّق بثقة مع منتجات أصلية %100، ومدفوعات آمنة متوافقة مع معيار PCI DSS، وحماية بيانات معتمدة وفق ISO 27001، وشحن دولي سريع، وإرجاع مجاني*، وتغليف آمن لكل طلب.