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size
17.78 x 2.24 x 25.4 cm
Product Information
Ideal For
General Readers, Fiction Lovers, Mythology & Historical Fiction Readers, Indian Mythology Enthusiasts, Young Adults (YA) & Adults, College Students, Competitive Exam Aspirants (General Reading / Culture), Gift Purpose (Books Gift Set), Spiritual & Philosophy Interest Readers, Library / Book Collection Buyers
Country Of Origin
India
About Author
Marc Peter Deisenroth Is A Well-Known Academic And Researcher In The Field Of Artificial Intelligence (Ai) And Machine Learning. He Is Especially Recognized For His Contributions To Probabilistic Machine Learning, Robotics, And Data-Efficient Learning Methods.
Author Name
Marc Peter Deisenroth
Genre
Computer Science
No Of Pages
496
Publisher
O'Reilly Media
Isbn Code
9.78E+12
Product Description
- Premium Quality: Mathematics for Machine Learning by Marc Peter Deisenroth is a comprehensive 496-page self-contained textbook that bridges the gap between mathematical and machine learning texts.
- Key Topics Covered: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics.
- User Experience: This textbook introduces mathematical concepts with a minimum of prerequisites, making it accessible to data science and computer science students as well as working professionals.
- Practical Application: It uses these mathematical concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines.
- Versatile Occasion: For students and others with a mathematical background, these derivations provide a deeper understanding of the foundations underpinning modern machine learning.
- Ideal For: An essential resource for college students, competitive exam aspirants in computer science, and library or book collection buyers seeking a rigorous yet approachable ML mathematics reference.