*Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.*

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Language: en

Pages: 398

Pages: 398

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

Language: en

Pages:

Pages:

Books about Kernel Methods for Machine Learning with Math and R

Language: en

Pages: 208

Pages: 208

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. The book’s main features are as

Language: en

Pages: 122

Pages: 122

★ 55% OFF for Bookstores! NOW at $26.95 instead of $39.95★ You find out about machine learning form A to Z even if you are a beginner Do you want to spark in the science of XXI century? Do you want to become a recreational scientist in deep learning? If

Language: en

Pages: 344

Pages: 344

Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits. With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You’ll work through Python examples