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 compute
Mathematics for Machine Learning
โ Scribed by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
- Publisher
- Cambridge University Press
- Year
- 2020
- Tongue
- English
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
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 compute
<span>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 c