Если любишь Mathematics for Machine Learning

Mathematics for Machine Learning
Marc Peter Deisenroth

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 g…

Вот что почитать дальше

Pattern classification and scene analysis
Pattern classification and scene analysis
Richard O. Duda
The Art of Statistics
The Art of Statistics
David J. Spiegelhalter
Statistical inference
Statistical inference
George Casella
Calculus
Calculus
James Stewart
What If?
What If?
Randall Munroe
How to Lie with Statistics
How to Lie with Statistics
Darrell Huff
Calculus with analytic geometry
Calculus with analytic geometry
Howard Anton
Linear algebra and its applications
Linear algebra and its applications
David C. Lay
Statistical methods for the social sciences
Statistical methods for the social sciences
Alan Agresti
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Aurélien Géron
Practical Statistics for Data Scientists: 50 Essential Concepts
Practical Statistics for Data Scientists: 50 Essential Concepts
Peter Bruce
Linear Algebra with Applications
Linear Algebra with Applications
Gareth Williams
Introductory statistics for the behavioral sciences
Introductory statistics for the behavioral sciences
Joan Welkowitz
A first course in probability
A first course in probability
Sheldon M. Ross
Introduction to mathematical statistics
Introduction to mathematical statistics
Robert V. Hogg