Mathematics for Machine Learning
Price: $50.84
(as of Nov 22,2024 12:03:47 UTC – Details)
From the Publisher
Publisher : Cambridge University Press; 1st edition (April 23, 2020)
Language : English
Paperback : 398 pages
ISBN-10 : 110845514X
ISBN-13 : 978-1108455145
Item Weight : 1.76 pounds
Dimensions : 7 x 0.88 x 10 inches
Customers say
Customers find the book valuable, excellent, and best in class. Opinions are mixed on the math content, with some finding it a good reference for calculations and bringing out the beauty of mathematics, while others say it’s a poor textbook to study math beyond machine learning and hard to follow.
AI-generated from the text of customer reviews
Mathematics for Machine Learning: A Comprehensive Guide
Mathematics is the foundation of machine learning, providing the tools and techniques necessary to understand and manipulate data in order to make predictions and decisions. In this post, we will explore the key mathematical concepts that underpin machine learning and discuss how they are used in practice.
Linear Algebra: Linear algebra is a crucial mathematical tool in machine learning, used to represent and manipulate data in the form of vectors and matrices. Concepts such as matrix multiplication, eigenvalues and eigenvectors, and matrix decompositions are essential for tasks such as dimensionality reduction, feature extraction, and solving optimization problems.
Calculus: Calculus is another fundamental branch of mathematics that plays a key role in machine learning. Concepts such as derivatives and integrals are used to optimize machine learning models, calculate gradients for optimization algorithms, and understand the behavior of complex functions.
Probability and Statistics: Probability theory and statistics are essential for understanding uncertainty and making informed decisions in machine learning. Concepts such as probability distributions, hypothesis testing, and Bayesian inference are used to model and analyze data, estimate parameters, and make predictions with confidence intervals.
Optimization: Optimization is a critical mathematical technique in machine learning, used to find the best set of parameters for a given model. Techniques such as gradient descent, stochastic gradient descent, and convex optimization are used to train machine learning models and minimize the loss function.
Machine learning is a rapidly growing field that relies heavily on mathematics for its theoretical foundations and practical applications. By understanding the key mathematical concepts behind machine learning, you can develop a deeper insight into how algorithms work and how to apply them effectively in real-world scenarios. Whether you are a beginner looking to get started in machine learning or an experienced practitioner seeking to deepen your understanding, a solid grasp of mathematics is essential for success in this exciting and dynamic field.
#Mathematics #Machine #Learning
