Discover the Role of Mathematics in the Modern Field of Artificial Intelligence

Math for Deep Learning is a practical guide for readers who want a better understanding of the mathematical principles used in deep learning and artificial intelligence. If you're curious about how neural networks work and which mathematical tools are used in their design and training, this book can serve as a useful resource.

This publication walks through selected mathematical concepts that frequently appear in deep learning. It covers the basics of linear algebra, statistics, probability, and other areas important for understanding many modern algorithms. The exposition emphasizes clarity and a gradual build-up of knowledge so it is accessible to a wide range of machine learning and data science enthusiasts.

Math for Deep Learning can be a suitable resource for both students and professionals who want to supplement or strengthen their mathematical foundations for working with deep learning methods. The book combines theory with examples that show how mathematical concepts can be related to typical tasks in the field of artificial intelligence.

Why get this book?

• Clear explanations of the key mathematical concepts for deep learning.
• Practical orientation - examples and demonstrations of use in the context of machine learning.
• Suitable for readers with varying levels of experience; the pace may be especially appropriate for those who want to reinforce the fundamentals.
• Helps to better navigate the mathematics used in neural networks and deep models.
• Supports the development of analytical thinking when working with data and algorithms.

Key features and contents:

• Fundamentals of linear algebra and their connections to neural networks.
• Probability and statistics used in data modeling.
• Mathematical principles of optimization and model learning.
• An exposition focused on understanding concepts in the context of deep learning.
• Designed to be readable even without very advanced mathematical education (some material may be more challenging depending on prior knowledge).