Algorithms are the core of artificial intelligence technology. This book introduces the basic algorithms of artificial intelligence.
The book has 10 chapters in total, covering dimension method, distance measurement algorithm, K-means clustering algorithm, error calculation,
Hill climbing algorithm, simulated annealing algorithm, Nelder-Mead algorithm and linear regression algorithm, etc. Book
All algorithms are explained with specific numerical calculations, and readers can try them on their own. Per chapter
All come with program examples, and sample code is available for download in multiple languages on GitHub.
This book is suitable for readers who are introductory to artificial intelligence and readers who are interested in artificial intelligence algorithms.
Read the reference.
Reader comments
This book is very suitable for recent graduates like us! The content is easy to understand and introduces many basic algorithms, such as the K-means clustering algorithm, which reminded me of the book "Introduction to Algorithms". And each chapter has program examples, which is great, like a caring teacher, allowing us to learn and practice at the same time. I think this book is a good reference book for students who want to get started with artificial intelligence.
Very basic, a good book for introductory algorithms. The preprocessing methods and various preparations before the algorithm starts training are very detailed and easy to understand without any lag.
This book is very suitable for recent graduates like us! The content is easy to understand and introduces many basic algorithms, such as the K-means clustering algorithm, which reminded me of the book "Introduction to Algorithms". And each chapter has program examples, which is great, like a caring teacher, allowing us to learn and practice at the same time. I think this book is a good reference book for students who want to get started with artificial intelligence.
Very basic, a good book for introductory algorithms. The preprocessing methods and various preparations before the algorithm starts training are very detailed and easy to understand without any lag.