Clojure for Machine Learning is published by Packt Publishing in April 2014. This book has 292 pages in English, ISBN-13 9781783284351.
Clojure for Machine Learning is an introduction to machine learning techniques and algorithms. This book demonstrates how you can apply these techniques to real-world problems using the Clojure programming language.
It explores many machine learning techniques and also describes how to use Clojure to build machine learning systems. This book starts off by introducing the simple machine learning problems of regression and classification. It also describes how you can implement these machine learning techniques in Clojure. The book also demonstrates several Clojure libraries, which can be useful in solving machine learning problems.
Clojure for Machine Learning familiarizes you with several pragmatic machine learning techniques. By the end of this book, you will be fully aware of the Clojure libraries that can be used to solve a given machine learning problem.
What you will learn from this book
- Build systems that use machine learning techniques in Clojure
- Understand machine learning problems such as regression, classifi cation, and clustering
- Discover the data structures used in machine learning techniques such as artifi cial neural networks and support vector machines
- Implement machine learning algorithms in Clojure
- Learn more about Clojure libraries to build machine learning systems
- Discover techniques to improve and debug solutions built on machine learning techniques
- Use machine learning techniques in a cloud architecture for the modern Web
A book that brings out the strengths of Clojure programming that have to facilitate machine learning. Each topic is described in substantial detail, and examples and libraries in Clojure are also demonstrated.
Who this book is written for
If you are a Clojure developer who wants to explore the area of machine learning, this book is for you. Basic understanding of the Clojure programming language is required. Familiarity with theoretical concepts and notation of mathematics and statistics would be an added advantage.