Go Machine Learning Projects
eBook Details:
- Paperback: 348 pages
- Publisher: WOW! eBook (November 30, 2018)
- Language: English
- ISBN-10: 1788993403
- ISBN-13: 978-1788993401
eBook Description:
Go Machine Learning Projects: Work through exciting projects to explore the capabilities of Go and Machine Learning
Go is the perfect language for machine learning; it helps to clearly describe complex algorithms, and also helps developers to understand how to run efficient optimized code. This book will teach you how to implement machine learning in Go to make programs that are easy to deploy and code that is not only easy to understand and debug, but also to have its performance measured.
The book begins by guiding you through setting up your machine learning environment with Go libraries and capabilities. You will then plunge into regression analysis of a real-life house pricing dataset and build a classification model in Go to classify emails as spam or ham. Using Gonum, Gorgonia, and STL, you will explore time series analysis along with decomposition and clean up your personal Twitter timeline by clustering tweets. In addition to this, you will learn how to recognize handwriting using neural networks and convolutional neural networks. Lastly, you’ll learn how to choose the most appropriate machine learning algorithms to use for your projects with the help of a facial detection project.
- Set up a machine learning environment with Go libraries
- Use Gonum to perform regression and classification
- Explore time series models and decompose trends with Go libraries
- Clean up your Twitter timeline by clustering tweets
- Learn to use external services for your machine learning needs
- Recognize handwriting using neural networks and CNN with Gorgonia
- Implement facial recognition using GoCV and OpenCV
By the end of this Go Machine Learning Projects book, you will have developed a solid machine learning mindset, a strong hold on the powerful Go toolkit, and a sound understanding of the practical implementations of machine learning algorithms in real-world projects.
