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Go Machine Learning Projects

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Learn
  • 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
About

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.

By the end of this 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.

Features
  • Explore ML tasks and Go’s machine learning ecosystem
  • Implement clustering, regression, classification, and neural networks with Go
  • Get to grips with libraries such as Gorgonia, Gonum, and GoCv for training models in Go
Page Count 348
Course Length 10 hours 26 minutes
ISBN 9781788993401
Date Of Publication 30 Nov 2018
What is a problem? 
What is an algorithm? 
What is machine learning? 
Do you need machine learning?
The general problem solving process
What is a model?
On writing and chapter organization 
Why Go? 
Quick start
Functions
Variables
Let's Go! 
Everything you know about neurons is wrong 
Neural networks – a redux
The project
CNNs
Describing a CNN
Running the neural network
Testing
Summary
What is a face? 
PICO 
GoCV 
Pigo
Face detection program 
Evaluating algorithms
Summary
MachineBox
What is MachineBox?
The project
The results
What does this all mean? 
Why MachineBox?
Summary

Authors

Xuanyi Chew

Xuanyi Chew is the Chief Data Scientist of a Sydney-based logistics startup. He is the primary author of Gorgonia, an open source deep learning package for Go. He's been practicing machine learning for the past 12 years, applying them typically to help startups. His goal in life is to make an artificial general intelligence a reality. He enjoys learning new things.