Go Machine Learning Projects: Eight projects demonstrating end-to-end machine learning and predictive analytics applications in Go

Xuanyi Chew

ISBN 10: 1788993403 / ISBN 13: 9781788993401

Edité par Packt Publishing 2018-11-30, 2018

A propos de cet article

N° de réf. du vendeur 6666-IUK-9781788993401

Détails bibliographiques

• Titre: Go Machine Learning Projects: Eight projects demonstrating end-to-end machine learning and predictive analytics applications in Go
• Éditeur: Packt Publishing 2018-11-30
• Date de parution: 2018
• Langue: anglais
• ISBN à 10 chiffres: 1788993403
• ISBN à 13 chiffres: 9781788993401
• Reliure: Paperback
• État de l'article: New

A propos de ce titre

Synopsis

Work through exciting projects to explore the capabilities of Go and Machine Learning

Key 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

Book Description

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.

What you will 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

Who this book is for

If you're a machine learning engineer, data science professional, or Go programmer who wants to implement machine learning in your real-world projects and make smarter applications easily, this book is for you. Some coding experience in Golang and knowledge of basic machine learning concepts will help you in understanding the concepts covered in this book.

Table of Contents

1. How to Solve All Machine Learning Problems
2. Linear Regression - House Price Prediction
3. Classification - Spam Email Detection
4. Decomposing CO2 Trends Using Time Series Analysis
5. Clean Up Your Personal Twitter Timeline by Clustering Tweets
6. Neural Networks - MNIST Handwriting Recognition
7. Convolutional Neural Networks - MNIST Handwriting Recognition
8. Basic Facial Detection
9. Hot Dog or Not Hot Dog - Using External Services
10. What's Next?

À propos de l?auteur

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.