Delivery: Can be download immediately after purchasing. For new customer, we need process for verification from 30 mins to 12 hours.
Version: PDF/EPUB. If you need EPUB and MOBI Version, please send contact us.
Compatible Devices: Can be read on any devices.
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.
This is a digital product.
Go Machine Learning Projects: Eight projects demonstrating end-to-end machine learning and predictive analytics applications in Go 1st Edition is written by Xuanyi Chew and published by Packt Publishing. The Digital and eTextbook ISBNs for Go Machine Learning Projects are 9781788995191, 1788995198 and the print ISBNs are 9781788993401, 1788993403.

Web and Big Data: Second International Joint Conference, APWeb-WAIM 2018, Macau, China, July 23-25, 2018, Proceedings, Part I eBook
Urban High-Resolution Remote Sensing: Algorithms and Modeling, 1st Edition eBook
Urban Human Mobility: Practices, Analytics, and Strategies for Smart Cities, 1st Edition eBook
Web Information Systems Engineering – WISE 2018: 19th International Conference, Dubai, United Arab Emirates, November 12-15, 2018, Proceedings, Part II eBook
Vibration Theory and Applications with Finite Elements and Active Vibration Control, 1st Edition eBook
Proceedings of the Tenth International Conference on Mathematics and Computing: ICMC 2024, Volume 2 eBook
How to Measure Anything in Cybersecurity Risk eBook
Information, Freedom and Property: The Philosophy of Law Meets the Philosophy of Technology eBook
The Allyn & Bacon Guide to Writing, 8th Edition eBook
Paragraphs and Essays: With Integrated Readings, 13th Edition eBook
Technical Communication Strategies for Today, 3rd Edition eBook
Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, andmore eBook
Proceedings of the 2nd International Conference on Computational and Bio Engineering: CBE 2020 eBook
South-Western Federal Taxation 2019: Essentials of Taxation: Individuals and Business Entities eBook
Teaching Psychology: A Step-by-Step Guide, 3rd Edition eBook
Electron Microscopy in Heterogeneous Catalysis eBook
I and You eBook
The Complete Project Management Office Handbook eBook
Word 2007 eBook 


Reviews
There are no reviews yet.