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Go Machine Learning Projects

Go Machine Learning Projects

By : Xuanyi Chew
5 (1)
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Go Machine Learning Projects

Go Machine Learning Projects

5 (1)
By: Xuanyi Chew

Overview of this book

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.
Table of Contents (12 chapters)
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Neural Networks - MNIST Handwriting Recognition

Imagine, you were a postal worker. Your would be job to deliver letters. Most of the time, the recipient's name and address would be printed and quite legible, and your job becomes quite easy. But come Thanksgiving and Christmas, the number of envelopes with handwritten addresses increases as people give their personal touches and flourishes. And, to be frank, some people (me included) just have terrible handwriting.

Blame it on schools for no longer emphasizing cursive handwriting if you must, but the problem remains: handwriting is hard to read and interpret. God forbid you have to deliver a letter penned by a doctor (good luck doing that!).

Imagine, instead, if you had built a machine learning system that allows you to read handwriting. That's what we will be doing this chapter and the next; we will be building a type...

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