Book Image

Go Machine Learning Projects

By : Xuanyi Chew
Book Image

Go Machine Learning Projects

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.

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Table of Contents (12 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
How to Solve All Machine Learning Problems
How to Solve All Machine Learning Problems
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!
2
Linear Regression - House Price Prediction
Linear Regression - House Price Prediction
The project
Exploratory data analysis
Linear regression
Discussion and further work
Summary
3
Classification - Spam Email Detection
Classification - Spam Email Detection
The project
Exploratory data analysis
The classifier
Naive Bayes
Implementating the classifier
Putting it all together
Summary
4
Decomposing CO2 Trends Using Time Series Analysis
Decomposing CO2 Trends Using Time Series Analysis
Exploratory data analysis
Decomposition
Forecasting
Summary
References
5
Clean Up Your Personal Twitter Timeline by Clustering Tweets
Clean Up Your Personal Twitter Timeline by Clustering Tweets
The project
K-means
DBSCAN
Data acquisition
Exploratory data analysis
Data massage
Clustering
Real data
The program
Tweaking the program
Summary
6
Neural Networks - MNIST Handwriting Recognition
Neural Networks - MNIST Handwriting Recognition
A neural network
Linear algebra 101
Learning
The project
Training the neural network
Cross-validation
Summary
7
Convolutional Neural Networks - MNIST Handwriting Recognition
Convolutional Neural Networks - MNIST Handwriting Recognition
Everything you know about neurons is wrong
Neural networks – a redux
The project
CNNs
Describing a CNN
Running the neural network
Testing
Summary
8
Basic Facial Detection
Basic Facial Detection
What is a face?
PICO
GoCV
Pigo
Face detection program
Evaluating algorithms
Summary
9
Hot Dog or Not Hot Dog - Using External Services
Hot Dog or Not Hot Dog - Using External Services
MachineBox
What is MachineBox?
The project
The results
What does this all mean?
Why MachineBox?
Summary
10
What's Next?
What's Next?
What should the reader focus on?
The researcher, the practitioner, and their stakeholder
What did this book not cover?
Where can I learn more?
Thank you
11
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Evaluating algorithms

There are many dimensions upon which we can evaluate the algorithms. This section explores how to evaluate algorithms.

Assuming we want to have fast face detection—which algorithm would be better?

The only way to understand the performance of an algorithm is to measure it. Thankfully Go comes with benchmarking built in. That is what we are about to do.

To build benchmarks we must be very careful about what we're benchmarking. In this case, we want to benchmark the performance of the detection algorithm. This means comparing classifier.DetectMultiScale versus, pigoClass.RunCascade and pigoClass.ClusterDetections.

Also, we have to compare apples to apples—it would be unfair if we compare one algorithm with a 3840 x 2160 image and the other algorithm with a 640 x 480 image. There are simply more pixels in the former compared to the latter...

Previous Section
End of Section 7
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