Machine Learning With Go

Build simple, maintainable, and easy to deploy machine learning applications.
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Machine Learning With Go

Daniel Whitenack

3 customer reviews
Build simple, maintainable, and easy to deploy machine learning applications.
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Book Details

ISBN 139781785882104
Paperback304 pages

Book Description

The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real-world scenarios.

Machine Learning with Go shows readers how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives readers patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization.

The readers will begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Readers will then develop a solid statistical toolkit that will allow them to quickly understand gain intuition about the content of a dataset. Finally, the readers will gain hands-on experience implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages.

Finally, the reader will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations.

Table of Contents

Chapter 1: Gathering and Organizing Data
Handling data - Gopher style
Best practices for gathering and organizing data with Go
CSV files
JSON
SQL-like databases
Caching
Data versioning
References
Summary
Chapter 2: Matrices, Probability, and Statistics
Matrices and vectors
Statistics
Probability
References
Summary
Chapter 3: Evaluation and Validation
Evaluation
Validation
References
Summary
Chapter 4: Regression
Understanding regression model jargon
Linear regression
Multiple linear regression
Nonlinear and other types of regression
References
Summary
Chapter 5: Classification
Understanding classification model jargon
Logistic regression
k-nearest neighbors
Decision trees and random forests
Naive bayes
References
Summary
Chapter 6: Clustering
Understanding clustering model jargon
Measuring Distance or Similarity
Evaluating clustering techniques
k-means clustering
Other clustering techniques
References
Summary
Chapter 7: Time Series and Anomaly Detection
Representing time series data in Go
Understanding time series jargon
Statistics related to time series
Auto-regressive models for forecasting
Auto-regressive moving averages and other time series models
Anomaly detection
References
Summary
Chapter 8: Neural Networks and Deep Learning
Understanding neural net jargon
Building a simple neural network
Utilizing the simple neural network
Introducing deep learning
References
Summary
Chapter 9: Deploying and Distributing Analyses and Models
Running models reliably on remote machines
Building a scalable and reproducible machine learning pipeline
References
Summary
Chapter 10: Algorithms/Techniques Related to Machine Learning
Gradient descent
Entropy, information gain, and related methods
Backpropagation

What You Will Learn

  • Learn about data gathering, organization, parsing, and cleaning.
  • Explore matrices, linear algebra, statistics, and probability.
  • See how to evaluate and validate models.
  • Look at regression, classification, clustering.
  • Learn about neural networks and deep learning
  • Utilize times series models and anomaly detection.
  • Get to grip with techniques for deploying and distributing analyses and models.
  • Optimize machine learning workflow techniques

Authors

Table of Contents

Chapter 1: Gathering and Organizing Data
Handling data - Gopher style
Best practices for gathering and organizing data with Go
CSV files
JSON
SQL-like databases
Caching
Data versioning
References
Summary
Chapter 2: Matrices, Probability, and Statistics
Matrices and vectors
Statistics
Probability
References
Summary
Chapter 3: Evaluation and Validation
Evaluation
Validation
References
Summary
Chapter 4: Regression
Understanding regression model jargon
Linear regression
Multiple linear regression
Nonlinear and other types of regression
References
Summary
Chapter 5: Classification
Understanding classification model jargon
Logistic regression
k-nearest neighbors
Decision trees and random forests
Naive bayes
References
Summary
Chapter 6: Clustering
Understanding clustering model jargon
Measuring Distance or Similarity
Evaluating clustering techniques
k-means clustering
Other clustering techniques
References
Summary
Chapter 7: Time Series and Anomaly Detection
Representing time series data in Go
Understanding time series jargon
Statistics related to time series
Auto-regressive models for forecasting
Auto-regressive moving averages and other time series models
Anomaly detection
References
Summary
Chapter 8: Neural Networks and Deep Learning
Understanding neural net jargon
Building a simple neural network
Utilizing the simple neural network
Introducing deep learning
References
Summary
Chapter 9: Deploying and Distributing Analyses and Models
Running models reliably on remote machines
Building a scalable and reproducible machine learning pipeline
References
Summary
Chapter 10: Algorithms/Techniques Related to Machine Learning
Gradient descent
Entropy, information gain, and related methods
Backpropagation

Book Details

ISBN 139781785882104
Paperback304 pages
Read More
From 3 reviews

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