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Machine Learning With Go

You're reading from  Machine Learning With Go

Product type Book
Published in Sep 2017
Publisher Packt
ISBN-13 9781785882104
Pages 304 pages
Edition 1st Edition
Languages

Table of Contents (11) Chapters

Preface 1. Gathering and Organizing Data 2. Matrices, Probability, and Statistics 3. Evaluation and Validation 4. Regression 5. Classification 6. Clustering 7. Time Series and Anomaly Detection 8. Neural Networks and Deep Learning 9. Deploying and Distributing Analyses and Models 10. Algorithms/Techniques Related to Machine Learning

What you need for this book

To run the examples in this book and experiment with the techniques covered in the book, you will generally need the following:

  • Access to a bash-like shell.
  • A complete Go environment including Go, an editor, and related default or custom environment variables defined. You can, for example, follow this guide at https://www.goinggo.net/2016/05/installing-go-and-your-workspace.html.
  • Various Go dependencies. These can be obtained as they are needed via go get ....

Then, to run the examples related to some of the advanced topics, such as data pipelining and deep learning, you will need a few additional things:

  • An installation or deployment of Pachyderm. You can follow these docs to get Pachyderm up and running locally or in the cloud, http://pachyderm.readthedocs.io/en/latest/.
  • A working Docker installation (https://www.docker.com/community-edition#/download).
  • An installation of TensorFlow. To install TensorFlow locally, you can follow this guide at https://www.tensorflow.org/install/.
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