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Go Machine Learning Projects

You're reading from   Go Machine Learning Projects Eight projects demonstrating end-to-end machine learning and predictive analytics applications in Go

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788993401
Length 348 pages
Edition 1st Edition
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Author (1):
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Xuanyi Chew Xuanyi Chew
Author Profile Icon Xuanyi Chew
Xuanyi Chew
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Table of Contents (12) Chapters Close

Preface 1. How to Solve All Machine Learning Problems 2. Linear Regression - House Price Prediction FREE CHAPTER 3. Classification - Spam Email Detection 4. Decomposing CO2 Trends Using Time Series Analysis 5. Clean Up Your Personal Twitter Timeline by Clustering Tweets 6. Neural Networks - MNIST Handwriting Recognition 7. Convolutional Neural Networks - MNIST Handwriting Recognition 8. Basic Facial Detection 9. Hot Dog or Not Hot Dog - Using External Services 10. What's Next? 11. Other Books You May Enjoy

Running the neural network

Observe that up to this point, we've merely described the computations we need to perform. The neural network doesn't actually run; this is simply a description on the neural network to run.

We need to be able to evaluate the mathematical expression. In order to do so, we need to compile the expression into a program that can be executed. Here's the code to do it:

    vm := gorgonia.NewTapeMachine(g, 
gorgonia.WithPrecompiled(prog, locMap),
gorgonia.BindDualValues(m.learnables()...))
solver := gorgonia.NewRMSPropSolver(gorgonia.WithBatchSize(float64(bs)))
defer vm.Close()

It's not strictly necessary to call gorgonia.Compile(g). This was done for pedagogical reasons, to showcase that the mathematical expression can indeed be compiled down into an assembly-like program. In production systems, I often just do something...

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