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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

MachineBox

As mentioned, we will not focus on the math going on behind the scenes of face detection. Instead, we will use an external service to perform the recognition for us. The external service is MachineBox. What it does is quite clever. Instead of having to write your own deep learning algorithms, MachineBox packages up the commonly-used deep learning functionalities into containers, and you simply just use them straight out of the box. What do I mean by commonly-used deep learning functionalities? Nowadays people are relying more and more on deep learning for tasks such as facial recognition.

Just like Viola-Jones in the early 2000s, there are only a few commonly used models—we used the Haar-like cascades generated by Rainer Lienhart in 2002. The same is becoming true of deep learning models, and I shall talk more about the implications of that in the next chapter...

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