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Go Machine Learning Projects

You're reading from  Go Machine Learning Projects

Product type Book
Published in Nov 2018
Publisher Packt
ISBN-13 9781788993401
Pages 348 pages
Edition 1st Edition
Languages
Author (1):
Xuanyi Chew Xuanyi Chew
Profile icon Xuanyi Chew

Table of Contents (12) Chapters

Preface 1. How to Solve All Machine Learning Problems 2. Linear Regression - House Price Prediction 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

Tweaking the program

If you have been following up to this point, you may get very poor results from all the clustering algorithms. I'd like to remind you that the stated objective of this book in general is to impart an understanding of what it's like to do data science in Go. For the most part, I have advocated a method that can be described as think hard about the problem, then write the answers down. But the reality is that often trial and error are required.

The solution that works for me on my Twitter home timeline may not work for you. For example, this code works well on a friend's Twitter feed. Why is this? He follows a lot of similar people who talk about similar things at the same time. It's a little harder to cluster tweets in my Twitter home feed. I follow a diverse array of people. The people I follow don't have set schedules of tweeting...

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