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Machine Learning With Go

You're reading from   Machine Learning With Go Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781785882104
Length 304 pages
Edition 1st Edition
Languages
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Author (1):
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Joseph Langstaff Whitenack Joseph Langstaff Whitenack
Author Profile Icon Joseph Langstaff Whitenack
Joseph Langstaff Whitenack
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Table of Contents (11) Chapters Close

Preface 1. Gathering and Organizing Data FREE CHAPTER 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

Gradient descent

In multiple examples (including those in Chapter 4, Regression and Chapter 5, Classification), we took advantage of an optimization technique called gradient descent. There are multiple variants of the gradient descent method, and, in general, you will see them pretty much everywhere in the machine learning world. Most prominently, they are utilized in the determination of optimal coefficients for algorithms such as linear or logistic regression, and thus, they often also play a role in more complicated techniques at least partially based on linear/logistic regression (such as neural networks).

The general idea of gradient descent methods is to determine a direction and magnitude of change in some parameters that will move you in the right direction to optimize some measure (such as error). Think about standing on some landscape. To move toward lower elevations...

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