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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 2. Matrices, Probability, and Statistics FREE CHAPTER 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

Understanding neural net jargon

There are a huge variety of neural network flavors, and each of these flavors has its own set of jargon. However, there is some common jargon that we should know regardless of the type of neural network that we are utilizing. This jargon is presented in the following points:

  • Nodes, perceptrons, or neurons: These interchangeable terms refer to the basic building blocks of a neural network. Each node or neuron takes in input data and performs an operation on this data. After performing the operation, the node/neuron may or may not pass the results of the operation on to other nodes/neurons.
  • Activation: The output or values associated with the operation of a node.
  • Activation function: The definition of the function that transforms the inputs to a node into the output, or activation.
  • Weights or biases: These values define the relationships between...
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