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Hands-On Automated Machine Learning

You're reading from  Hands-On Automated Machine Learning

Product type Book
Published in Apr 2018
Publisher Packt
ISBN-13 9781788629898
Pages 282 pages
Edition 1st Edition
Languages
Authors (2):
Sibanjan Das Sibanjan Das
Profile icon Sibanjan Das
Umit Mert Cakmak Umit Mert Cakmak
Profile icon Umit Mert Cakmak
View More author details
Toc

Table of Contents (10) Chapters close

Preface 1. Introduction to AutoML 2. Introduction to Machine Learning Using Python 3. Data Preprocessing 4. Automated Algorithm Selection 5. Hyperparameter Optimization 6. Creating AutoML Pipelines 7. Dive into Deep Learning 8. Critical Aspects of ML and Data Science Projects 9. Other Books You May Enjoy

Autoencoders

An autoencoder is a type of DL which can be used for unsupervised learning. It is similar to other dimensionality reduction techniques such as Principal Component Analysis (PCA) which we studied earlier. However, PCA projects data from higher dimensions to lower dimensions using linear transformation, but autoencoders use non-linear transformations.

In an autoencoder, there are two parts to its structure:

  • Encoder: This part compresses the input into a fewer number of elements or bits. The input is compressed to the maximum point, which is known as latent space or bottleneck. These compressed bits are known as encoded bits.
  • Decoder: The decoder tries to reconstruct the input based on the encoded bits. If the decoder can reproduce the exact input from the encoded bits, then we can say that there was a perfect encoding. However, it is an ideal case scenario and does...
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