Book Image

Hands-On Automated Machine Learning

By : Sibanjan Das, Umit Mert Cakmak
Book Image

Hands-On Automated Machine Learning

By: Sibanjan Das, Umit Mert Cakmak

Overview of this book

AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible. In this book, you’ll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning. By the end of this book, you will have a clearer understanding of the different aspects of automated Machine Learning, and you’ll be able to incorporate automation tasks using practical datasets. You can leverage your learning from this book to implement Machine Learning in your projects and get a step closer to winning various machine learning competitions.
Table of Contents (10 chapters)

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...