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Product typeBook
Published inSep 2020
Reading LevelBeginner
PublisherPackt
ISBN-139781838640859
Edition1st Edition
Languages
Python
Tools
Keras
Concepts
Deep Learning
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Author (1)
Dr. Pablo Rivas
Dr. Pablo Rivas
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Dr. Pablo Rivas

Dr. Pablo Rivas is an assistant professor of computer science at Baylor University in Texas. He worked in industry for a decade as a software engineer before becoming an academic. He is a senior member of the IEEE, ACM, and SIAM. He was formerly at NASA Goddard Space Flight Center performing research. He is an ally of women in technology, a deep learning evangelist, machine learning ethicist, and a proponent of the democratization of machine learning and artificial intelligence in general. He teaches machine learning and deep learning. Dr. Rivas is a published author and all his papers are related to machine learning, computer vision, and machine learning ethics. Dr. Rivas prefers Vim to Emacs and spaces to tabs.
Read more about Dr. Pablo Rivas

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A perceptron over non-linearly separable data

As we have discussed before, a perceptron will find a solution in finite time if the data is separable. However, how many iterations it will take to find a solution depends on how close the groups are to each other in the feature space.

Convergence is when the learning algorithm finds a solution or reaches a steady state that is acceptable to the designer of the learning model.

The following paragraphs will deal with convergence on different types of data: linearly separable and non-linearly separable.

Convergence on linearly separable data

For the particular dataset that we have been studying in this chapter, the separation between the two groups of data is a parameter that can be varied (this is usually a problem with real data). The parameter is class_sep and can take on a real number; for example:

X, y = make_classification(..., class_sep=2.0, ...)

This allows us to study how many iterations it takes, on average, for the perceptron algorithm...

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Deep Learning for Beginners
Published in: Sep 2020Publisher: PacktISBN-13: 9781838640859
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Author (1)

author image
Dr. Pablo Rivas

Dr. Pablo Rivas is an assistant professor of computer science at Baylor University in Texas. He worked in industry for a decade as a software engineer before becoming an academic. He is a senior member of the IEEE, ACM, and SIAM. He was formerly at NASA Goddard Space Flight Center performing research. He is an ally of women in technology, a deep learning evangelist, machine learning ethicist, and a proponent of the democratization of machine learning and artificial intelligence in general. He teaches machine learning and deep learning. Dr. Rivas is a published author and all his papers are related to machine learning, computer vision, and machine learning ethics. Dr. Rivas prefers Vim to Emacs and spaces to tabs.
Read more about Dr. Pablo Rivas

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