You're reading from Python Machine Learning - Third Edition

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Published inDec 2019

Reading LevelExpert

PublisherPackt

ISBN-139781789955750

Edition3rd Edition

Languages

Python

Tools

TensorFlow Keras

Concepts

Machine Learning

Authors (2):

Sebastian Raschka

Vahid Mirjalili

View More author details

Classifying handwritten digits

In the previous section, we covered a lot of the theory around NNs, which can be a little bit overwhelming if you are new to this topic. Before we continue with the discussion of the algorithm for learning the weights of the MLP model, backpropagation, let's take a short break from the theory and see an NN in action.

Additional resources on backpropagation

The NN theory can be quite complex; thus, it is recommended that you refer to two additional resources, which cover some of the concepts that we discuss in this chapter in more detail:

Chapter 6, Deep Feedforward Networks, Deep Learning, I. Goodfellow, Y. Bengio, and A. Courville, MIT Press, 2016 (Manuscripts freely accessible at http://www.deeplearningbook.org).
Pattern Recognition and Machine Learning, C. M. Bishop and others, Volume 1. Springer New York, 2006.
Lecture slides from the deep learning course at the University of Wisconsin–Madison...

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Python Machine Learning - Third Edition

Published in: Dec 2019Publisher: PacktISBN-13: 9781789955750

Authors (2)

Sebastian Raschka

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.
Read more about Sebastian Raschka

Vahid Mirjalili

Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University.
Read more about Vahid Mirjalili

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Classifying handwritten digits

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