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Applied Deep Learning with Keras

You're reading from   Applied Deep Learning with Keras Solve complex real-life problems with the simplicity of Keras

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Product type Paperback
Published in Apr 2019
Publisher
ISBN-13 9781838555078
Length 412 pages
Edition 1st Edition
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Authors (3):
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Matthew Moocarme Matthew Moocarme
Author Profile Icon Matthew Moocarme
Matthew Moocarme
Mahla Abdolahnejad Mahla Abdolahnejad
Author Profile Icon Mahla Abdolahnejad
Mahla Abdolahnejad
Ritesh Bhagwat Ritesh Bhagwat
Author Profile Icon Ritesh Bhagwat
Ritesh Bhagwat
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Introduction


In the previous chapter, we discussed some applications of machine learning and even built models with the scikit-learn Python package. In this chapter, we will continue learning how to build machine learning models and extend our knowledge to build an Artificial Neural Network (ANN) with the Keras package. (Remember that ANNs represent a large class of machine learning algorithms that are so called because their architecture resembles the neurons in the human brain.)

Keras is a machine learning library designed specifically for building neural networks. While scikit-learn functionality spans a broader area of machine learning algorithms, the functionality of scikit-learn for neural networks is minimal.

ANNs can be used for the same machine learning tasks as other algorithms that we have encountered, such as logistic regression for classification tasks, linear regression for regression problems, and k-means for clustering. Whenever we begin any machine learning problem, to determine...

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