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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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Toc

Machine Learning Libraries


While this book is an introduction to deep learning with Keras, as mentioned earlier, we will start by utilizing scikit-learn. This will help us establish the fundamentals of building a machine learning model using the Python programming language.

Similar to scikit-learn, Keras makes it easy to create models in the Python programming language through an easy-to-use API. However, the goal of Keras is for the creation and training of neural networks, rather than machine learning models in general. ANNs represent a large class of machine learning algorithms, and they are so called because their architecture resembles the neurons in the human brain. The Keras library has many general-purpose functions built in, such as optimizers, activation functions, and layer properties, so that users, like in scikit-learn, do not have to code these algorithms from scratch.

You have been reading a chapter from
Applied Deep Learning with Keras
Published in: Apr 2019
Publisher:
ISBN-13: 9781838555078
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