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Java Deep Learning Essentials

You're reading from   Java Deep Learning Essentials Unlocking the next generation of predictive power

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Product type Paperback
Published in May 2016
Publisher Packt
ISBN-13 9781785282195
Length 254 pages
Edition 1st Edition
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Author (1):
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Yusuke Sugomori Yusuke Sugomori
Author Profile Icon Yusuke Sugomori
Yusuke Sugomori
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Toc

Implementing from scratch versus a library/framework

We implemented the machine learning algorithms of neural networks in Chapter 2, Algorithms for Machine Learning – Preparing for Deep Learning, and many deep learning algorithms from scratch in Chapter 3, Deep Belief Nets and Stacked Denoising Autoencoders and Chapter 4, Dropout and Convolutional Neural Networks. Of course, we can apply our own code to practical applications with some customizations, but we have to be careful when we want to utilize them because we can't deny the possibility that they might cause several problems in the future. What could they be? Here are the possible situations:

  • The code we wrote has some missing parameters for better optimization because we implemented just the essence of the algorithms for simplicity and so you better understand the concepts. While you can still train and optimize the model with them, you could get higher precision rates by adding another parameter of your own implementation...
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