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Deep Learning Essentials

You're reading from   Deep Learning Essentials Your hands-on guide to the fundamentals of deep learning and neural network modeling

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781785880360
Length 284 pages
Edition 1st Edition
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Authors (3):
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Wei Di Wei Di
Author Profile Icon Wei Di
Wei Di
Anurag Bhardwaj Anurag Bhardwaj
Author Profile Icon Anurag Bhardwaj
Anurag Bhardwaj
Jianing Wei Jianing Wei
Author Profile Icon Jianing Wei
Jianing Wei
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Toc

Table of Contents (12) Chapters Close

Preface 1. Why Deep Learning? FREE CHAPTER 2. Getting Yourself Ready for Deep Learning 3. Getting Started with Neural Networks 4. Deep Learning in Computer Vision 5. NLP - Vector Representation 6. Advanced Natural Language Processing 7. Multimodality 8. Deep Reinforcement Learning 9. Deep Learning Hacks 10. Deep Learning Trends 11. Other Books You May Enjoy

How a network learns

Suppose we have a two-layer network. Let’s represent inputs/outputs with , and the two layers by states, that is, the connection weights with bias value: and . We will also use σ as the activation function.

Weight initialization

After the configuration of the network, training starts with initializing the weights' values. A proper weight initialization is important in the sense that all the training does is to adjust the coefficients to best capture the patterns from data in order to successfully output the approximation of the target value. In most cases, weights are initialized randomly. In some finely-tuned settings, weights are initialized using a pre-trained model.

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