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Hands-On Deep Learning Architectures with Python

You're reading from   Hands-On Deep Learning Architectures with Python Create deep neural networks to solve computational problems using TensorFlow and Keras

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781788998086
Length 316 pages
Edition 1st Edition
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Authors (2):
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Saransh Mehta Saransh Mehta
Author Profile Icon Saransh Mehta
Saransh Mehta
Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: The Elements of Deep Learning FREE CHAPTER
2. Getting Started with Deep Learning 3. Deep Feedforward Networks 4. Restricted Boltzmann Machines and Autoencoders 5. Section 2: Convolutional Neural Networks
6. CNN Architecture 7. Mobile Neural Networks and CNNs 8. Section 3: Sequence Modeling
9. Recurrent Neural Networks 10. Section 4: Generative Adversarial Networks (GANs)
11. Generative Adversarial Networks 12. Section 5: The Future of Deep Learning and Advanced Artificial Intelligence
13. New Trends of Deep Learning 14. Other Books You May Enjoy

Meta-learning

Meta-learning is another exciting trend of research in deep learning. It goes beyond training on a huge dataset for a specific task, like traditional learning does. It tries to mimic a humans' learning process by leveraging a past experience that has been learned from a distribution of tasks. It can achieve good performance, even with just a handful of training samples. However, conventional deep learning methods are not able to do so. 

One big challenge in deep learning – training data

You might have seen the following plot, comparing the performance between deep learning and traditional machine learning algorithms given various amount of training data:

With only a small amount of training...

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