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Python Deep Learning

You're reading from   Python Deep Learning Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789348460
Length 386 pages
Edition 2nd Edition
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Authors (5):
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Gianmario Spacagna Gianmario Spacagna
Author Profile Icon Gianmario Spacagna
Gianmario Spacagna
Daniel Slater Daniel Slater
Author Profile Icon Daniel Slater
Daniel Slater
Valentino Zocca Valentino Zocca
Author Profile Icon Valentino Zocca
Valentino Zocca
Peter Roelants Peter Roelants
Author Profile Icon Peter Roelants
Peter Roelants
Ivan Vasilev Ivan Vasilev
Author Profile Icon Ivan Vasilev
Ivan Vasilev
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Toc

Table of Contents (12) Chapters Close

Preface 1. Machine Learning - an Introduction FREE CHAPTER 2. Neural Networks 3. Deep Learning Fundamentals 4. Computer Vision with Convolutional Networks 5. Advanced Computer Vision 6. Generating Images with GANs and VAEs 7. Recurrent Neural Networks and Language Models 8. Reinforcement Learning Theory 9. Deep Reinforcement Learning for Games 10. Deep Learning in Autonomous Vehicles 11. Other Books You May Enjoy

Advanced network architectures

We are now familiar with the powerful technique of transfer learning. In this section, we'll discuss some recent and popular network architectures that go beyond the ones we've seen so far. You'll be able to use these networks as pre-trained models in a transfer learning scenario, or if you are brave enough, train them from scratch to solve your tasks.

VGG

The first architecture we're going to discuss is VGG (from Oxford's Visual Geometry Group, https://arxiv.org/abs/1409.1556). It was introduced in 2014, when it became a runner-up in the ImageNet challenge of that year. The VGG family of networks remains popular today and is often used as a benchmark against newer architectures...

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