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Hands-On Transfer Learning with Python

You're reading from   Hands-On Transfer Learning with Python Implement advanced deep learning and neural network models using TensorFlow and Keras

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781788831307
Length 438 pages
Edition 1st Edition
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Authors (4):
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Nitin Panwar Nitin Panwar
Author Profile Icon Nitin Panwar
Nitin Panwar
Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
Tamoghna Ghosh Tamoghna Ghosh
Author Profile Icon Tamoghna Ghosh
Tamoghna Ghosh
Dipanjan Sarkar Dipanjan Sarkar
Author Profile Icon Dipanjan Sarkar
Dipanjan Sarkar
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Toc

Table of Contents (14) Chapters Close

Preface 1. Machine Learning Fundamentals FREE CHAPTER 2. Deep Learning Essentials 3. Understanding Deep Learning Architectures 4. Transfer Learning Fundamentals 5. Unleashing the Power of Transfer Learning 6. Image Recognition and Classification 7. Text Document Categorization 8. Audio Event Identification and Classification 9. DeepDream 10. Style Transfer 11. Automated Image Caption Generator 12. Image Colorization 13. Other Books You May Enjoy

Deep learning frameworks

One of the primary reasons for the widespread popularity and adoption of deep learning is the Python deep learning ecosystem, which consists of easy-to-use open source deep learning frameworks. However, the deep learning landscape is rapidly changing, considering how new frameworks keep getting launched and older ones reach the end of their life. Deep learning enthusiasts might know that Theano was the first and most popular deep learning framework, created by MILA (https://mila.quebec/), which was headed by Yoshua Bengio. Unfortunately, it was recently announced that further development and support for Theano is ending after the launch of its latest version (1.0) in 2017. Hence, it is of paramount importance to understand what frameworks are out there that can be leveraged to implement and solve deep learning. Another point to remember here is that several...

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