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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

Building a deep learning audio event identifier

We will now look at a strategy using which we can build an actual audio event identifier by leveraging the classification model we built in the previous section. This will enable us to take any new audio file and predict which category it might belong to by making use of the entire workflow we defined in this chapter, starting from building the base feature maps, extracting features using the VGG-16 model, and then leveraging our classification model to make a prediction. The code snippets used in this section are also available in the Prediction Pipeline.ipynb Jupyter Notebook in case you want to run the examples yourself. The Notebook contains the AudioIdentifier class, which we have created by reusing all the components we have built in the previous sections of this chapter. Do refer to the Notebook to access the full code for...

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