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Hands-On Meta Learning with Python

You're reading from   Hands-On Meta Learning with Python Meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789534207
Length 226 pages
Edition 1st Edition
Languages
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Author (1):
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Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
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Table of Contents (12) Chapters Close

Preface 1. Introduction to Meta Learning 2. Face and Audio Recognition Using Siamese Networks FREE CHAPTER 3. Prototypical Networks and Their Variants 4. Relation and Matching Networks Using TensorFlow 5. Memory-Augmented Neural Networks 6. MAML and Its Variants 7. Meta-SGD and Reptile 8. Gradient Agreement as an Optimization Objective 9. Recent Advancements and Next Steps 10. Assessments 11. Other Books You May Enjoy

Building an audio recognition model using siamese networks

In the last tutorial, we saw how to use siamese networks to recognize a face. Now we will see how to use siamese networks to recognize audio. We will train our network to differentiate between the sound of a dog and the sound of a cat. The dataset of cat and dog audio can be downloaded from here: https://www.kaggle.com/mmoreaux/audio-cats-and-dogs#cats_dogs.zip.

Once we have downloaded the data, we fragment our data into three folders: Dogs, Sub_dogs, and Cats. In Dogs and Sub_dogs, we place the dog's barking audio and in the Cats folder, we place the cat's audio. The objective of our network is to recognize whether the audio is a dog's barking or some different sound. As we know, for a siamese network, we need to feed input as a pair; we select an audio from the Dogs and Sub_dogs folders and mark them as...

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Hands-On Meta Learning with Python
Published in: Dec 2018
Publisher: Packt
ISBN-13: 9781789534207
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