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Hands-On One-shot Learning with Python

You're reading from Hands-On One-shot Learning with Python Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch

Product type Paperback

Published in Apr 2020

Publisher Packt

ISBN-13 9781838825461

Length 156 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Deep Learning

Authors (2):

Jadon

Garg

View More author details

Table of Contents (11) Chapters

Preface

1. Section 1: One-shot Learning Introduction

2. Introduction to One-shot Learning FREE CHAPTER

3. Section 2: Deep Learning Architectures

4. Metrics-Based Methods

5. Model-Based Methods

6. Optimization-Based Methods

7. Section 3: Other Methods and Conclusion

8. Generative Modeling-Based Methods

9. Conclusions and Other Approaches

10. Other Books You May Enjoy

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Questions

What are neural Turing machines, and how do they help in learning?
How does the memory matrix help the model to learn faster?
How does fragmentation among the meta-learner and the base learner help the architecture to learn one-shot learning?

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Authors (2)

Jadon

Jadon

Shruti Jadon is currently working as a Machine Learning Software Engineer at Juniper Networks, Sunnyvale and visiting Researcher at Rhode Island Hospital (Brown University). She has obtained her master's degree in Computer Science from University of Massachusetts, Amherst. Her research interests include deep learning architectures, computer vision, and convex optimization. In the past, she has worked at Autodesk, Quantiphi, SAP Labs, and Snapdeal.

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Garg

Garg

Ankush Garg is currently working as a Software Engineer in the auto-translation team at Google, Mountain View. He has obtained his master's degree in Computer Science from the University of Massachusetts, Amherst and Bachelor's at NSIT, Delhi. His research interests include language modeling, model compression, and optimization. In the past, he has worked as a Software Engineer at Amazon, India.

See other products by Garg

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