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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):

Ankush Garg

Shruti Jadon

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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 similarity metrics? Why does cosine similarity work best?
Why do matching networks use the LSTM architecture to obtain embeddings?
What are the disadvantages associated with the contrastive loss function, and how does the triplet loss function assist in solving it?
What is the curse of dimensionality? How can we deal with it?

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Authors (2)

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

Nishant Garg has over 17 years' software architecture and development experience in various technologies, such as Java Enterprise Edition, SOA, Spring, Hadoop, Hive, Flume, Sqoop, Oozie, Spark, Shark, YARN, Impala, Kafka, Storm, Solr/Lucene, NoSQL databases (such as HBase, Cassandra, and MongoDB), and MPP databases (such as GreenPlum). He received his MS in software systems from the Birla Institute of Technology and Science, Pilani, India, and is currently working as a technical architect for the Big Data RandD Group with Impetus Infotech Pvt. Ltd. Previously, Nishant has enjoyed working with some of the most recognizable names in IT services and financial industries, employing full software life cycle methodologies such as Agile and SCRUM. Nishant has also undertaken many speaking engagements on big data technologies and is also the author of Apache Kafka and HBase Essentials, Packt Publishing.

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