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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

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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781838825461
Length 156 pages
Edition 1st Edition
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Authors (2):
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Ankush Garg Ankush Garg
Author Profile Icon Ankush Garg
Ankush Garg
Shruti Jadon Shruti Jadon
Author Profile Icon Shruti Jadon
Shruti Jadon
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Toc

Table of Contents (11) Chapters Close

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

Machine learning – historical overview

Machine learning is a program that, given a task (loss function), learns through experience (training data). With experience, that program learns to perform the given task to a desirable standard. During the 1960s, machine learning was majorly focused on creating different forms of data preprocessing filters. With the introduction of image filters, the focus then shifted toward computer vision, and major research work was undertaken in this domain during the 1990s and 2000s. After some stability in terms of traditional machine learning algorithms being developed, researchers moved to the probabilistic domain, as it became more promising with the introduction of high-dimensional data. Deep learning bloomed when it won the ImageNet Challenge in 2012, and has since taken on an important role in the field of data science.

Machine learning...

You have been reading a chapter from
Hands-On One-shot Learning with Python
Published in: Apr 2020
Publisher: Packt
ISBN-13: 9781838825461
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