Hands-On One-shot Learning with Python:Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch


Hands-On One-shot Learning with Python:A practical guide to implementing fast and accurate deep learning models with fewer training samples
by:Shruti Jadon,Ankush Garg
pages 页数:156 pages
Publisher Finelybook 出版社:Packt Publishing (10 April 2020)
Language 语言:English
ISBN-10 书号:1838825460
ISBN-13 书号:9781838825461

Book Description
Get to grips with building powerful deep learning models using scikit-learn and Keras
One-shot learning has been an active field of research for scientists trying to develop a cognitive machine that mimics human learning. As there are numerous theories about how humans perform one-shot learning, there are several methods to achieve it too.
Hands-On One-Shot Learning with Python will guide you through exploring and designing deep learning models that can grasp information about an object from one or only a few training examples. The book begins with an overview of deep learning and one-shot learning and then introduces you to the different methods you can use to achieve it, such as deep learning architectures and probabilistic models. Once you are well versed with the core principles, you’ll explore some real-world examples and implementations of one-shot learning using scikit-learn and Keras 2.x in computer vision (CV), and natural language processing (NLP).
By the end of this book, you’ll be well-versed with the different one-and few-shot learning methods and be able to build your own deep learning models using them.
What you will learn

Understand the fundamental concepts of one-and few-shot learning
Work with different deep learning architectures for one-shot learning
Understand when to use one-shot and transfer learning respectively
Study the Bayesian network approach for one-shot learning
Implement Siamese neural networks and memory-augmented networks in Keras
Discover different forms of optimization algorithms that help to improve accuracy even with smaller volumes of data
Explore various computer vision and NLP-based one-shot learning architectures
Table of Contents
Preface
Section 1:One-shot Learning Introduction
Chapter 1:Introduction to One-shot Learning
Section 2:Deep Learning Architectures
Chapter 2:Metrics-Based Methods
Chapter 3:Model-Based Methods
Chapter 4:Optimization-Based Methods
Section 3:Other Methods and Conclusion
Chapter 5:Generative Modeling-Based Methods
Chapter 6:Conclusions and Other Approaches
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Index

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