When humans make inferences about unseen data, they make use of strong prior knowledge (or inductive bias) about related events they've seen, heard, touched, or experienced. For example, an infant who has grown up with a dog may see a cat for the first time and immediately infer that it shares similarities with the pet-like temperament of the household dog. Of course, cats and dogs as species and individuals are wildly different; however, it's fair to say that a cat is more similar to a dog than other random things the child has experienced—such as food. Humans, as opposed to machine learning models, don't need thousands of examples of cats to learn that category from scratch once they have already learned to recognize a dog. The human brain has this innate capability of learning to learn, which is related to transfer learning...
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Table Of Contents
Hands-On One-shot Learning with Python
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Hands-On One-shot Learning with Python
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Overview of this book
One-shot learning has been an active field of research for scientists trying to develop a cognitive machine that mimics human learning. With this book, you'll explore key approaches to one-shot learning, such as metrics-based, model-based, and optimization-based techniques, all with the help of practical examples.
Hands-On One-shot Learning with Python will guide you through the exploration and design of deep learning models that can obtain information about an object from one or just a few training samples. 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've got to grips with the core principles, you'll explore real-world examples and implementations of one-shot learning using PyTorch 1.x on datasets such as Omniglot and MiniImageNet. Finally, you'll explore generative modeling-based methods and discover the key considerations for building systems that exhibit human-level intelligence.
By the end of this book, you'll be well-versed with the different one- and few-shot learning methods and be able to use them to build your own deep learning models.
Table of Contents (11 chapters)
Preface
Section 1: One-shot Learning Introduction
Free Chapter
Introduction to One-shot Learning
Section 2: Deep Learning Architectures
Metrics-Based Methods
Model-Based Methods
Optimization-Based Methods
Section 3: Other Methods and Conclusion
Generative Modeling-Based Methods
Conclusions and Other Approaches
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