Hands-On One-shot Learning with Python
- Paperback: 156 pages
- Publisher: WOW! eBook (April 10, 2020)
- Language: English
- ISBN-10: 1838825460
- ISBN-13: 978-1838825461
Hands-On One-shot Learning with Python: Get to grips with building powerful deep learning models using PyTorch and scikit-learn
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.
- Get to grips with 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 one-shot learning approaches based on metrics, models, and optimization in PyTorch
- Discover different optimization algorithms that help to improve accuracy even with smaller volumes of data
- Explore various one-shot learning architectures based on classification and regression
By the end of this Hands-On One-shot Learning with Python 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.