One-shot learning has been an active field of research for scientists trying to develop a cognitive machine that mimics human learning. With this course, 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.
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 course 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 course, 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.