The goal of MANNs is to excel at one-shot learning tasks. The NMT controller, as we read earlier, uses both content-based addressing and location-based addressing. On the other hand, the MANN controller uses only content-based addressing. There are two reasons for this. One reason is that location-based addressing is not required for one-shot learning tasks. In this task, for a given input, there are only two actions that a controller might need to take and both actions are content dependent and not location dependent. One action is taken when the input is very similar to a previously seen input, in which case we can update the current contents of the memory. The other action is taken when the current input is not similar to previously seen inputs, in which case we don't want to overwrite the recent information; instead, we write to the least...
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.
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Hands-On One-shot Learning with Python
Table of Contents (11 chapters)
Preface
Section 1: One-shot Learning Introduction
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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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