Hands-On One-shot Learning with Python

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  • Understand the fundamental concepts of one/few-shot learning
  • Work with different deep learning architectures for one-shot learning
  • Know when to use one-shot and when to use transfer learning
  • Study the Bayesian network approach to one-shot learning
  • Implement Siamese networks and memory augmented networks in Keras
  • Explore various computer vision and NLP-based one-shot learning architectures

One-shot learning has been an active field of research for scientists trying to develop a cognitive machine close to humans in terms of learning. As there are numerous theories about how humans perform one-shot learning, there are a lot of different methods to achieve it.

Hands-On One-Shot Learning with Python will guide you in 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 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 the book, you’ll have a thorough understanding of one/few-shot learning methods and be able to build your own deep learning models using them.

  • Learn how you can speed up the deep learning process with one-shot learning
  • Leverage the power of Python and Keras to build state-of-the-art one-shot learning models
  • Explore one-shot learning architectures such as Siamese networks and memory augmented networks
Page Count 122
Course Length 3 hours 39 minutes
ISBN 9781838825461
Date Of Publication 10 Apr 2020


Shruti Jadon

Shruti Jadon is currently working as Visiting Researcher at Rhode Island Hospital(Brown University). She has obtained her Masters’ Degree in Computer Science from the University of Massachusetts, Amherst. During her Masters’ program, she worked under Professor Erik L. Miller and Professor Andrew McCallum. In Past, she has worked at Quantiphi, John Hopkins Medical School, Autodesk, and SAP.

Ankush Garg