Hands-On One-shot Learning with Python
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.
|Course Length||3 hours 39 minutes|
|Date Of Publication||10 Apr 2020|
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