Get to grips with building powerful deep learning models using PyTorch and scikit-learn
Key Features
Learn how you can speed up the deep learning process with one-shot learning
Use Python and PyTorch to build state-of-the-art one-shot learning models
Explore architectures such as Siamese networks, memory-augmented neural networks, model-agnostic meta-learning, and discriminative k-shot learning
Book DescriptionOne-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.
What you will learn
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
Who this book is forIf you're an AI researcher or a machine learning or deep learning expert looking to explore one-shot learning, this book is for you. It will help you get started with implementing various one-shot techniques to train models faster. Some Python programming experience is necessary to understand the concepts covered in this book.
Key Features
Learn how you can speed up the deep learning process with one-shot learning
Use Python and PyTorch to build state-of-the-art one-shot learning models
Explore architectures such as Siamese networks, memory-augmented neural networks, model-agnostic meta-learning, and discriminative k-shot learning
Book DescriptionOne-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.
What you will learn
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
Who this book is forIf you're an AI researcher or a machine learning or deep learning expert looking to explore one-shot learning, this book is for you. It will help you get started with implementing various one-shot techniques to train models faster. Some Python programming experience is necessary to understand the concepts covered in this book.
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