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Book details
Format: epub
File Size: 17 MB
Print Length: 156 pages
Publisher: Packt Publishing; 1 edition (10 April 2020)
Sold by: Amazon Asia-Pacific Holdings Private Limited
Language: English
ASIN: B07S9QWNG2
Get to grips with building powerful deep learning models using scikit-learn and Keras
Key Features
Learn how you can speed up the deep learning process with one-shot learning
Use 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
Book Description
One-shot learning has been an active field of research for scientists trying to develop a cognitive machine that mimics human learning. As there are numerous theories about how humans perform one-shot learning, there are several methods to achieve it too.
Hands-On One-Shot Learning with Python will guide you through 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 you can use 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 this book, you’ll be well-versed with the different one-and few-shot learning methods and be able to build your own deep learning models using them.
What you will learn
Understand 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 Siamese neural networks and memory-augmented networks in Keras
Discover different forms of optimization algorithms that help to improve accuracy even with smaller volumes of data
Explore various computer vision and NLP-based one-shot learning architectures
Who This Book Is For
If you’re an AI researcher, or a machine learning or deep learning expert who wants to apply one-shot learning to reduce the overall time taken to train models, this book will serve as a solid introduction. Some Python programming experience is necessary to understand the concepts covered in this book.
Book details
Format: epub
File Size: 17 MB
Print Length: 156 pages
Publisher: Packt Publishing; 1 edition (10 April 2020)
Sold by: Amazon Asia-Pacific Holdings Private Limited
Language: English
ASIN: B07S9QWNG2
Get to grips with building powerful deep learning models using scikit-learn and Keras
Key Features
Learn how you can speed up the deep learning process with one-shot learning
Use 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
Book Description
One-shot learning has been an active field of research for scientists trying to develop a cognitive machine that mimics human learning. As there are numerous theories about how humans perform one-shot learning, there are several methods to achieve it too.
Hands-On One-Shot Learning with Python will guide you through 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 you can use 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 this book, you’ll be well-versed with the different one-and few-shot learning methods and be able to build your own deep learning models using them.
What you will learn
Understand 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 Siamese neural networks and memory-augmented networks in Keras
Discover different forms of optimization algorithms that help to improve accuracy even with smaller volumes of data
Explore various computer vision and NLP-based one-shot learning architectures
Who This Book Is For
If you’re an AI researcher, or a machine learning or deep learning expert who wants to apply one-shot learning to reduce the overall time taken to train models, this book will serve as a solid introduction. Some Python programming experience is necessary to understand the concepts covered in this book.