Hands-On One-shot Learning with Python: Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch
bGet to grips with building powerful deep learning models using PyTorch and scikit-learn/b h4Key Features/h4 ulliLearn how you can speed up the deep learning process with one-shot learning /li liUse Python and PyTorch to build state-of-the-art one-shot learning models /li liExplore architectures suc...
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham
Packt Publishing Limited
2020
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| Edition: | 1 |
| Subjects: | |
| Summary: | bGet to grips with building powerful deep learning models using PyTorch and scikit-learn/b h4Key Features/h4 ulliLearn how you can speed up the deep learning process with one-shot learning /li liUse Python and PyTorch to build state-of-the-art one-shot learning models /li liExplore architectures such as Siamese networks, memory-augmented neural networks, model-agnostic meta-learning, and discriminative k-shot learning/li/ul h4Book Description/h4 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. h4What you will learn/h4 ulliGet to grips with the fundamental concepts of one- and few-shot learning /li liWork with different deep learning architectures for one-shot learning /li liUnderstand when to use one-shot and transfer learning, respectively /li liStudy the Bayesian network approach for one-shot learning /li liImplement one-shot learning approaches based on metrics, models, and optimization in PyTorch /li liDiscover different optimization algorithms that help to improve accuracy even with smaller volumes of data /li liExplore various one-shot learning architectures based on classification and regression/li/ul h4Who this book is for/h4 If 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 |
| Physical Description: | 1 Online-Ressource (156 Seiten) |
| ISBN: | 9781838824877 |
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| 520 | |a bGet to grips with building powerful deep learning models using PyTorch and scikit-learn/b h4Key Features/h4 ulliLearn how you can speed up the deep learning process with one-shot learning /li liUse Python and PyTorch to build state-of-the-art one-shot learning models /li liExplore architectures such as Siamese networks, memory-augmented neural networks, model-agnostic meta-learning, and discriminative k-shot learning/li/ul h4Book Description/h4 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. | ||
| 520 | |a 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. | ||
| 520 | |a h4What you will learn/h4 ulliGet to grips with the fundamental concepts of one- and few-shot learning /li liWork with different deep learning architectures for one-shot learning /li liUnderstand when to use one-shot and transfer learning, respectively /li liStudy the Bayesian network approach for one-shot learning /li liImplement one-shot learning approaches based on metrics, models, and optimization in PyTorch /li liDiscover different optimization algorithms that help to improve accuracy even with smaller volumes of data /li liExplore various one-shot learning architectures based on classification and regression/li/ul h4Who this book is for/h4 If 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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| spelling | Jadon, Shruti Verfasser aut Hands-On One-shot Learning with Python Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch Jadon, Shruti 1 Birmingham Packt Publishing Limited 2020 1 Online-Ressource (156 Seiten) txt rdacontent c rdamedia cr rdacarrier bGet to grips with building powerful deep learning models using PyTorch and scikit-learn/b h4Key Features/h4 ulliLearn how you can speed up the deep learning process with one-shot learning /li liUse Python and PyTorch to build state-of-the-art one-shot learning models /li liExplore architectures such as Siamese networks, memory-augmented neural networks, model-agnostic meta-learning, and discriminative k-shot learning/li/ul h4Book Description/h4 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. h4What you will learn/h4 ulliGet to grips with the fundamental concepts of one- and few-shot learning /li liWork with different deep learning architectures for one-shot learning /li liUnderstand when to use one-shot and transfer learning, respectively /li liStudy the Bayesian network approach for one-shot learning /li liImplement one-shot learning approaches based on metrics, models, and optimization in PyTorch /li liDiscover different optimization algorithms that help to improve accuracy even with smaller volumes of data /li liExplore various one-shot learning architectures based on classification and regression/li/ul h4Who this book is for/h4 If 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 COMPUTERS / Neural Networks COMPUTERS / Machine Theory Garg, Ankush Sonstige oth |
| spellingShingle | Jadon, Shruti Hands-On One-shot Learning with Python Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch COMPUTERS / Neural Networks COMPUTERS / Machine Theory |
| title | Hands-On One-shot Learning with Python Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch |
| title_auth | Hands-On One-shot Learning with Python Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch |
| title_exact_search | Hands-On One-shot Learning with Python Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch |
| title_exact_search_txtP | Hands-On One-shot Learning with Python Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch |
| title_full | Hands-On One-shot Learning with Python Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch Jadon, Shruti |
| title_fullStr | Hands-On One-shot Learning with Python Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch Jadon, Shruti |
| title_full_unstemmed | Hands-On One-shot Learning with Python Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch Jadon, Shruti |
| title_short | Hands-On One-shot Learning with Python |
| title_sort | hands on one shot learning with python learn to implement fast and accurate deep learning models with fewer training samples using pytorch |
| title_sub | Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch |
| topic | COMPUTERS / Neural Networks COMPUTERS / Machine Theory |
| topic_facet | COMPUTERS / Neural Networks COMPUTERS / Machine Theory |
| work_keys_str_mv | AT jadonshruti handsononeshotlearningwithpythonlearntoimplementfastandaccuratedeeplearningmodelswithfewertrainingsamplesusingpytorch AT gargankush handsononeshotlearningwithpythonlearntoimplementfastandaccuratedeeplearningmodelswithfewertrainingsamplesusingpytorch |