Hands-On Meta Learning with Python
Meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow
Published on 23. September 2024
226 pages
978-1-78953-702-4 (ISBN)
Description
No detailed description available for "Hands-On Meta Learning with Python".
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Edition
Digital original
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Digital original
File size
24,46 MB
ISBN-13
978-1-78953-702-4 (9781789537024)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
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Additional editions
Sudharsan Ravichandiran
Hands-On Meta Learning with Python
Meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow
Book
12/2018
Packt Publishing
€50.50
Shipment within 3-4 weeks
Person
Ravichandiran Sudharsan :
Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.
Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.
Content
Table of Contents
Introduction to Meta Learning
Face and Audio Recognition using Siamese Network
Prototypical Network and its variants
Building Matching and Relation Network using Tensorflow
Memory Augmented Networks
MAML and its variants
Meta-SGD and Reptile ALgorithm
Gradient Agreement as an Optimization Objective
Recent Advancements and Next Steps
Introduction to Meta Learning
Face and Audio Recognition using Siamese Network
Prototypical Network and its variants
Building Matching and Relation Network using Tensorflow
Memory Augmented Networks
MAML and its variants
Meta-SGD and Reptile ALgorithm
Gradient Agreement as an Optimization Objective
Recent Advancements and Next Steps
