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Hands-on meta learning with Python : meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow

Hands-on meta learning with Python : meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow (Loan 1 times)

Material type
단행본
Personal Author
Ravichandiran, Sudharsan.
Title Statement
Hands-on meta learning with Python : meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow / Sudharsan Ravichandiran.
Publication, Distribution, etc
Birmingham :   Packt,   2018.  
Physical Medium
iv, 215 p. : ill. ; 24 cm.
ISBN
9781789534207
Bibliography, Etc. Note
Includes bibliographical references and index.
Subject Added Entry-Topical Term
Artificial intelligence. Machine learning. Python (Computer program language).
000 00000cam u2200205 a 4500
001 000046087568
005 20210728130204
008 210727s2018 enka b 001 0 eng d
020 ▼a 9781789534207
035 ▼a (KERIS)BIB000015304035
040 ▼a 211029 ▼c 211029 ▼d 211009
082 0 4 ▼a 005.133 ▼2 23
084 ▼a 005.133 ▼2 DDCK
090 ▼a 005.133 ▼b R256h
100 1 ▼a Ravichandiran, Sudharsan.
245 1 0 ▼a Hands-on meta learning with Python : ▼b meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow / ▼c Sudharsan Ravichandiran.
260 ▼a Birmingham : ▼b Packt, ▼c 2018.
300 ▼a iv, 215 p. : ▼b ill. ; ▼c 24 cm.
504 ▼a Includes bibliographical references and index.
650 0 ▼a Artificial intelligence.
650 0 ▼a Machine learning.
650 0 ▼a Python (Computer program language).
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 005.133 R256h Accession No. 121257824 Availability In loan Due Date 2021-10-27 Make a Reservation Available for Reserve R Service M

Contents information

Table of Contents

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

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