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Hands-on one-shot learning with Python : learn to implement fast and accurate deep learning models with fewer training samples using PyTorch

Hands-on one-shot learning with Python : learn to implement fast and accurate deep learning models with fewer training samples using PyTorch (Loan 1 times)

Material type
단행본
Personal Author
Jadon, Shruti. Garg, Ankush.
Title Statement
Hands-on one-shot learning with Python : learn to implement fast and accurate deep learning models with fewer training samples using PyTorch / Shruti Jadon, Ankush Garg.
Publication, Distribution, etc
Birmingham :   Packt Publishing,   2020.  
Physical Medium
iii, 145 p. : ill. ; 24 cm.
ISBN
9781838825461 (pbk.)
General Note
Includes index.  
Subject Added Entry-Topical Term
Machine learning. Computer vision. Python (Computer program language).
000 00000cam u2200205 a 4500
001 000046087567
005 20210728130030
008 210727s2020 enka 001 0 eng d
020 ▼a 9781838825461 (pbk.)
035 ▼a (KERIS)BIB000015800584
040 ▼a 211062 ▼c 211062 ▼d 211009
082 0 4 ▼a 006.31 ▼2 23
084 ▼a 006.31 ▼2 DDCK
090 ▼a 006.31 ▼b J21h
100 1 ▼a Jadon, Shruti.
245 1 0 ▼a Hands-on one-shot learning with Python : ▼b learn to implement fast and accurate deep learning models with fewer training samples using PyTorch / ▼c Shruti Jadon, Ankush Garg.
246 3 0 ▼a One-shot learning with Python
260 ▼a Birmingham : ▼b Packt Publishing, ▼c 2020.
300 ▼a iii, 145 p. : ▼b ill. ; ▼c 24 cm.
500 ▼a Includes index.
650 0 ▼a Machine learning.
650 0 ▼a Computer vision.
650 0 ▼a Python (Computer program language).
700 1 ▼a Garg, Ankush.
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 006.31 J21h Accession No. 121257823 Availability In loan Due Date 2021-10-27 Make a Reservation Available for Reserve R Service M

Contents information

Table of Contents

Introduction to One-shot Learning
Metrics-Based Methods
Models-Based Methods
Optimization-Based Methods
Generative Modeling-Based Methods
Conclusion and Other Approaches

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