MARC details
| 000 -LEADER |
|---|
| fixed length control field |
01251cam a2200265 i 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
200909n2019 af 001 0 eng u |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781788834131 |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
(Sirsi) a2089 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
<a href="005.133">005.133</a> |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Thomas, Sherin |
| Relator term |
, author |
| 9 (RLIN) |
8447 |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
PyTorch Deep Learning Hands-On |
| Remainder of title |
:Build CNNs, RNNs, GANs, Reinforcement Learning, and more, quickly and easily |
| Statement of responsibility, etc. |
/ Sherin Thomas with Sudhanshu Passi. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Birmingham : |
| Name of producer, publisher, distributor, manufacturer |
Packt Publishing, Limited, |
| Date of production, publication, distribution, manufacture, or copyright notice |
[2019]. |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
©2019. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
232 pages |
| Other physical details |
: illustrations |
| Dimensions |
; 24 cm |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Content type code |
txt |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
unmediated |
| Media type code |
n |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
volume |
| Carrier type code |
nc |
| Source |
rdacarrier |
| 490 0# - SERIES STATEMENT |
|---|
| Series statement |
Expert insight. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Implement every major architecture of deep learning in PyTorch. Starting with simple neural networks, it covers PyTorch for computer vision (CNN), natural language (RNN), GANs, and reinforcement learning. It covers deep learning workflows, migrating models to TorchScript, and deploying to efficient production environments. |
| 596 ## - |
|---|
| -- |
1 3 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Machine learning |
| 9 (RLIN) |
2745 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Neural networks (Computer science) |
| 9 (RLIN) |
3761 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Python (Computer program language) |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Passi, Sudhanshu |
| Relator term |
, author |
| 9 (RLIN) |
8448 |