MARC details
| 000 -LEADER |
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| fixed length control field |
02087nam a22002657a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20240210165305.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
240210b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781803232911 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.3 |
| Item number |
KAP |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Kapoor, Amita |
| 245 ## - TITLE STATEMENT |
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| Title |
Deep learning with TensorFlow and Keras: |
| Remainder of title |
build and deploy supervised, unsupervised, deep, and reinforcement learning models |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
3rd |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher, distributor, etc. |
Packt Publishing |
| Place of publication, distribution, etc. |
Birmingham |
| Date of publication, distribution, etc. |
2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxix, 667 p. |
| 365 ## - TRADE PRICE |
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| Price type code |
INR |
| Price amount |
3699.00 |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments. This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.<br/><br/>(https://www.packtpub.com/product/deep-learning-with-tensorflow-and-keras-third-edition/9781803232911) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computer science |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computer programming |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Artificial intelligence |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Gulli, Antonio |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Pal, Sujit |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Koha item type |
Book |
| Source of classification or shelving scheme |
Dewey Decimal Classification |