Hands-on neural networks with Keras : (Record no. 91937)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02060nam a2200253Ia 4500 |
| 001 - CONTROL NUMBER | |
| control field | 355656 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | 0000000000 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20211104093422.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 130128n 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781789536089 |
| 040 ## - CATALOGING SOURCE | |
| Description conventions | rda |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA 76.87 |
| Item number | .P975 2019 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Purkait, Niloy |
| 9 (RLIN) | 128259 |
| 245 #0 - TITLE STATEMENT | |
| Title | Hands-on neural networks with Keras : |
| Remainder of title | design and create neural networks using deep learning and artificial intelligence principles / |
| Statement of responsibility, etc. | Niloy Purkait. |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | UK : |
| Name of producer, publisher, distributor, manufacturer | Packt Publishing, |
| Date of production, publication, distribution, manufacture, or copyright notice | c2019. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xii, 443 pages |
| Dimensions | 23 cm. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | unmediated |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | volume |
| Source | rdacarrier |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Neural networks are used to solve a wide range of problems in different areas of AI and deep learning. Hands-On Neural Networks with Keras will start with teaching you about the core concepts of neural networks. You will delve into combining different neural network models and work with real-world use cases, including computer vision, natural language understanding, synthetic data generation, and many more. Moving on, you will become well versed with convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, autoencoders, and generative adversarial networks (GANs) using real-world training datasets. We will examine how to use CNNs for image recognition, how to use reinforcement learning agents, and many more. We will dive into the specific architectures of various networks and then implement each of them in a hands-on manner using industry-grade frameworks. By the end of this book, you will be highly familiar with all prominent deep learning models and frameworks, and the options you have when applying deep learning to real-world scenarios and embedding artificial intelligence as the core fabric of your organization.--https://www.packtpub.com |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Neural networks (Computer science). |
| Source of heading or term | sears |
| 9 (RLIN) | 63982 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | |
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