| 000 -LEADER |
|---|
| fixed length control field |
nam a22 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
240216b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9780367458621 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Item number |
GHA |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ghayoumi, Mehdi |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Deep learning in practice |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher, distributor, etc |
CRC Press, |
| Place of publication, distribution, etc |
Boca Raton : |
| Date of publication, distribution, etc |
2022 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xix, 198 p. ; |
| Dimensions |
25 cm. |
| Other physical details |
ill., (b & w), |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
69.99 |
| Price type code |
₤ |
| Unit of pricing |
94.80 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
Deep Learning in Practice helps you learn how to develop and optimize a model for your projects using Deep Learning (DL) methods and architectures. Key features: Demonstrates a quick review on Python, NumPy, and TensorFlow fundamentals. Explains and provides examples of deploying TensorFlow and Keras in several projects. Explains the fundamentals of Artificial Neural Networks (ANNs). Presents several examples and applications of ANNs. Learning the most popular DL algorithms features. Explains and provides examples for the DL algorithms that are presented in this book. Analyzes the DL network⁰́₉s parameter and hyperparameters. Reviews state-of-the-art DL examples. Necessary and main steps for DL modeling. Implements a Virtual Assistant Robot (VAR) using DL methods. Necessary and fundamental information to choose a proper DL algorithm. Gives instructions to learn how to optimize your DL model IN PRACTICE. This book is useful for undergraduate and graduate students, as well as practitioners in industry and academia. It will serve as a useful reference for learning deep learning fundamentals and implementing a deep learning model for any project, step by step. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Deep learning |
|
|---|
| Topical term or geographic name as entry element |
Business statistics |
|
|---|
| Topical term or geographic name as entry element |
Sconomics statistics |
|
|---|
| Topical term or geographic name as entry element |
Computer science |
|
|---|
| Topical term or geographic name as entry element |
Machine learning |
|
|---|
| Topical term or geographic name as entry element |
Neural networks |
|
|---|
| Topical term or geographic name as entry element |
Reshape array |
|
|---|
| Topical term or geographic name as entry element |
Matrix multiplication |
|
|---|
| Topical term or geographic name as entry element |
Python libraries |
|
|---|
| Topical term or geographic name as entry element |
Object recognition |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Books |