Applied deep learning: (Record no. 3865)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01948nam a22002177a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20221122121349.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 221122b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781484247211 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | MIC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Michelucci, Umberto |
| 245 ## - TITLE STATEMENT | |
| Title | Applied deep learning: |
| Remainder of title | a case-based approach to understanding deep neural networks |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 2nd |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc. | Apress |
| Place of publication, distribution, etc. | New York |
| Date of publication, distribution, etc. | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxi, 410 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 1199.00 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | About this book<br/>Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. <br/><br/>The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. <br/><br/>Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy). |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Neural networks (Computer science) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Python (Computer program language) |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Book |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Bill No | Bill Date | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Total Checkouts | Full call number | Accession Number | Date last seen | Copy number | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | IT & Decisions Sciences | TB1974 | 28-10-2022 | Indian Institute of Management LRC | Indian Institute of Management LRC | General Stacks | 11/22/2022 | Technical Bureau India Pvt. Ltd. | 839.30 | 006.31 MIC | 003709 | 11/22/2022 | 1 | 1119.00 | 11/22/2022 | Book |