Deep belief nets in C++ and CUDA C : volume 1 - restricted Boltzmann machines and supervised feedforward networks / (Record no. 64860)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01729nam a22001937a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 200430b2018 ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781484240205 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.32 MAS-T |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Masters, Timothy |
| 245 ## - TITLE STATEMENT | |
| Title | Deep belief nets in C++ and CUDA C : volume 1 - restricted Boltzmann machines and supervised feedforward networks / |
| Statement of responsibility, etc. | Timothy Masters |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | NY |
| Name of publisher, distributor, etc. | Apress |
| Date of publication, distribution, etc. | 2018 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 219 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 609.00 |
| 500 ## - GENERAL NOTE | |
| General note | Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. <br/>The first of three in a series on C++ and CUDA C deep learning and belief nets, Deep Belief Nets in C++ and CUDA C: Volume 1 shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a thought process that is capable of learning abstract concepts built from simpler primitives. As such, you’ll see that a typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. <br/>All the routines and algorithms presented in the book are available in the code download, which also contains some libraries of related routines. |
| 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 | C++ (Computer program language) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Programming languages (Electronic computers) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | CUDA (Computer architecture) |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) | |
| Withdrawn status | |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Full call number | Barcode | Date last seen | Date last checked out | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | 003-007 | BITS Pilani Hyderabad | BITS Pilani Hyderabad | General Stack (For lending) | 30/04/2020 | 3 | 006.32 MAS-T | 40373 | 13/07/2024 | 18/04/2024 | 30/04/2020 | Books |