Graph Algorithms / (Record no. 64979)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01776nam a2200193 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 200530b2019 ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789352138487 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 511.5 NEE-M |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Needham, Mark |
| 245 ## - TITLE STATEMENT | |
| Title | Graph Algorithms / |
| Statement of responsibility, etc. | Mark Needham and Amy E. Hodler |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | India |
| Name of publisher, distributor, etc. | Shroff Publishers |
| Date of publication, distribution, etc. | 2019 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 235 p. |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE | |
| Title | INR |
| Volume/sequential designation | 950.00. |
| 500 ## - GENERAL NOTE | |
| General note | learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions and enhance your machine learning models. With this practical guide, developers and data scientists will discover how graph analytics deliver value, whether they're used for building dynamic network models or forecasting real-world behavior.<Br> mark Needham and Amy hodler from Neo4j explain how graph algorithms describe complex structures and reveal difficult-to-find patterns—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. You’ll walk through hands-on examples that show you how to use graph algorithms in Apache Spark and Neo4j, two of the most common choices for graph analytics. <Br><br/>Learn how graph analytics reveal more predictive elements in today’s data <understand how popular graph algorithms work and how they've applied<br/>Use Sample code and tips from more than 20 graph algorithm examples<br/>Learn which algorithms to use for different types of questions <explore examples with working code and Sample datasets for Spark and Neo4j<br/>Create an ML work flow for link prediction by combining Neo4j and Spark. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Spark (Electronic resource : Apache Software Foundation) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Graph algorithms |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Web applications |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Application software--Development |
| 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 | Cost, normal purchase price | Total Checkouts | Total Renewals | Full call number | Barcode | Checked out | Date last seen | Date last checked out | Price effective from | Koha item type |
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| Dewey Decimal Classification | 510 | BITS Pilani Hyderabad | BITS Pilani Hyderabad | General Stack (For lending) | 30/05/2020 | 950.00 | 3 | 2 | 511.5 NEE-M | 40545 | 15/01/2025 | 10/12/2024 | 10/12/2024 | 30/05/2020 | Books |