Practical mathematics for AI and deep learning / Tamoghna Ghosh and Shravan Kumar Belagal Math.
By: Ghosh, Tamoghna [author.]
Contributor(s): Math, Shravan Kumar Belagal [author.]
Language: English Publisher: New Delhi : BPB Publications, 2023Edition: First editionDescription: xxiv, 504 pages : illustrations ; 24 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9789355511935Subject(s): Artificial intelligence -- MathematicsDDC classification: 006.301/51| Item type | Current location | Home library | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|
BOOK
|
COLLEGE LIBRARY | COLLEGE LIBRARY SUBJECT REFERENCE | 006.30151 G3465 2023 (Browse shelf) | Available | CITU-CL-53608 |
Browsing COLLEGE LIBRARY Shelves , Shelving location: SUBJECT REFERENCE Close shelf browser
|
|
|
|
|
|
|
||
| 006.3 T766 2001 Data mining and statistical analysis using SQL / | 006.3 W184 2003 Data mining : opportunities and challenges / | 006.3 Xi4 2002 Probabilistic reasoning in multiagent systems : a graphical models approach / | 006.30151 G3465 2023 Practical mathematics for AI and deep learning / | 006.3023 G829 2007 Careers in artificial intelligence / | 006.31 B233 2012 Bayesian reasoning and machine learning / | 006.31 B413 2015 Machine learning : hands-on for developers and technical professionals / |
Tamoghna is an AI Software Solutions Engineer in Client Computing Group at Intel and has 15 years of work experience. He has a master’s in computer science from Indian Statistical Institute and a master’s in mathematics form Calcutta University. He has 4 US patents, 3 IEEE papers and has also authored book on Transfer learning.
Shravan is currently an AI Engineer at Intel’s Client Computing Group with 11 years of working experience. He had Master of Engineering degree from Indian Institute of Science, Computer Science and Automation department. He has been granted with 4 US patents. His interest lies in application of AI algorithms to solve real world problems.
Includes index.
1. Overview of AI -- 2. Linear algebra -- 3. Vector calculus -- 4. Basic statistics and probability theory -- 5. Statistical inference and applications -- 6. Neural networks -- 7. Clustering -- 8. Dimensionality Reduction -- 9. Computer vision -- 10. Sequence learning models -- 11. Natural language processing -- 12. Generative models -- Index.
There are no comments for this item.