The book explores high-performance architectures for data-intensive applications, as well as efficient analytical strategies, to speed up data processing in applications in automation, machine learning, deep learning, bioinformatics, natural language processing, and vision intelligence.
The book explores high-performance architectures for data-intensive applications, as well as efficient analytical strategies, to speed up data processing in applications in automation, machine learning, deep learning, bioinformatics, natural language processing, and vision intelligence.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Dr. M. M. Raghuwanshi is the Dean of Engineering at S.B.Jain Institute of Technology Management and Research, Nagpur, India. Dr. Pradnya Borkar is an Associate Professor at the Department of Computer Science and Engineering and R&D Cell Incharge, Jhulelal Institute of Technology, Nagpur. Dr. Rutvij H. Jhaveri is an experienced researcher working in the Department of Computer Science & Engineering, Pandit Deendayal Energy University (PDEU/PDPU), Gandhinagar, India since Dec. 2019. Dr. Roshani Raut is an as Associate Professor in the Department of Information Technology and Associate Dean International Relations, in Pimpri Chinchwad College of Engineering, Pune, India.
Inhaltsangabe
1. Introduction to High Performance Computing Architectures 2. High Performance Computing: Use Cases, API's and Applications 3. Parallelization Techniques 4. High Performance Computing for Machine Learning 5. Implementation of Parallel Computing with Artificial Intelligence in Big Data Analytics 6. D-UNet: Deep Learning Architecture for Colon Polyp Segmentation in Endoscopic Images 7. Early-Stage Plant Disease Detection using YOLOv8 8. Landslide Detection Using Custom Deep Convolutional Neural Network 9. GPU in Big Data: An Acceleration Technique 10. Use of NLP Techniques and High-Performance Computing for Automated Knowledge-based Ontology Construction of Saffron Crop 11. Implementing High-performance Computing with Artificial Intelligence in Healthcare Systems 12. BLMP2CE: Design of a Dual-Bioinspired Low-Complexity Data Mining Engine with Parallel Processing for Automatic Cluster Analysis via Ensemble Learning Operations 13. Deep Learning and Edge Computing with HPC 14. Usage of IoT, High Performance Computing, Machine and Deep Learning in a Human Activity Recognition (HAR) System: Challenges and Opportunities 15. Artificial Intelligence in Industry: An Approach to Automation 16. Usage of IoT, Artificial Intelligence and Machine Learning with HPC: Issues, Challenges, and a Case Study 17. Advancing High-Performance Computing for AI in the Era of Large-Scale Models: A Research Roadmap
1. Introduction to High Performance Computing Architectures 2. High Performance Computing: Use Cases, API's and Applications 3. Parallelization Techniques 4. High Performance Computing for Machine Learning 5. Implementation of Parallel Computing with Artificial Intelligence in Big Data Analytics 6. D-UNet: Deep Learning Architecture for Colon Polyp Segmentation in Endoscopic Images 7. Early-Stage Plant Disease Detection using YOLOv8 8. Landslide Detection Using Custom Deep Convolutional Neural Network 9. GPU in Big Data: An Acceleration Technique 10. Use of NLP Techniques and High-Performance Computing for Automated Knowledge-based Ontology Construction of Saffron Crop 11. Implementing High-performance Computing with Artificial Intelligence in Healthcare Systems 12. BLMP2CE: Design of a Dual-Bioinspired Low-Complexity Data Mining Engine with Parallel Processing for Automatic Cluster Analysis via Ensemble Learning Operations 13. Deep Learning and Edge Computing with HPC 14. Usage of IoT, High Performance Computing, Machine and Deep Learning in a Human Activity Recognition (HAR) System: Challenges and Opportunities 15. Artificial Intelligence in Industry: An Approach to Automation 16. Usage of IoT, Artificial Intelligence and Machine Learning with HPC: Issues, Challenges, and a Case Study 17. Advancing High-Performance Computing for AI in the Era of Large-Scale Models: A Research Roadmap
Es gelten unsere Allgemeinen Geschäftsbedingungen: www.buecher.de/agb
Impressum
www.buecher.de ist ein Internetauftritt der buecher.de internetstores GmbH
Geschäftsführung: Monica Sawhney | Roland Kölbl | Günter Hilger
Sitz der Gesellschaft: Batheyer Straße 115 - 117, 58099 Hagen
Postanschrift: Bürgermeister-Wegele-Str. 12, 86167 Augsburg
Amtsgericht Hagen HRB 13257
Steuernummer: 321/5800/1497
