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Proceedings of the 15th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2023): Volume 2: Deep Learning and Large Language Models [Mīkstie vāki]

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  • Formāts: Paperback / softback, 549 pages, height x width: 235x155 mm, 238 Illustrations, color; 38 Illustrations, black and white; XVIII, 549 p. 276 illus., 238 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Networks and Systems 1245
  • Izdošanas datums: 04-May-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031810821
  • ISBN-13: 9783031810824
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  • Mīkstie vāki
  • Cena: 198,63 €*
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Proceedings of the 15th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2023): Volume 2: Deep Learning and Large Language ModelsPalielināt
  • Formāts: Paperback / softback, 549 pages, height x width: 235x155 mm, 238 Illustrations, color; 38 Illustrations, black and white; XVIII, 549 p. 276 illus., 238 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Networks and Systems 1245
  • Izdošanas datums: 04-May-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031810821
  • ISBN-13: 9783031810824
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031810824.html
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This book presents 55 selected papers focused on Deep Learning and Large Language Models from the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2023) and 14th World Congress on Nature and Biologically Inspired Computing (NaBIC 2023). SoCPaR – NaBIC 2023 was held in 5 different cities namely Olten, Switzerland; Porto, Portugal; Kaunas, Lithuania; Greater Noida, India; Kochi, India and in online mode. The conference had contributions by authors from 39 countries. This Volume offers a valuable reference guide for all scientists, academicians, researchers, students and practitioners focused on advanced machine learning including deep learning methods, large language models and its real-world applications.

A Hybrid Lightweight Deep Learning Model For Edge Devices: Combining Knowledge Distillation, Pruning, and Quantization.- Exploring the Effects of Weight Initialization Methods Combined with Different Activation Functions in Feedforward Neural Networks.- Enhancing Concealment in 3D Mesh Models using Chaotic based Steganography Algorithm with Minimal Perceptual Distortion.- Improving data delivery and energy efficiency in MANETs: a stacking based SVM approach for multipath.- Optimizing CNN Architecture for Quality Control of Corneal Confocal Microscopy Images Using a Genetic Algorithm.