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Könyv Various Deep Learning Algorithms in Computational Intelligence Oscar Humberto Montiel Ross

Various Deep Learning Algorithms in Computational Intelligence

Szerző: Oscar Humberto Montiel Ross
Nyelv: Angol
Kötés: Kemény kötésű
Kiadó: MDPI AG
Elérhetőség: Beszállítói készleten
Küldés 14-21 napon belül
27 948 Ft
This reprint highlights the importance of Deep Learning (DL), which has garnered significant attenti...

Információk a könyvről

Szerző
Oscar Humberto Montiel Ross
Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2023
oldal
282
EAN
9783036581385
ISBN
3036581383
Enbook ID
43749162
Kiadó
MDPI AG
Súly
907
Méretek
175 x 250 x 23

Teljes leírás

This reprint highlights the importance of Deep Learning (DL), which has garnered significant attention in science, industry, and academia. It draws inspiration from the functioning of the human brain and the concept of learning. Unlike traditional and machine learning methods, deep learning techniques emulate the human brain's neural networks at a lower scale, allowing them to process and analyze substantial quantities of unstructured data. The remarkable proficiency of deep learning in unveiling intricate structures within extensive datasets genuinely resembles the extraordinary aptitude of the brain to recognize patterns and form complex connections. This unique characteristic allows DL to excel in modeling and solving complex problems across various scientific and technological fields. Just as the brain learns from experience, DL architectures learn through algorithms from data by adjusting numerous parameters during training to optimize their performance and accuracy. This concept of learning and adaptation is fundamental to DL's success.

This reprint serves as an excellent opportunity to disseminate current knowledge beyond academic boundaries, reaching a diverse audience encompassing academics, professionals, and the general public. This wide readership fosters the potential for meaningful connections to established projects and the cultivation of collaboration for future research endeavors.

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