Pengguna:KhalilullahAlFaath/Jaringan saraf konvolusional

Jaringan saraf konvolusional (bahasa Inggris: Convolutional Neural Network) yang biasa disingkat CNN adalah jaringan saraf lapis maju teregularisasi yang dapat mengekstraksi fitur sendiri dengan menggunakan optimasi filter (atau kernel). Masalah hilang atau meledaknya gradien yang seringkali muncul selama propagasi balik pada jaringan saraf awal, dicegah dengan menggunakan bobot teregularisi pada koneksi yang lebih sedikit.^[1]^[2] Contohnya, untuk setiap neuron pada lapisan yang terhubung sepenuhnya (fully-connected layers), 10.000 bobot diperlukan untuk memproses sebuah citra berukuran 100 x 100 piksel. Namun, dengan menerapkan kernel konvolusi (atau korelasi silang) bertingkat,^[3]^[4] hanya dibutuhkan 25 neuron untuk memproses petak berukuran 5x5.^[5]^[6] Fitur tingkat tinggi diekstrak dari jendela konteks yang lebih luas dibandingkan fitur tingkat rendah.

CNN diaplikasikan pada:

CNN juga dikenal sebagai Shift Invariant atau Space Invariant Artificial Neural Networks (SIANN), berdasarkan arsitektur bobot bersama (shared-weights) dari kernel konvolusi (filter) yang bergeser sepanjang fitur masukan dan menghasilkan respons yang equivariasi-translasi, yang dikenal sebagai peta fitur (feature maps). Sebaliknya, kebanyakan CNN tidak invarian translasi, karena adanya operasi downsampling yang diaplikasikan pada masukan.

^ Venkatesan, Ragav; Li, Baoxin (2017-10-23). Convolutional Neural Networks in Visual Computing: A Concise Guide (dalam bahasa Inggris). CRC Press. ISBN 978-1-351-65032-8. Diarsipkan dari versi asli tanggal 2023-10-16. Diakses tanggal 2020-12-13.
^ Balas, Valentina E.; Kumar, Raghvendra; Srivastava, Rajshree (2019-11-19). Recent Trends and Advances in Artificial Intelligence and Internet of Things (dalam bahasa Inggris). Springer Nature. ISBN 978-3-030-32644-9. Diarsipkan dari versi asli tanggal 2023-10-16. Diakses tanggal 2020-12-13.
^ Zhang, Yingjie; Soon, Hong Geok; Ye, Dongsen; Fuh, Jerry Ying Hsi; Zhu, Kunpeng (September 2020). "Powder-Bed Fusion Process Monitoring by Machine Vision With Hybrid Convolutional Neural Networks". IEEE Transactions on Industrial Informatics. 16 (9): 5769–5779. doi:10.1109/TII.2019.2956078. ISSN 1941-0050. Diarsipkan dari versi asli tanggal 2023-07-31. Diakses tanggal 2023-08-12.
^ Chervyakov, N.I.; Lyakhov, P.A.; Deryabin, M.A.; Nagornov, N.N.; Valueva, M.V.; Valuev, G.V. (September 2020). "Residue Number System-Based Solution for Reducing the Hardware Cost of a Convolutional Neural Network". Neurocomputing (dalam bahasa Inggris). 407: 439–453. doi:10.1016/j.neucom.2020.04.018. Diarsipkan dari versi asli tanggal 2023-06-29. Diakses tanggal 2023-08-12. Convolutional neural networks represent deep learning architectures that are currently used in a wide range of applications, including computer vision, speech recognition, malware dedection, time series analysis in finance, and many others.
^ Habibi, Aghdam, Hamed (2017-05-30). Guide to convolutional neural networks : a practical application to traffic-sign detection and classification. Heravi, Elnaz Jahani. Cham, Switzerland. ISBN 9783319575490. OCLC 987790957.
^ Atlas, Homma, and Marks. "An Artificial Neural Network for Spatio-Temporal Bipolar Patterns: Application to Phoneme Classification" (PDF). Neural Information Processing Systems (NIPS 1987). 1. Diarsipkan dari versi asli (PDF) tanggal 2021-04-14.
^ Valueva, M.V.; Nagornov, N.N.; Lyakhov, P.A.; Valuev, G.V.; Chervyakov, N.I. (2020). "Application of the residue number system to reduce hardware costs of the convolutional neural network implementation". Mathematics and Computers in Simulation. Elsevier BV. 177: 232–243. doi:10.1016/j.matcom.2020.04.031. ISSN 0378-4754. Convolutional neural networks are a promising tool for solving the problem of pattern recognition.
^ van den Oord, Aaron; Dieleman, Sander; Schrauwen, Benjamin (2013-01-01). Burges, C. J. C.; Bottou, L.; Welling, M.; Ghahramani, Z.; Weinberger, K. Q., ed. Deep content-based music recommendation (PDF). Curran Associates, Inc. hlm. 2643–2651. Diarsipkan dari versi asli (PDF) tanggal 2022-03-07. Diakses tanggal 2022-03-31.
^ Collobert, Ronan; Weston, Jason (2008-01-01). "A unified architecture for natural language processing". Proceedings of the 25th international conference on Machine learning - ICML '08. New York, NY, USA: ACM. hlm. 160–167. doi:10.1145/1390156.1390177. ISBN 978-1-60558-205-4.
^ Avilov, Oleksii; Rimbert, Sebastien; Popov, Anton; Bougrain, Laurent (July 2020). "Deep Learning Techniques to Improve Intraoperative Awareness Detection from Electroencephalographic Signals" (PDF). 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (PDF). 2020. Montreal, QC, Canada: IEEE. hlm. 142–145. doi:10.1109/EMBC44109.2020.9176228. ISBN 978-1-7281-1990-8. PMID 33017950 Periksa nilai |pmid= (bantuan). Diarsipkan dari versi asli tanggal 2022-05-19. Diakses tanggal 2023-07-21.
^ Tsantekidis, Avraam; Passalis, Nikolaos; Tefas, Anastasios; Kanniainen, Juho; Gabbouj, Moncef; Iosifidis, Alexandros (July 2017). "Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks". 2017 IEEE 19th Conference on Business Informatics (CBI). Thessaloniki, Greece: IEEE. hlm. 7–12. doi:10.1109/CBI.2017.23. ISBN 978-1-5386-3035-8.

[auto3-1] Venkatesan, Ragav; Li, Baoxin (2017-10-23). Convolutional Neural Networks in Visual Computing: A Concise Guide (dalam bahasa Inggris). CRC Press. ISBN 978-1-351-65032-8. Diarsipkan dari versi asli tanggal 2023-10-16. Diakses tanggal 2020-12-13.

[auto2-2] Balas, Valentina E.; Kumar, Raghvendra; Srivastava, Rajshree (2019-11-19). Recent Trends and Advances in Artificial Intelligence and Internet of Things (dalam bahasa Inggris). Springer Nature. ISBN 978-3-030-32644-9. Diarsipkan dari versi asli tanggal 2023-10-16. Diakses tanggal 2020-12-13.

[3] Zhang, Yingjie; Soon, Hong Geok; Ye, Dongsen; Fuh, Jerry Ying Hsi; Zhu, Kunpeng (September 2020). "Powder-Bed Fusion Process Monitoring by Machine Vision With Hybrid Convolutional Neural Networks". IEEE Transactions on Industrial Informatics. 16 (9): 5769–5779. doi:10.1109/TII.2019.2956078. ISSN 1941-0050. Diarsipkan dari versi asli tanggal 2023-07-31. Diakses tanggal 2023-08-12.

[4] Chervyakov, N.I.; Lyakhov, P.A.; Deryabin, M.A.; Nagornov, N.N.; Valueva, M.V.; Valuev, G.V. (September 2020). "Residue Number System-Based Solution for Reducing the Hardware Cost of a Convolutional Neural Network". Neurocomputing (dalam bahasa Inggris). 407: 439–453. doi:10.1016/j.neucom.2020.04.018. Diarsipkan dari versi asli tanggal 2023-06-29. Diakses tanggal 2023-08-12. Convolutional neural networks represent deep learning architectures that are currently used in a wide range of applications, including computer vision, speech recognition, malware dedection, time series analysis in finance, and many others.

[auto1-5] Habibi, Aghdam, Hamed (2017-05-30). Guide to convolutional neural networks : a practical application to traffic-sign detection and classification. Heravi, Elnaz Jahani. Cham, Switzerland. ISBN 9783319575490. OCLC 987790957.

[6] Atlas, Homma, and Marks. "An Artificial Neural Network for Spatio-Temporal Bipolar Patterns: Application to Phoneme Classification" (PDF). Neural Information Processing Systems (NIPS 1987). 1. Diarsipkan dari versi asli (PDF) tanggal 2021-04-14.

[Valueva_Nagornov_Lyakhov_Valuev_2020_pp._232–243-7] Valueva, M.V.; Nagornov, N.N.; Lyakhov, P.A.; Valuev, G.V.; Chervyakov, N.I. (2020). "Application of the residue number system to reduce hardware costs of the convolutional neural network implementation". Mathematics and Computers in Simulation. Elsevier BV. 177: 232–243. doi:10.1016/j.matcom.2020.04.031. ISSN 0378-4754. Convolutional neural networks are a promising tool for solving the problem of pattern recognition.

[8] van den Oord, Aaron; Dieleman, Sander; Schrauwen, Benjamin (2013-01-01). Burges, C. J. C.; Bottou, L.; Welling, M.; Ghahramani, Z.; Weinberger, K. Q., ed. Deep content-based music recommendation (PDF). Curran Associates, Inc. hlm. 2643–2651. Diarsipkan dari versi asli (PDF) tanggal 2022-03-07. Diakses tanggal 2022-03-31.

[9] Collobert, Ronan; Weston, Jason (2008-01-01). "A unified architecture for natural language processing". Proceedings of the 25th international conference on Machine learning - ICML '08. New York, NY, USA: ACM. hlm. 160–167. doi:10.1145/1390156.1390177. ISBN 978-1-60558-205-4.

[10] Avilov, Oleksii; Rimbert, Sebastien; Popov, Anton; Bougrain, Laurent (July 2020). "Deep Learning Techniques to Improve Intraoperative Awareness Detection from Electroencephalographic Signals" (PDF). 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (PDF). 2020. Montreal, QC, Canada: IEEE. hlm. 142–145. doi:10.1109/EMBC44109.2020.9176228. ISBN 978-1-7281-1990-8. PMID 33017950 Periksa nilai |pmid= (bantuan). Diarsipkan dari versi asli tanggal 2022-05-19. Diakses tanggal 2023-07-21.

[Tsantekidis_7–12-11] Tsantekidis, Avraam; Passalis, Nikolaos; Tefas, Anastasios; Kanniainen, Juho; Gabbouj, Moncef; Iosifidis, Alexandros (July 2017). "Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks". 2017 IEEE 19th Conference on Business Informatics (CBI). Thessaloniki, Greece: IEEE. hlm. 7–12. doi:10.1109/CBI.2017.23. ISBN 978-1-5386-3035-8.

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]