Rekurrent (RNN) va svyortkali (CNN) neyron tarmoqlar
Abstract
Svyortkali Neyron Tarmoqlar (CNN) va Rekurrent Neyron Tarmoqlar (RNN) zamonaviy chuqur o'rganishning ikki asosiy yo'nalishini tashkil etadi. CNN fazoviy ma'lumotlarni ierarxik tarzda qayta ishlash orqali kompyuter ko'rishi sohasini tubdan o'zgartirdi. RNN esa LSTM va GRU kabi kengaytirilgan variantlari bilan ketma-ket ma'lumotlardagi vaqtinchalik bog'liqliklarni samarali modellashtiradi. Ushbu maqola har ikki arxitekturaning nazariy asoslari, matematik ifodalari, o'qitish usullari va cheklovlarini qamrab oladi. Shuningdek, taqqosiy tahlil, amaliy qo'llanishlar, gibrid yondashuvlar va kelajak tadqiqot istiqbollari ham muhokama qilinadi..
Kalit so‘zlar: Konvolyutsion Neyron Tarmoqlar, Rekurrent Neyron Tarmoqlar, LSTM, GRU, Chuqur O‘rganish, Kompyuter Ko‘rishi.
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