Александр Кириченко

Нейросетевые технологии. Конспект


Скачать книгу

в таких нейроконструкциях допускается использование известных алгоритмов из математической статистики, например, для выявления причинно-следственных связей, формирования существенных признаков, генерации гипотез, а так же – таких конструкций, как систем управления базами знаний (СУБЗ). В них можно использовать не выполненные в виде нейронных сетей логические элементы, а обычные цифровые программы.

      Конец ознакомительного фрагмента.

      Текст предоставлен ООО «ЛитРес».

      Прочитайте эту книгу целиком, купив полную легальную версию на ЛитРес.

      Безопасно оплатить книгу можно банковской картой Visa, MasterCard, Maestro, со счета мобильного телефона, с платежного терминала, в салоне МТС или Связной, через PayPal, WebMoney, Яндекс.Деньги, QIWI Кошелек, бонусными картами или другим удобным Вам способом.

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