251,85 €
296,29 €
-15% su kodu: ENG15
Recurrent Neural Networks for Temporal Data Processing
251,85 € 
- Išsiųsime per 10–14 d.d.
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the connections are not feed-forward ones only. In RNNs, connections between units form directed cycles, providing an implicit internal memory. Those RNNs are adapted to problems dealing with signals evolving through time. Their internal memory gives them the ability to naturally take time into account. Valuable approximation results have been obtained for dynamical systems.
251.85
2025-08-04 23:59:00
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- Leidėjas: IntechOpen
- Metai: 2011
- Puslapiai: 116
- ISBN-10: 9533076852
- ISBN-13: 9789533076850
- Formatas: 17 x 24.4 x 0.8 cm, kieti viršeliai
- Kalba: Anglų
- Extra -15 % nuolaida šiai knygai su kodu: ENG15
Recurrent Neural Networks for Temporal Data Processing + nemokamas atvežimas! | knygos.lt
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Aprašymas
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the connections are not feed-forward ones only. In RNNs, connections between units form directed cycles, providing an implicit internal memory. Those RNNs are adapted to problems dealing with signals evolving through time. Their internal memory gives them the ability to naturally take time into account. Valuable approximation results have been obtained for dynamical systems.EXTRA 15 % nuolaida su kodu: ENG15
251,85 €
296,29 €
Išsiųsime per 10–14 d.d.
Akcija baigiasi už 7d.02:52:43
Nuolaidos kodas galioja perkant nuo 10 €. Nuolaidos nesumuojamos.
- Leidėjas: IntechOpen
- Metai: 2011
- Puslapiai: 116
- ISBN-10: 9533076852
- ISBN-13: 9789533076850
- Formatas: 17 x 24.4 x 0.8 cm, kieti viršeliai
- Kalba: Anglų
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the connections are not feed-forward ones only. In RNNs, connections between units form directed cycles, providing an implicit internal memory. Those RNNs are adapted to problems dealing with signals evolving through time. Their internal memory gives them the ability to naturally take time into account. Valuable approximation results have been obtained for dynamical systems.
Atsiliepimai