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Titlebook: Parametrized, Deformed and General Neural Networks; George A. Anastassiou Book 2023 The Editor(s) (if applicable) and The Author(s), under

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11#
發(fā)表于 2025-3-23 11:04:56 | 只看該作者
George A. Anastassioueadsheet in 1979 enabled ordinary computer users to handle complex mathematical and statistical analysis and fueled the adoption of personal computers. Spreadsheets have proven to be highly successful tools for interacting with numerical data with numerous advantages. Users can easily organize large
12#
發(fā)表于 2025-3-23 17:26:00 | 只看該作者
13#
發(fā)表于 2025-3-23 18:26:00 | 只看該作者
14#
發(fā)表于 2025-3-24 00:22:40 | 只看該作者
15#
發(fā)表于 2025-3-24 04:32:08 | 只看該作者
George A. Anastassiou. Words like apps, smartphones, smartwatches, and software are so common nowadays that sometimes it’s hard to remember that most of these things became part of our daily lives not so long ago. I remember that was in my first year of college, back in 2008, when a friend showed me that his mobile phon
16#
發(fā)表于 2025-3-24 07:52:09 | 只看該作者
George A. Anastassiouild your very own software product.Software is everywhere, but despite being so common and useful, it remains magical and mysterious to many. Still, more and more people are finding themselves working for tech companies, or with an array of software products, services, and tools. This can segregate
17#
發(fā)表于 2025-3-24 13:31:35 | 只看該作者
18#
發(fā)表于 2025-3-24 18:17:25 | 只看該作者
,Abstract Ordinary and Fractional Neural Network Approximations Based on Richard’s Curve,or all the real line by quasi-interpolation Banach space valued neural network operators. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function or its Banach space valued high order derivative or fractional derivatives.
19#
發(fā)表于 2025-3-24 20:04:10 | 只看該作者
20#
發(fā)表于 2025-3-25 02:21:41 | 只看該作者
Parametrized Hyperbolic Tangent Based Banach Space Valued Basic and Fractional Neural Network Approximations,or all the real line by quasi-interpolation Banach space valued neural network operators. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function or its Banach space valued high order derivative of fractional derivatives.
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