Torrent details for "Anastassiou G. AI Mathematics. Advanced Neural Network Approxima…" Log in to bookmark
Controls:
×
Report Torrent
Please select a reason for reporting this torrent:
Your report will be reviewed by our moderation team.
×
Report Information
Loading report information...
This torrent has been reported 0 times.
Report Summary:
| User | Reason | Date |
|---|
Failed to load report information.
×
Success
Your report has been submitted successfully.
Checked by:
Category:
Language:
None
Total Size:
10.4 MB
Info Hash:
8BED5AAB5B0EA263E0AF0BD9AF78169FDFF8FBF8
Added By:
Added:
March 29, 2026, 10:41 a.m.
Stats:
|
(Last updated: March 29, 2026, 10:45 a.m.)
| File | Size |
|---|---|
| Anastassiou G. AI Mathematics. Advanced Neural Network Approximation 2026.pdf | 10.4 MB |
Name
DL
Uploader
Size
S/L
Added
-
104.9 MB
[0
/
2]
2023-07-02
| Uploaded by indexFroggy | Size 104.9 MB | Health [ 0 /2 ] | Added 2023-07-02 |
-
27.1 MB
[34
/
0]
2023-07-01
| Uploaded by indexFroggy | Size 27.1 MB | Health [ 34 /0 ] | Added 2023-07-01 |
-
14.1 MB
[22
/
8]
2023-07-01
| Uploaded by indexFroggy | Size 14.1 MB | Health [ 22 /8 ] | Added 2023-07-01 |
NOTE
SOURCE: Anastassiou G. AI Mathematics. Advanced Neural Network Approximation 2026
-----------------------------------------------------------------------------------
COVER
-----------------------------------------------------------------------------------
MEDIAINFO
Textbook in PDF format
This book presents the new idea of going from the neural networks main tools, the activation functions, to convolution integrals and singular integrals approximations. That is the rare case of employing applied mathematics to treat theoretical ones.
Authors introduce and use also the symmetrized neural network operators able to achieve supersonic speeds of convergence.
Authors use a great variety of activation functions. Thus, in this book all presented is original work by the author given at a very general level to cover a maximum number of different kinds of Neural Networks: giving ordinary, fractional, and stochastic approximations. It is presented here univariate, fractional, and multivariate approximations. Iterated-sequential multi-layer approximations are also studied
×