Neural Network Design (2nd Edition)
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This is not a completed Solutions Manual. In case you need help with any exercise of the book or generally you have a question about Neural Networks you can have a look at Artificial Intelligence Stack Exchange, which is the best community to learn and discuss. You are also welcome to use discussions of this repository.
Book details
Title : Neural Network Design (2nd Edition)
Authors : Martin T. Hagan, Howard B. Demuth, Mark H. Beale, Orlando De Jesus
ISBN-10 : 0-9717321-1-6
ISBN-13 : 978-0-9717321-1-7
A PDF version of this textbook can be found at : http://hagan.okstate.edu/NNDesign.pdf
| Chapter | Name |
|---|---|
| 2 | Neuron Model and Network Architectures |
| 4 | Perceptron Learning Rule |
| 7 | Supervised Hebbian Learning |
| 8 | Performance Surfaces and Optimum Points |
| 9 | Performance Optimization |
| 10 | Widrow-Hoff Learning |
| 11 | Backpropagation |
| 12 | Variations on Backpropagation |
| 13 | Generalization |
| 14 | Dynamic Networks (DNN) |
| 15 | Associative Learning |
| 16 | Competitive Networks (CNN) |
| 17 | Radial Basis Networks (RBF) |
🔶 Note that some solutions contain Greek language text , you can either ignore it or use Google Translate .
| Chapter | Exercise | Add Date | Update Date | Author(s) |
|---|---|---|---|---|
| 2 | E2.6 | 01/17/20 | 01/17/20 | @estamos |
| 4 | E4.8 | 01/17/20 | 01/17/20 | @estamos |
| 7 | E7.1 E7.2 E7.4 E7.5 E7.6 E7.9 | 03/04/20 | 14/01/21 | @estamos & @OUStudent |
| 8 | E8.1 E8.2 E8.4 E8.7 E8.10 | 01/16/21 | 01/16/21 | @OUStudent |
| 9 | E9.1 E9.5 E7.7 E9.10 | 01/18/21 | 01/20/21 | @OUStudent |
| 10 | E10.2 E10.4 E10.5 E10.6 E10.12 | 01/17/20 | 01/20/21 | @estamos & @OUStudent |
| 11 | E11.1 E11.3 E11.6 E11.7 E11.9 E11.10 E11.11 E11.12 E11.13 E11.25 | 01/17/20 | 01/25/21 | @estamos & @OUStudent |
| 12 | E12.2 E12.4 E12.7 E12.9 E12.11 | 01/17/20 | 01/25/20 | @estamos & @OUStudent |
| 13 | E13.3 E13.5 13.13 | 02/12/21 | 02/12/21 | @OUStudent |
| 15 | E15.1 E15.5 15.9 | 02/12/21 | 02/12/21 | @OUStudent |
| 16 | E16.3 E16.5 E16.10 E16.13 | 01/17/20 | 02/12/21 | @estamos & @OUStudent |
| 17 | E17.3 E17.5 E17.10 E17.11 | 01/17/20 | 02/12/21 | @estamos & @OUStudent |
Title : Neural Network Design
Authors : Martin T. Hagan, Howard B. Demuth, Mark H. Beale
ISBN : 978-0-534-94332-5
Publishing Company, Boston, MA, 1996
| Exercises | Download |
|---|---|
| E2.2 | webpage |
| E2.3 | webpage |
| E3.1 | doc |
| E4.2 E4.3 E4.4 E4.5 E4.6 E4.8 | webpage |
| E4.3 E4.8 | doc |
| E8.5 E9.2 E9.6 | doc |
| E10.4 E10.5 E11.7 E11.11 | doc |
| E12.1 E12.4 E12.5 E12.6 | doc |
| E13.5 E14.2 E14.4 E14.8 | doc |
| E15.6 E15.7 E14.4 E14.8 | doc |
| E16.1 E16.3 E16.5 E16.7 | doc |
This is a set of demonstrations paired with the Neural Network Design & Neural Network Design: Deep Learning books written in Python. You can read more about nndesigndemos at PyPI of project.
Authors : Amir Jafari, Martin Hagan, Pedro Uría
pip install nndesigndemospython3 -m venv env
source env/bin/activate # macOS/Linux
env\Scripts\activate.bat # Windows
pip install nndesigndemos-
Python 3.5+
-
PyQt5 5.14.1
-
NumPy 1.18.1
-
SciPy 1.4.1
-
Matplotlib 3.1.2
from nndesigndemos import nndtoc
nndtoc()
