Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

Sign up
Appearance settings

Commit 39d8d29

Browse files
author
Ahmed Gad
committed
Edit version and docs
1 parent 83a5dc8 commit 39d8d29

File tree

3 files changed

+1
-34
lines changed

3 files changed

+1
-34
lines changed

‎README.md

Lines changed: 0 additions & 33 deletions
Original file line numberDiff line numberDiff line change
@@ -253,16 +253,6 @@ Get started with the genetic algorithm by reading the tutorial titled [**Introdu
253253

254254
[![Introduction to Genetic Algorithm](https://user-images.githubusercontent.com/16560492/82078259-26252d00-96e1-11ea-9a02-52a99e1054b9.jpg)](https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad)
255255

256-
## Tutorial: Build Neural Networks in Python
257-
258-
Read about building neural networks in Python through the tutorial titled [**Artificial Neural Network Implementation using NumPy and Classification of the Fruits360 Image Dataset**](https://www.linkedin.com/pulse/artificial-neural-network-implementation-using-numpy-fruits360-gad) available at these links:
259-
260-
* [LinkedIn](https://www.linkedin.com/pulse/artificial-neural-network-implementation-using-numpy-fruits360-gad)
261-
* [Towards Data Science](https://towardsdatascience.com/artificial-neural-network-implementation-using-numpy-and-classification-of-the-fruits360-image-3c56affa4491)
262-
* [KDnuggets](https://www.kdnuggets.com/2019/02/artificial-neural-network-implementation-using-numpy-and-image-classification.html)
263-
264-
[![Building Neural Networks Python](https://user-images.githubusercontent.com/16560492/82078281-30472b80-96e1-11ea-8017-6a1f4383d602.jpg)](https://www.linkedin.com/pulse/artificial-neural-network-implementation-using-numpy-fruits360-gad)
265-
266256
## Tutorial: Optimize Neural Networks with Genetic Algorithm
267257

268258
Read about training neural networks using the genetic algorithm through the tutorial titled [**Artificial Neural Networks Optimization using Genetic Algorithm with Python**](https://www.linkedin.com/pulse/artificial-neural-networks-optimization-using-genetic-ahmed-gad) available at these links:
@@ -273,29 +263,6 @@ Read about training neural networks using the genetic algorithm through the tuto
273263

274264
[![Training Neural Networks using Genetic Algorithm Python](https://user-images.githubusercontent.com/16560492/82078300-376e3980-96e1-11ea-821c-aa6b8ceb44d4.jpg)](https://www.linkedin.com/pulse/artificial-neural-networks-optimization-using-genetic-ahmed-gad)
275265

276-
## Tutorial: Building CNN in Python
277-
278-
To start with coding the genetic algorithm, you can check the tutorial titled [**Building Convolutional Neural Network using NumPy from Scratch**](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad) available at these links:
279-
280-
- [LinkedIn](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad)
281-
- [Towards Data Science](https://towardsdatascience.com/building-convolutional-neural-network-using-numpy-from-scratch-b30aac50e50a)
282-
- [KDnuggets](https://www.kdnuggets.com/2018/04/building-convolutional-neural-network-numpy-scratch.html)
283-
- [Chinese Translation](http://m.aliyun.com/yunqi/articles/585741)
284-
285-
[This tutorial](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad)) is prepared based on a previous version of the project but it still a good resource to start with coding CNNs.
286-
287-
[![Building CNN in Python](https://user-images.githubusercontent.com/16560492/82431022-6c3a1200-9a8e-11ea-8f1b-b055196d76e3.png)](https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad)
288-
289-
## Tutorial: Derivation of CNN from FCNN
290-
291-
Get started with the genetic algorithm by reading the tutorial titled [**Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step**](https://www.linkedin.com/pulse/derivation-convolutional-neural-network-from-fully-connected-gad) which is available at these links:
292-
293-
* [LinkedIn](https://www.linkedin.com/pulse/derivation-convolutional-neural-network-from-fully-connected-gad)
294-
* [Towards Data Science](https://towardsdatascience.com/derivation-of-convolutional-neural-network-from-fully-connected-network-step-by-step-b42ebafa5275)
295-
* [KDnuggets](https://www.kdnuggets.com/2018/04/derivation-convolutional-neural-network-fully-connected-step-by-step.html)
296-
297-
[![Derivation of CNN from FCNN](https://user-images.githubusercontent.com/16560492/82431369-db176b00-9a8e-11ea-99bd-e845192873fc.png)](https://www.linkedin.com/pulse/derivation-convolutional-neural-network-from-fully-connected-gad)
298-
299266
## Book: Practical Computer Vision Applications Using Deep Learning with CNNs
300267

301268
You can also check my book cited as [**Ahmed Fawzy Gad 'Practical Computer Vision Applications Using Deep Learning with CNNs'. Dec. 2018, Apress, 978-1-4842-4167-7**](https://www.amazon.com/Practical-Computer-Vision-Applications-Learning/dp/1484241665) which discusses neural networks, convolutional neural networks, deep learning, genetic algorithm, and more.

‎pygad/visualize/.DS_Store

6 KB
Binary file not shown.

‎pyproject.toml

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -9,7 +9,7 @@ build-backend = "setuptools.build_meta"
99

1010
[project]
1111
name = "pygad"
12-
version = "3.4.0"
12+
version = "3.5.0"
1313
description = "PyGAD: A Python Library for Building the Genetic Algorithm and Training Machine Learning Algoithms (Keras & PyTorch)."
1414
readme = {file = "README.md", content-type = "text/markdown"}
1515
requires-python = ">=3"

0 commit comments

Comments
(0)

AltStyle によって変換されたページ (->オリジナル) /