semvec/pypi-packages huggingface dataset:
Artificial-Neural-Network-somkietacode 1.0.1
Artificial Neural Network, is a deep learning API written in Python.
Requires Python: >=3.7

# Artificial Neural Network
![Artificial Neural Network logo](https://raw.githubusercontent.com/somkietacode/Artificial_Neural_Network/main/image/gitban.png)

This repository hosts the development of the Artificial Neural Network library.

## About Artificial Neural Network

Artificial Neural Network, is a deep learning API written in Python.
It was developed with a focus on enabling fast experimentation.
*Being able to go from idea to result as fast as possible is key to doing good research.*

Artificial Neural Network is:

-   **Simple** 
-   **Flexible** 
-   **Powerful** 

## First contact with Artificial Neural Network

The core data structures of Artificial Neural Network are __consign__ and __result__.
It implement four model in two layer neural network for helping you fast build __predictor__.

Here is an `exemple` :

```python
from Artificial_Neural_Network import artificialneuralnetwork_classifier
import pandas as pd
import numpy as np

# Reading and cleaning dataset form a CSV file

df = pd.read_csv('admission_data.csv')
df = df.apply(pd.to_numeric, errors='coerce')
df = df.dropna()

# Select X dataset (consign) and convert them in numpy matrix 
x = np.matrix(df[["GRE Score","TOEFL Score","University Rating","SOP","LOR ","CGPA"]].to_numpy() )

# Select Y dataset (response) and convert them in numpy matrix 
y = np.matrix(df[["Research"]].to_numpy())

# Train the model
ANN = artificialneuralnetwork_classifier(x,y)

```

Let make prediction

```python

X = np.matrix([[318,110,3,4,3,8.8] ])
print(Ann.predict(X))

```

It is a binairy classifier. Mean that your response should be 0 or 1. And your dataset response may also be binary.

### Admission data used for this exemple :

GRE  Score	 | TOEFL Score	| University  Rating	| SOP | 	LOR |	CGPA	 | Research
-----------:|---------------:|---------------------:|----:|-------:|--------:|----------
337	| 118| 	4	| 4.5	| 4.5	| 9.65	| 1
324	| 107	| 4	| 4	| 4.5	| 8.87	| 1
316	| 104 | 3	| 3	| 3.5 | 	8	| 1
322	| 110	| 3	| 3.5	| 2.5	| 8.67	| 1
314	| 103	| 2	| 2	| 3	| 8.21	| 0
330	| 115	| 5	| 4.5	| 3	| 9.34	| 1
321	| 109	| 3	| 3	| 4	| 8.2	| 1
308	| 101	| 2	| 3	| 4	| 7.9	| 0
302	| 102	| 1	| 2	| 1.5	| 8	| 0
323	| 108	| 3	| 3.5	| 3	| 8.6	| 0


---
## Support

You can ask questions and join the development discussion:

- [Facebook page](https://www.facebook.com/globalanalysistech) .

---
processamento-imagens-pythonico 0.0.1
Image processing package using skimage
Requires Python: >=3.8

# processamento_imagens_pythonico

Description. 
The package processamento_imagens_pythonico is used to:
	Processamento
		- Histogram matching
		- Structural similarity
		- Resize image
	Utils:
		- Read image
		- Save image
		- Plot image
		- Plot result
		- Plot histogram
## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install processamento_imagens_pythonico

```bash
pip install processamento_imagens_pythonico
```
## Author
Guilherme

## License
[MIT](https://choosealicense.com/licenses/mit/)