semvec/pypi-packages huggingface dataset:
NeuNetMlP 0.0.1
Multilayered Perceptron Neural Network Built From Numpy & Python
License: MIT
Keywords: Neural Network

First Release: Multilayered Perceptron Network:  
    #2 = InputSize,3=HiddenLayerSize, 1=OutputSize 
    NN = Network(2,3,1) 
    
    #Training Data training_input= (hours sleeping, hours studying), training_output = test score of the student 
    training_input= np.array(([12, 12], [1, 5], [3, 6]), dtype=float) 
    training_output= np.array(([100], [86], [89]), dtype=float) 
    training_input = training_input/np.amax(training_input, axis=0) #maximum of training_input array 
    training_output = training_output/100 # maximum test score is 100 
    
    #Train 
    
    NN.train(training_input,training_output ,10000) 
    
    #Running Data 
    running_data=np.array(([10,10])) 
    
    #Run 
    print("Predicted Output: " + str(NN.run(running_data)))

CHANGELOG
=============================
0.0.1(2/27/2022)
-----------------------------------
-First Release:Multilayered Perceptron Network
topsis-101953010 1.1
A Python package to rank ML models/choices using topsis technique
License: MIT

This package is implementation of topsis technique of multi-criteria decision analysis.
This package will accept three parameters:
1. data.csv //file which contains the models and parameters
2. string of weights separated by commas(,)
3. string of requirements (+/-) separated by commas(,)
// important
install pandas,sys,operator and math libraries before installing this
//
You can install this package using following command
pip install topsis-101953010
NeuNetMLP 0.0.4
Multilayered Perceptron Neural Network Built From Numpy & Python
License: MIT
Keywords: Neural Network

First Release: Multilayered Perceptron Network:  
    #2 = InputSize,3=HiddenLayerSize, 1=OutputSize 
    NN = Network(2,3,1) 
    
    #Training Data training_input= (hours sleeping, hours studying), training_output = test score of the student 
    training_input= np.array(([12, 12], [1, 5], [3, 6]), dtype=float) 
    training_output= np.array(([100], [86], [89]), dtype=float) 
    training_input = training_input/np.amax(training_input, axis=0) #maximum of training_input array 
    training_output = training_output/100 # maximum test score is 100 
    
    #Train 
    
    NN.train(training_input,training_output ,10000) 
    
    #Running Data 
    running_data=np.array(([10,10])) 
    
    #Run 
    print("Predicted Output: " + str(NN.run(running_data)))

CHANGELOG
=============================
0.0.1(2/27/2022)
-----------------------------------
-First Release:Multilayered Perceptron Network