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Implementation of Neural Network Training using Forward and Backward Propagation in Python
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 106 |
| Year of Publication: 2026 |
| Authors: Ahmad Farhan AlShammari |
10.5120/ijca3528d2883b77
|
Ahmad Farhan AlShammari . Implementation of Neural Network Training using Forward and Backward Propagation in Python. International Journal of Computer Applications. 187, 106 ( May 2026), 51-59. DOI=10.5120/ijca3528d2883b77
Abstract
The goal of this research is to implement neural network training using forward and backward propagation in Python. Neural network is used to process the input data and provide accurate predictions. The training of neural network is performed in two stages: forward and backward propagation. During the training process, the cost function is computed and the weights and biases are updated to reach the optimal solution. The basic steps of neural network training using forward and backward propagation are explained: defining neural network (input, target output, and weights and biases), performing forward propagation, computing cost function, performing backward propagation, updating weights and biases, printing predicted output, and plotting charts. The developed program was tested on an experimental data. The program has successfully performed the basic steps of neural network training using forward and backward propagation and provided the required results.
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- Python: http://www.python.org
- Numpy: http://www.numpy.org
- Pandas: http://pandas.pydata.org
- Matplotlib: http://www. matplotlib.org
- Seaborn: http://seaborn.pydata.org
- SciPy: http://scipy.org
- NLTK: http://www.nltk.org
- SK Learn: http://scikit-learn.org
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