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.

References

• Sammut, C., & Webb, G. I. (2011). "Encyclopedia of Machine Learning". Springer.
• Jung, A. (2022). "Machine Learning: The Basics". Springer.
• Kubat, M. (2021). "An Introduction to Machine Learning". Springer.
• Li, H. (2023). "Machine Learning Methods". Springer.
• Zollanvari, A. (2023). " Machine Learning with Python". Springer.
• Chopra, D., & Khurana, R. (2023). "Introduction to Machine Learning with Python". Bentham Science Publishers.
• Müller, A. C., & Guido, S. (2016). "Introduction to Machine Learning with Python: A Guide for Data Scientists". O'Reilly Media.
• Raschka, S. (2015). "Python Machine Learning". Packt Publishing.
• Forsyth, D. (2019). "Applied Machine Learning". Springer.
• Sarkar, D., Bali, R., & Sharma, T. (2018). "Practical Machine Learning with Python". Apress.
• Igual, L., & Segui, S. (2017). "Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications". Springer.
• VanderPlas, J. (2017). "Python Data Science Handbook: Essential Tools for Working with Data". O'Reilly Media.
• Muddana, A., & Vinayakam, S. (2024). "Python for Data Science". Springer.
• Unpingco, J. (2021). "Python Programming for Data Analysis". Springer.
• Zelle, J. (2017). "Python Programming: An Introduction to Computer Science". Franklin, Beedle & Associates.
• Chun, W. (2001). "Core Python Programming". Prentice Hall Professional.
• Padmanabhan, T. (2016). "Programming with Python". Springer.
• Beazley, D., & Jones, B. K. (2013). "Python Cookbook: Recipes for Mastering Python 3". O'Reilly Media.
• Gurney, K. (1997). "An Introduction to Neural Networks". UCL Press.
• Krose, B., & Smagt, P. (1996). "An Introduction to Neural Networks". University of Amsterdam.
• Haykin, S. (2009). "Neural Networks and Learning Machines". Pearson.
• Silva, I., Spatti, D., Flauzino, R., Liboni, L., & Alves, S. (2017). "Artificial Neural Networks: A Practical Course". Springer.
• Kinsley, H., & Kukieła, D. (2020). "Neural Networks from Scratch in Python".
• Rajput, V. (2023). "Ultimate Neural Network Programming with Python". Orange Education.
• De Marchi, L., & Mitchell, L. (2019). "Hands-On Neural Networks". Packt Publishing.
• Aggarwal, C. (2018). "Neural Networks and Deep Learning: A Textbook". Springer.
• Nielsen, M. (2015). "Neural Networks and Deep Learning". Determination Press.
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). "Deep Learning". MIT Press.
• Prince, S. (2023). "Understanding Deep Learning". MIT Press.
• Menshawy, A. (2018). "Deep Learning by Example". Packt Publishing.
• Zhang, A., Lipton, Z., Li, M., & Smola, A. (2023). "Dive into Deep Learning". Cambridge University Press.
• Chollet, F. (2018). "Deep Learning with Python". Manning Publications.
• McCulloch W., & Pitts W. (1943). "A Logical Calculus of the Ideas Immanent in Nervous Activity". The Bulletin of Mathematical Biophysics. 5 (4): 115–133.
• Rosenblatt F (1958). "The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain". Psychological Review. 65 (6): 386–408.
• Widrow, B., & Hoff, M. (1960). "Adaptive Switching Circuits". Stanford University Labs.
• Rumelhart D., Hinton G., & Williams R. (1986). "Learning Representations by Back-Propagating Errors". Nature. 323 (6088): 533–536.
• 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