IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 10 NO. 5 2024
DOI: 10.56201/ijcsmt.v10.no5.2024.pg36.46

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Learning in Artificial Neural Networks

Grace Tam-Nurseman and Philip Achimugu

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Abstract

A lot of Materials out there either in softcopy online or hardcopy over the shelve has proven difficult to understand due to high technicality in the style of writing. The are so much of mathematical formulas and terms that are difficult for upcoming scholars in this field of artificial intelligence to grasp. This has discouraged young scholar. The need to simplify what artificial intelligence really is, is necessary to encourage more people to see its beauty and benefit. Developing countries need more of this knowledge in order to invest and develop artificial intelligence related projects to encourage fast growing rate. This article is written in the simplest of terms for people to appreciate artificial intelligence. It is free of mathematical formulas which is one of the discouraging factors in the study of artificial intelligence. It is aimed at providing the basics building foundation for scholars intending to embark on artificial intelligence projects.

keywords:

Artificial Intelligence, Neurons

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References:

Prakhar Swarup, Artificial Intelligence, International Journal of Computing and Corporate
Research, 2, no. 4 (July 2012).
Roy, Rupali. AI, ML, and DL: How not to get them mixed! Towards Data Science. April 29,
https://towardsdatascience.com/understanding-the-difference-between-ai-ml-and-
dl-cceb63252a6c

Siganos, Christos Stergiou and Dimitrios. NEURAL NETWORKS.
http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html, 2022 2011: 1-13.

George B. Johnson, and Peter H. Raven. Biology. Austin, Texas: Holt Rinehart and Winston.,
Frothingham, Scott. Excitatory Neurotransmitters. December 12, 2018.
https://www.healthline.com/health/excitatory-neurotransmitters (accessed August 06,
2022).

Roos, Matthew. Deep Learning Neurons versus Biological Neurons. March 14, 2019.
https://towardsdatascience.com/deep-learning-versus-biological-neurons-floating-point-
numbers-spikes-and-neurotransmitters (accessed August 7, 2022).

Davalo, Eric and Naim Patrick. Neural Networks. London: Palgrave, 1991.

Gupta, Dishashree. Fundamentals of Deep Learning – Activation Functions and When to Use
Them? Octobeer 23, 2017. https://www.analyticsvidhya.com (accessed September 30,
2019).

Draelos, Rachel Lea Ballantyne. The History of Convolutional Neural Networks. Toward
DataScience. April 13, 2019. https://towardsdatascience.com/a-short-history-of-
convolutional-neural-networks-7032e241c483

Chouinard, Jean-Christophe. What is Supervised Learning. Supervised Learning in Machine
Learning. May 5, 2022. https://www.jcchouinard.com/supervised-learning/ (accessed
October 3, 2022).

altexsoft. What is unsupervised learning? Unsupervised Learning: Algorithms and Examples.
April 14, 2021. https://www.altexsoft.com/blog/unsupervised-machine-learning/
(accessed May 12, 2022).

B?a?ej Osi?ski, and Konrad Budek. What is reinforcement learning? What is reinforcement
learning? The complete guide. July 2018, 5. https://deepsense.ai/what-is-reinforcement-
learning-the-complete-guide/ (accessed July 3, 2022).

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