The "INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS" course provides a comprehensive overview of artificial neural networks (ANNs), a fundamental concept in the field of artificial intelligence. Through ten modules, participants will explore the foundations of ANNs, various neural network architectures, activation functions, and the backpropagation algorithm used for training. The course also covers advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and unsupervised learning. Additionally, participants will learn about transfer learning, pre-trained models, ethical considerations in the use of ANNs, and future trends shaping the development of artificial neural networks.

Who This Course Is For: This course is designed for students, professionals, researchers, and enthusiasts seeking to develop a strong foundational understanding of artificial neural networks. It is suitable for individuals with a background in computer science, mathematics, engineering, or related fields who wish to explore the principles and applications of ANNs. Whether participants are new to the field of artificial intelligence or seeking to deepen their knowledge and skills in neural network modeling and design, this course provides an accessible and comprehensive introduction to the core concepts and techniques of artificial neural networks.