Foundations of Artificial Neural Networks and Training Principles

Understanding artificial neural networks is essential for anyone entering the field of artificial intelligence. This written course provides a structured introduction to the core mathematical principles, architectural models, and training procedures that allow neural networks to learn from data. You will begin with essential definitions and foundational concepts, building a strong theoretical baseline before exploring optimization strategies and network training routines. Through structured readings and practical code snippets, you will develop a clear understanding of how deep learning systems operate under the hood. What you'll learn: - Understand foundational terminology, activation functions, and basic network architectures - Master the mechanics of forward propagation, loss functions, and backpropagation - Apply optimization algorithms including gradient descent and adaptive learning rates - Configure regularization techniques such as dropout and weight decay to prevent overfitting - Analyze common training issues like vanishing gradients and learn methods to mitigate them - Explore modern foundational concepts including attention mechanisms and transformer basics The course begins with introductory theory and gradually advances to key optimization strategies, offering a thorough conceptual framework. It is tailored for beginners, developers, and data enthusiasts with no prior deep learning experience. Read through the lessons to establish a solid foundation in neural network theory.