Deep Learning Foundations: Training Neural Networks and Optimization

Have you ever wondered what actually happens under the hood when a neural network learns? Understanding the mechanics of training, optimization, and activation functions is the key to building models that perform reliably in the real world. This text-based course guides you through the fundamental principles of training neural networks, transitioning you from a basic understanding of neural connections to a confident grasp of backpropagation, modern optimization algorithms, and regularization techniques used in the industry today. You will learn to understand the core terminology of neural networks, including weights, biases, and loss functions; analyze how activation functions like ReLU, Sigmoid, and GeLU shape network behavior; explore foundational optimization techniques, from Gradient Descent to modern algorithms like Adam and AdamW; apply regularization strategies, including dropout and weight decay, to prevent model overfitting; and practice diagnosing training issues such as vanishing or exploding gradients through written conceptual exercises. The course begins with essential definitions and the mathematical intuition behind backpropagation, then progresses to exploring advanced optimizers and modern training strategies, reinforcing your knowledge with written self-assessment exercises. This course is designed for beginners in machine learning and data science who want to build a solid theoretical and practical foundation in neural network training, with no advanced mathematical background required. Start reading today to unlock the core mechanics of deep learning and elevate your machine learning skills.